Center for Teaching

Metacognition.

Thinking about One’s Thinking |   Putting Metacognition into Practice

Thinking about One’s Thinking

learning about problem solving refers to learning different strategies

Initially studied for its development in young children (Baker & Brown, 1984; Flavell, 1985), researchers soon began to look at how experts display metacognitive thinking and how, then, these thought processes can be taught to novices to improve their learning (Hatano & Inagaki, 1986).  In How People Learn , the National Academy of Sciences’ synthesis of decades of research on the science of learning, one of the three key findings of this work is the effectiveness of a “‘metacognitive’ approach to instruction” (Bransford, Brown, & Cocking, 2000, p. 18).

Metacognitive practices increase students’ abilities to transfer or adapt their learning to new contexts and tasks (Bransford, Brown, & Cocking, p. 12; Palincsar & Brown, 1984; Scardamalia et al., 1984; Schoenfeld, 1983, 1985, 1991).  They do this by gaining a level of awareness above the subject matter : they also think about the tasks and contexts of different learning situations and themselves as learners in these different contexts.  When Pintrich (2002) asserts that “Students who know about the different kinds of strategies for learning, thinking, and problem solving will be more likely to use them” (p. 222), notice the students must “know about” these strategies, not just practice them.  As Zohar and David (2009) explain, there must be a “ conscious meta-strategic level of H[igher] O[rder] T[hinking]” (p. 179).

Metacognitive practices help students become aware of their strengths and weaknesses as learners, writers, readers, test-takers, group members, etc.  A key element is recognizing the limit of one’s knowledge or ability and then figuring out how to expand that knowledge or extend the ability. Those who know their strengths and weaknesses in these areas will be more likely to “actively monitor their learning strategies and resources and assess their readiness for particular tasks and performances” (Bransford, Brown, & Cocking, p. 67).

The absence of metacognition connects to the research by Dunning, Johnson, Ehrlinger, and Kruger on “Why People Fail to Recognize Their Own Incompetence” (2003).  They found that “people tend to be blissfully unaware of their incompetence,” lacking “insight about deficiencies in their intellectual and social skills.”  They identified this pattern across domains—from test-taking, writing grammatically, thinking logically, to recognizing humor, to hunters’ knowledge about firearms and medical lab technicians’ knowledge of medical terminology and problem-solving skills (p. 83-84).  In short, “if people lack the skills to produce correct answers, they are also cursed with an inability to know when their answers, or anyone else’s, are right or wrong” (p. 85).  This research suggests that increased metacognitive abilities—to learn specific (and correct) skills, how to recognize them, and how to practice them—is needed in many contexts.

Putting Metacognition into Practice

In “ Promoting Student Metacognition ,” Tanner (2012) offers a handful of specific activities for biology classes, but they can be adapted to any discipline. She first describes four assignments for explicit instruction (p. 116):

  • Preassessments—Encouraging Students to Examine Their Current Thinking: “What do I already know about this topic that could guide my learning?”

learning about problem solving refers to learning different strategies

  • Retrospective Postassessments—Pushing Students to Recognize Conceptual Change: “Before this course, I thought evolution was… Now I think that evolution is ….” or “How is my thinking changing (or not changing) over time?”
  • Reflective Journals—Providing a Forum in Which Students Monitor Their Own Thinking: “What about my exam preparation worked well that I should remember to do next time? What did not work so well that I should not do next time or that I should change?”

Next are recommendations for developing a “classroom culture grounded in metacognition” (p. 116-118):

  • Giving Students License to Identify Confusions within the Classroom Culture:  ask students what they find confusing, acknowledge the difficulties
  • Integrating Reflection into Credited Course Work: integrate short reflection (oral or written) that ask students what they found challenging or what questions arose during an assignment/exam/project
  • Metacognitive Modeling by the Instructor for Students: model the thinking processes involved in your field and sought in your course by being explicit about “how you start, how you decide what to do first and then next, how you check your work, how you know when you are done” (p. 118)

To facilitate these activities, she also offers three useful tables:

  • Questions for students to ask themselves as they plan, monitor, and evaluate their thinking within four learning contexts—in class, assignments, quizzes/exams, and the course as a whole (p. 115)
  • Prompts for integrating metacognition into discussions of pairs during clicker activities, assignments, and quiz or exam preparation (p. 117)
  • Questions to help faculty metacognitively assess their own teaching (p. 119)

Weimer’s “ Deep Learning vs. Surface Learning: Getting Students to Understand the Difference ” (2012) offers additional recommendations for developing students’ metacognitive awareness and improvement of their study skills:

“[I]t is terribly important that in explicit and concerted ways we make students aware of themselves as learners. We must regularly ask, not only ‘What are you learning?’ but ‘How are you learning?’ We must confront them with the effectiveness (more often ineffectiveness) of their approaches. We must offer alternatives and then challenge students to test the efficacy of those approaches. ” (emphasis added)

She points to a tool developed by Stanger-Hall (2012, p. 297) for her students to identify their study strategies, which she divided into “ cognitively passive ” (“I previewed the reading before class,” “I came to class,” “I read the assigned text,” “I highlighted the text,” et al) and “ cognitively active study behaviors ” (“I asked myself: ‘How does it work?’ and ‘Why does it work this way?’” “I wrote my own study questions,” “I fit all the facts into a bigger picture,” “I closed my notes and tested how much I remembered,” et al) .  The specific focus of Stanger-Hall’s study is tangential to this discussion, 1 but imagine giving students lists like hers adapted to your course and then, after a major assignment, having students discuss which ones worked and which types of behaviors led to higher grades. Even further, follow Lovett’s advice (2013) by assigning “exam wrappers,” which include students reflecting on their previous exam-preparation strategies, assessing those strategies and then looking ahead to the next exam, and writing an action plan for a revised approach to studying. A common assignment in English composition courses is the self-assessment essay in which students apply course criteria to articulate their strengths and weaknesses within single papers or over the course of the semester. These activities can be adapted to assignments other than exams or essays, such as projects, speeches, discussions, and the like.

As these examples illustrate, for students to become more metacognitive, they must be taught the concept and its language explicitly (Pintrich, 2002; Tanner, 2012), though not in a content-delivery model (simply a reading or a lecture) and not in one lesson. Instead, the explicit instruction should be “designed according to a knowledge construction approach,” or students need to recognize, assess, and connect new skills to old ones, “and it needs to take place over an extended period of time” (Zohar & David, p. 187).  This kind of explicit instruction will help students expand or replace existing learning strategies with new and more effective ones, give students a way to talk about learning and thinking, compare strategies with their classmates’ and make more informed choices, and render learning “less opaque to students, rather than being something that happens mysteriously or that some students ‘get’ and learn and others struggle and don’t learn” (Pintrich, 2002, p. 223).

learning about problem solving refers to learning different strategies

  • What to Expect (when reading philosophy)
  • The Ultimate Goal (of reading philosophy)
  • Basic Good Reading Behaviors
  • Important Background Information, or discipline- and course-specific reading practices, such as “reading for enlightenment” rather than information, and “problem-based classes” rather than historical or figure-based classes
  • A Three-Part Reading Process (pre-reading, understanding, and evaluating)
  • Flagging, or annotating the reading
  • Linear vs. Dialogical Writing (Philosophical writing is rarely straightforward but instead “a monologue that contains a dialogue” [p. 365].)

What would such a handout look like for your discipline?

Students can even be metacognitively prepared (and then prepare themselves) for the overarching learning experiences expected in specific contexts . Salvatori and Donahue’s The Elements (and Pleasures) of Difficulty (2004) encourages students to embrace difficult texts (and tasks) as part of deep learning, rather than an obstacle.  Their “difficulty paper” assignment helps students reflect on and articulate the nature of the difficulty and work through their responses to it (p. 9).  Similarly, in courses with sensitive subject matter, a different kind of learning occurs, one that involves complex emotional responses.  In “ Learning from Their Own Learning: How Metacognitive and Meta-affective Reflections Enhance Learning in Race-Related Courses ” (Chick, Karis, & Kernahan, 2009), students were informed about the common reactions to learning about racial inequality (Helms, 1995; Adams, Bell, & Griffin, 1997; see student handout, Chick, Karis, & Kernahan, p. 23-24) and then regularly wrote about their cognitive and affective responses to specific racialized situations.  The students with the most developed metacognitive and meta-affective practices at the end of the semester were able to “clear the obstacles and move away from” oversimplified thinking about race and racism ”to places of greater questioning, acknowledging the complexities of identity, and redefining the world in racial terms” (p. 14).

Ultimately, metacognition requires students to “externalize mental events” (Bransford, Brown, & Cocking, p. 67), such as what it means to learn, awareness of one’s strengths and weaknesses with specific skills or in a given learning context, plan what’s required to accomplish a specific learning goal or activity, identifying and correcting errors, and preparing ahead for learning processes.

————————

1 Students who were tested with short answer in addition to multiple-choice questions on their exams reported more cognitively active behaviors than those tested with just multiple-choice questions, and these active behaviors led to improved performance on the final exam.

  • Adams, Maurianne, Bell, Lee Ann, and Griffin, Pat. (1997). Teaching for diversity and social justice: A sourcebook . New York: Routledge.
  • Bransford, John D., Brown Ann L., and Cocking Rodney R. (2000). How people learn: Brain, mind, experience, and school . Washington, D.C.: National Academy Press.
  • Baker, Linda, and Brown, Ann L. (1984). Metacognitive skills and reading.  In Paul David Pearson, Michael L. Kamil, Rebecca Barr, & Peter Mosenthal (Eds.), Handbook of research in reading: Volume III (pp. 353–395).  New York: Longman.
  • Brown, Ann L. (1980). Metacognitive development and reading. In Rand J. Spiro, Bertram C. Bruce, and William F. Brewer, (Eds.), Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence, and education (pp. 453-482). Hillsdale, NJ: Erlbaum.
  • Chick, Nancy, Karis, Terri, and Kernahan, Cyndi. (2009). Learning from their own learning: how metacognitive and meta-affective reflections enhance learning in race-related courses . International Journal for the Scholarship of Teaching and Learning, 3(1). 1-28.
  • Commander, Nannette Evans, and Valeri-Gold, Marie. (2001). The learning portfolio: A valuable tool for increasing metacognitive awareness . The Learning Assistance Review, 6 (2), 5-18.
  • Concepción, David. (2004). Reading philosophy with background knowledge and metacognition . Teaching Philosophy , 27 (4). 351-368.
  • Dunning, David, Johnson, Kerri, Ehrlinger, Joyce, and Kruger, Justin. (2003) Why people fail to recognize their own incompetence . Current Directions in Psychological Science, 12 (3). 83-87.
  • Flavell,  John H. (1985). Cognitive development. Englewood Cliffs, NJ: Prentice Hall.
  • Hatano, Giyoo and Inagaki, Kayoko. (1986). Two courses of expertise. In Harold Stevenson, Azuma, Horishi, and Hakuta, Kinji (Eds.), Child development and education in Japan, New York: W.H. Freeman.
  • Helms, Janet E. (1995). An update of Helms’ white and people of color racial identity models . In J.G. Ponterotto, Joseph G., Casas, Manuel, Suzuki, Lisa A., and Alexander, Charlene M. (Eds.), Handbook of multicultural counseling (pp. 181-198) . Thousand Oaks, CA: Sage.
  • Lovett, Marsha C. (2013). Make exams worth more than the grade. In Matthew Kaplan, Naomi Silver, Danielle LaVague-Manty, and Deborah Meizlish (Eds.), Using reflection and metacognition to improve student learning: Across the disciplines, across the academy . Sterling, VA: Stylus.
  • Palincsar, Annemarie Sullivan, and Brown, Ann L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities . Cognition and Instruction, 1 (2). 117-175.
  • Pintrich, Paul R. (2002). The Role of metacognitive knowledge in learning, teaching, and assessing . Theory into Practice, 41 (4). 219-225.
  • Salvatori, Mariolina Rizzi, and Donahue, Patricia. (2004). The Elements (and pleasures) of difficulty . New York: Pearson-Longman.
  • Scardamalia, Marlene, Bereiter, Carl, and Steinbach, Rosanne. (1984). Teachability of reflective processes in written composition . Cognitive Science , 8, 173-190.
  • Schoenfeld, Alan H. (1991). On mathematics as sense making: An informal attack on the fortunate divorce of formal and informal mathematics. In James F. Voss, David N. Perkins, and Judith W. Segal (Eds.), Informal reasoning and education (pp. 311-344). Hillsdale, NJ: Erlbaum.
  • Stanger-Hall, Kathrin F. (2012). Multiple-choice exams: An obstacle for higher-level thinking in introductory science classes . Cell Biology Education—Life Sciences Education, 11(3), 294-306.
  • Tanner, Kimberly D.  (2012). Promoting student metacognition . CBE—Life Sciences Education, 11, 113-120.
  • Weimer, Maryellen.  (2012, November 19). Deep learning vs. surface learning: Getting students to understand the difference . Retrieved from the Teaching Professor Blog from http://www.facultyfocus.com/articles/teaching-professor-blog/deep-learning-vs-surface-learning-getting-students-to-understand-the-difference/ .
  • Zohar, Anat, and David, Adi Ben. (2009). Paving a clear path in a thick forest: a conceptual analysis of a metacognitive component . Metacognition Learning , 4 , 177-195.

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TEAL Center Fact Sheet No. 4: Metacognitive Processes

Metacognition is one’s ability to use prior knowledge to plan a strategy for approaching a learning task, take necessary steps to problem solve, reflect on and evaluate results, and modify one’s approach as needed. It helps learners choose the right cognitive tool for the task and plays a critical role in successful learning.

What Is Metacognition?

Metacognition refers to awareness of one’s own knowledge—what one does and doesn’t know—and one’s ability to understand, control, and manipulate one’s cognitive processes (Meichenbaum, 1985). It includes knowing when and where to use particular strategies for learning and problem solving as well as how and why to use specific strategies. Metacognition is the ability to use prior knowledge to plan a strategy for approaching a learning task, take necessary steps to problem solve, reflect on and evaluate results, and modify one’s approach as needed. Flavell (1976), who first used the term, offers the following example: I am engaging in Metacognition if I notice that I am having more trouble learning A than B; if it strikes me that I should double check C before accepting it as fact (p. 232).

Cognitive strategies are the basic mental abilities we use to think, study, and learn (e.g., recalling information from memory, analyzing sounds and images, making associations between or comparing/contrasting different pieces of information, and making inferences or interpreting text). They help an individual achieve a particular goal, such as comprehending text or solving a math problem, and they can be individually identified and measured. In contrast, metacognitive strategies are used to ensure that an overarching learning goal is being or has been reached. Examples of metacognitive activities include planning how to approach a learning task, using appropriate skills and strategies to solve a problem, monitoring one’s own comprehension of text, self-assessing and self-correcting in response to the self-assessment, evaluating progress toward the completion of a task, and becoming aware of distracting stimuli.

Elements of Metacognition

Researchers distinguish between metacognitive knowledge and metacognitive regulation (Flavell, 1979, 1987; Schraw & Dennison, 1994). Metacognitive knowledge refers to what individuals know about themselves as cognitive processors, about different approaches that can be used for learning and problem solving, and about the demands of a particular learning task. Metacognitive regulation refers to adjustments individuals make to their processes to help control their learning, such as planning, information management strategies, comprehension monitoring, de-bugging strategies, and evaluation of progress and goals. Flavell (1979) further divides metacognitive knowledge into three categories:

  • Person variables: What one recognizes about his or her strengths and weaknesses in learning and processing information.
  • Task variables: What one knows or can figure out about the nature of a task and the processing demands required to complete the task—for example, knowledge that it will take more time to read, comprehend, and remember a technical article than it will a similar-length passage from a novel.
  • Strategy variables: The strategies a person has “at the ready” to apply in a flexible way to successfully accomplish a task; for example, knowing how to activate prior knowledge before reading a technical article, using a glossary to look up unfamiliar words, or recognizing that sometimes one has to reread a paragraph several times before it makes sense.

Livingston (1997) provides an example of all three variables: “I know that I ( person variable ) have difficulty with word problems ( task variable ), so I will answer the computational problems first and save the word problems for last ( strategy variable ).”

Why Teach Metacognitive Skills?

Research shows that metacognitive skills can be taught to students to improve their learning (Nietfeld & Shraw, 2002; Thiede, Anderson, & Therriault, 2003).

Constructing understanding requires both cognitive and metacognitive elements. Learners “construct knowledge” using cognitive strategies, and they guide, regulate, and evaluate their learning using metacognitive strategies. It is through this “thinking about thinking,” this use of metacognitive strategies, that real learning occurs. As students become more skilled at using metacognitive strategies, they gain confidence and become more independent as learners.

Individuals with well-developed metacognitive skills can think through a problem or approach a learning task, select appropriate strategies, and make decisions about a course of action to resolve the problem or successfully perform the task. They often think about their own thinking processes, taking time to think about and learn from mistakes or inaccuracies (North Central Regional Educational Laboratory, 1995). Some instructional programs encourage students to engage in “metacognitive conversations” with themselves so that they can “talk” with themselves about their learning, the challenges they encounter, and the ways in which they can self-correct and continue learning.

Moreover, individuals who demonstrate a wide variety of metacognitive skills perform better on exams and complete work more efficiently—they use the right tool for the job, and they modify learning strategies as needed, identifying blocks to learning and changing tools or strategies to ensure goal attainment. Because Metacognition plays a critical role in successful learning, it is imperative that instructors help learners develop metacognitively.

What’s the Research?

Metacognitive strategies can be taught (Halpern, 1996), they are associated with successful learning (Borkowski, Carr, & Pressley, 1987). Successful learners have a repertoire of strategies to select from and can transfer them to new settings (Pressley, Borkowski, & Schneider, 1987). Instructors need to set tasks at an appropriate level of difficulty (i.e., challenging enough so that students need to apply metacognitive strategies to monitor success but not so challenging that students become overwhelmed or frustrated), and instructors need to prompt learners to think about what they are doing as they complete these tasks (Biemiller & Meichenbaum, 1992). Instructors should take care not to do the thinking for learners or tell them what to do because this runs the risk of making students experts at seeking help rather than experts at thinking about and directing their own learning. Instead, effective instructors continually prompt learners, asking “What should you do next?”

McKeachie (1988) found that few college instructors explicitly teach strategies for monitoring learning. They assume that students have already learned these strategies in high school. But many have not and are unaware of the metacognitive process and its importance to learning. Rote memorization is the usual—and often the only—learning strategy employed by high school students when they enter college (Nist, 1993). Simpson and Nist (2000), in a review of the literature on strategic learning, emphasize that instructors need to provide explicit instruction on the use of study strategies. The implication for ABE programs is that it is likely that ABE learners need explicit instruction in both cognitive and metacognitive strategies. They need to know that they have choices about the strategies they can employ in different contexts, and they need to monitor their use of and success with these strategies.

Recommended Instructional Strategies

Instructors can encourage ABE learners to become more strategic thinkers by helping them focus on the ways they process information. Self-questioning, reflective journal writing, and discussing their thought processes with other learners are among the ways that teachers can encourage learners to examine and develop their metacognitive processes.

Fogarty (1994) suggests that Metacognition is a process that spans three distinct phases, and that, to be successful thinkers, students must do the following:

  • Develop a plan before approaching a learning task, such as reading for comprehension or solving a math problem.
  • Monitor their understanding; use “fix-up” strategies when meaning breaks down.
  • Evaluate their thinking after completing the task.

Instructors can model the application of questions, and they can prompt learners to ask themselves questions during each phase. They can incorporate into lesson plans opportunities for learners to practice using these questions during learning tasks, as illustratetd in the following examples:

  • During the planning phase, learners can ask, What am I supposed to learn? What prior knowledge will help me with this task? What should I do first? What should I look for in this reading? How much time do I have to complete this? In what direction do I want my thinking to take me?
  • During the monitoring phase, learners can ask, How am I doing? Am I on the right track? How should I proceed? What information is important to remember? Should I move in a different direction? Should I adjust the pace because of the difficulty? What can I do if I do not understand?
  • During the evaluation phase, learners can ask, H ow well did I do? What did I learn? Did I get the results I expected? What could I have done differently? Can I apply this way of thinking to other problems or situations? Is there anything I don’t understand—any gaps in my knowledge? Do I need to go back through the task to fill in any gaps in understanding? How might I apply this line of thinking to other problems?

Rather than viewing reading, writing, science, social studies, and math only as subjects or content to be taught, instructors can see them as opportunities for learners to reflect on their learning processes. Examples follow for each content area:

  • Reading: Teach learners how to ask questions during reading and model “think-alouds.” Ask learners questions during read-alouds and teach them to monitor their reading by constantly asking themselves if they understand what the text is about. Teach them to take notes or highlight important details, asking themselves, “Why is this a key phrase to highlight?” and “Why am I not highlighting this?”
  • Writing: Model prewriting strategies for organizing thoughts, such as brainstorming ideas using a word web, or using a graphic organizer to put ideas into paragraphs, with the main idea at the top and the supporting details below it.
  • Social Studies and Science: Teach learners the importance of using organizers such as KWL charts, Venn diagrams, concept maps , and anticipation/reaction charts to sort information and help them learn and understand content. Learners can use organizers prior to a task to focus their attention on what they already know and identify what they want to learn. They can use a Venn diagram to identify similarities and differences between two related concepts.
  • Math: Teach learners to use mnemonics to recall steps in a process, such as the order of mathematical operations. Model your thought processes in solving problems—for example, “This is a lot of information; where should I start? Now that I know____, is there something else I know?”

The goal of teaching metacognitive strategies is to help learners become comfortable with these strategies so that they employ them automatically to learning tasks, focusing their attention, deriving meaning, and making adjustments if something goes wrong. They do not think about these skills while performing them but, if asked what they are doing, they can usually accurately describe their metacognitive processes.

Biemiller, A., & Meichenbaum, D. (1992). The nature and nurture of the self-directed learner. Educational Leadership, 50, 75–80.

Borkowski, J., Carr, M., & Pressely, M. (1987). “Spontaneous” strategy use: Perspectives from metacognitive theory. Intelligence, 11, 61–75.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906–911.

Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231–236). Hillsdale, NJ: Lawrence Erlbaum Associates.

Flavell, J. H. (1987). Speculations about the nature and development of metacognition. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 21–29). Hillside, NJ: Lawrence Erlbaum Associates.

Fogarty, R. (1994). How to teach for metacognition. Palatine, IL: IRI/Skylight Publishing.

Halpern, D. F. (1996). Thought and knowledge: An introduction to critical thinking. Mahwah, NJ: Lawrence Erlbaum Associates.

Livingston, J. A. (1997). Metacognition: An overview. Retrieved December 27, 2011 from http://gse.buffalo.edu/fas/shuell/CEP564/Metacog.htm

McKeachie, W. J. (1988). The need for study strategy training. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction, and evaluation (pp. 3–9). New York: Academic Press.

Meichenbaum, D. (1985). Teaching thinking: A cognitive-behavioral perspective. In S. F., Chipman, J. W. Segal, & R. Glaser (Eds.), Thinking and learning skills, Vol. 2: Research and open questions. Hillsdale, NJ: Lawrence Erlbaum Associates.

North Central Regional Educational Laboratory. (1995). Strategic teaching and reading project guidebook. Retrieved December 27, 2011

Nietfeld, J. L., & Shraw, G. (2002). The effect of knowledge and strategy explanation on monitoring accuracy. Journal of Educational Research, 95, 131–142.

Nist, S. (1993). What the literature says about academic literacy. Georgia Journal of Reading, Fall-Winter, 11–18.

Pressley, M., Borkowski, J. G., & Schneider, W. (1987). Cognitive strategies: Good strategy users coordinate metacognition and knowledge. In R. Vasta, & G. Whitehurst (Eds.), Annals of child development, 4, 80–129. Greenwich, CT: JAI Press.

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460–475.

Simpson, M. L., & Nist, S. L. (2000). An update on strategic learning: It’s more than textbook reading strategies. Journal of Adolescent and Adult Literacy, 43 (6) 528–541.

Thiede, K. W., Anderson, M. C., & Therriault, D. (2003). Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95, 66–73.

Authors: TEAL Center staff

Reviewed by: David Scanlon, Boston College

About the TEAL Center: The Teaching Excellence in Adult Literacy (TEAL) Center is a project of the U.S. Department of Education, Office of Career, Technical, and Adult Education (OCTAE), designed to improve the quality of teaching in adult education in the content areas.

7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

  • Describe problem solving strategies
  • Define algorithm and heuristic
  • Explain some common roadblocks to effective problem solving

   People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

   When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

   Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

  • When one is faced with too much information
  • When the time to make a decision is limited
  • When the decision to be made is unimportant
  • When there is access to very little information to use in making the decision
  • When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

  • Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
  • Analogy – is using a solution that solves a similar problem.
  • Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
  • Divide and conquer – breaking down large complex problems into smaller more manageable problems.
  • Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
  • Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
  • Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
  • Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
  • Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
  • Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
  • Reduction – adapting the problem to be as similar problems where a solution exists.
  • Research – using existing knowledge or solutions to similar problems to solve the problem.
  • Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

  • 1. Only one disk can be moved at a time.
  • 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
  • 3. No disc may be placed on top of a smaller disk.

learning about problem solving refers to learning different strategies

  Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

learning about problem solving refers to learning different strategies

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage  (1990) suggesting that while collecting data for what would later be his book  The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as  insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground.  Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

   Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

   Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

   Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

A puzzle involving a scale is shown. At the top of the figure it reads: “Sam Loyds Puzzling Scales.” The first row of the puzzle shows a balanced scale with 3 blocks and a top on the left and 12 marbles on the right. Below this row it reads: “Since the scales now balance.” The next row of the puzzle shows a balanced scale with just the top on the left, and 1 block and 8 marbles on the right. Below this row it reads: “And balance when arranged this way.” The third row shows an unbalanced scale with the top on the left side, which is much lower than the right side. The right side is empty. Below this row it reads: “Then how many marbles will it require to balance with that top?”

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

   Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

   Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

The first puzzle is a Sudoku grid of 16 squares (4 rows of 4 squares) is shown. Half of the numbers were supplied to start the puzzle and are colored blue, and half have been filled in as the puzzle’s solution and are colored red. The numbers in each row of the grid, left to right, are as follows. Row 1: blue 3, red 1, red 4, blue 2. Row 2: red 2, blue 4, blue 1, red 3. Row 3: red 1, blue 3, blue 2, red 4. Row 4: blue 4, red 2, red 3, blue 1.The second puzzle consists of 9 dots arranged in 3 rows of 3 inside of a square. The solution, four straight lines made without lifting the pencil, is shown in a red line with arrows indicating the direction of movement. In order to solve the puzzle, the lines must extend beyond the borders of the box. The four connecting lines are drawn as follows. Line 1 begins at the top left dot, proceeds through the middle and right dots of the top row, and extends to the right beyond the border of the square. Line 2 extends from the end of line 1, through the right dot of the horizontally centered row, through the middle dot of the bottom row, and beyond the square’s border ending in the space beneath the left dot of the bottom row. Line 3 extends from the end of line 2 upwards through the left dots of the bottom, middle, and top rows. Line 4 extends from the end of line 3 through the middle dot in the middle row and ends at the right dot of the bottom row.

   Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a  ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm:  problem-solving strategy characterized by a specific set of instructions

anchoring bias:  faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic:  faulty heuristic in which you make a decision based on information readily available to you

confirmation bias:  faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness:  inability to see an object as useful for any other use other than the one for which it was intended

heuristic:  mental shortcut that saves time when solving a problem

hindsight bias:  belief that the event just experienced was predictable, even though it really wasn’t

mental set:  continually using an old solution to a problem without results

problem-solving strategy:  method for solving problems

representative bias:  faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error:  problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards:  heuristic in which you begin to solve a problem by focusing on the end result

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Problem-Solving Strategies and Obstacles

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

learning about problem solving refers to learning different strategies

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

learning about problem solving refers to learning different strategies

JGI / Jamie Grill / Getty Images

  • Application
  • Improvement

From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

  • Discovery of the problem
  • Deciding to tackle the issue
  • Seeking to understand the problem more fully
  • Researching available options or solutions
  • Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

  • Perceptually recognizing the problem
  • Representing the problem in memory
  • Considering relevant information that applies to the problem
  • Identifying different aspects of the problem
  • Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

  • Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
  • Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
  • Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
  • Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

  • Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
  • Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
  • Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
  • Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

  • Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
  • Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
  • Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
  • Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
  • Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
  • Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

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The Oxford Handbook of Cognitive Psychology

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The Oxford Handbook of Cognitive Psychology

48 Problem Solving

Department of Psychological and Brain Sciences, University of California, Santa Barbara

  • Published: 03 June 2013
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Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.

The study of problem solving begins with defining problem solving, problem, and problem types. This introduction to problem solving is rounded out with an examination of cognitive processes in problem solving, the role of knowledge in problem solving, and historical approaches to the study of problem solving.

Definition of Problem Solving

Problem solving refers to cognitive processing directed at achieving a goal for which the problem solver does not initially know a solution method. This definition consists of four major elements (Mayer, 1992 ; Mayer & Wittrock, 2006 ):

Cognitive —Problem solving occurs within the problem solver’s cognitive system and can only be inferred indirectly from the problem solver’s behavior (including biological changes, introspections, and actions during problem solving). Process —Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of a new mental representation. Directed —Problem solving is aimed at achieving a goal. Personal —Problem solving depends on the existing knowledge of the problem solver so that what is a problem for one problem solver may not be a problem for someone who already knows a solution method.

The definition is broad enough to include a wide array of cognitive activities such as deciding which apartment to rent, figuring out how to use a cell phone interface, playing a game of chess, making a medical diagnosis, finding the answer to an arithmetic word problem, or writing a chapter for a handbook. Problem solving is pervasive in human life and is crucial for human survival. Although this chapter focuses on problem solving in humans, problem solving also occurs in nonhuman animals and in intelligent machines.

How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving). Thus, problem solving is a type of thinking (i.e., directed thinking).

Reasoning refers to problem solving within specific classes of problems, such as deductive reasoning or inductive reasoning. In deductive reasoning, the reasoner is given premises and must derive a conclusion by applying the rules of logic. For example, given that “A is greater than B” and “B is greater than C,” a reasoner can conclude that “A is greater than C.” In inductive reasoning, the reasoner is given (or has experienced) a collection of examples or instances and must infer a rule. For example, given that X, C, and V are in the “yes” group and x, c, and v are in the “no” group, the reasoning may conclude that B is in “yes” group because it is in uppercase format. Thus, reasoning is a type of problem solving.

Definition of Problem

A problem occurs when someone has a goal but does not know to achieve it. This definition is consistent with how the Gestalt psychologist Karl Duncker ( 1945 , p. 1) defined a problem in his classic monograph, On Problem Solving : “A problem arises when a living creature has a goal but does not know how this goal is to be reached.” However, today researchers recognize that the definition should be extended to include problem solving by intelligent machines. This definition can be clarified using an information processing approach by noting that a problem occurs when a situation is in the given state, the problem solver wants the situation to be in the goal state, and there is no obvious way to move from the given state to the goal state (Newell & Simon, 1972 ). Accordingly, the three main elements in describing a problem are the given state (i.e., the current state of the situation), the goal state (i.e., the desired state of the situation), and the set of allowable operators (i.e., the actions the problem solver is allowed to take). The definition of “problem” is broad enough to include the situation confronting a physician who wishes to make a diagnosis on the basis of preliminary tests and a patient examination, as well as a beginning physics student trying to solve a complex physics problem.

Types of Problems

It is customary in the problem-solving literature to make a distinction between routine and nonroutine problems. Routine problems are problems that are so familiar to the problem solver that the problem solver knows a solution method. For example, for most adults, “What is 365 divided by 12?” is a routine problem because they already know the procedure for long division. Nonroutine problems are so unfamiliar to the problem solver that the problem solver does not know a solution method. For example, figuring out the best way to set up a funding campaign for a nonprofit charity is a nonroutine problem for most volunteers. Technically, routine problems do not meet the definition of problem because the problem solver has a goal but knows how to achieve it. Much research on problem solving has focused on routine problems, although most interesting problems in life are nonroutine.

Another customary distinction is between well-defined and ill-defined problems. Well-defined problems have a clearly specified given state, goal state, and legal operators. Examples include arithmetic computation problems or games such as checkers or tic-tac-toe. Ill-defined problems have a poorly specified given state, goal state, or legal operators, or a combination of poorly defined features. Examples include solving the problem of global warming or finding a life partner. Although, ill-defined problems are more challenging, much research in problem solving has focused on well-defined problems.

Cognitive Processes in Problem Solving

The process of problem solving can be broken down into two main phases: problem representation , in which the problem solver builds a mental representation of the problem situation, and problem solution , in which the problem solver works to produce a solution. The major subprocess in problem representation is representing , which involves building a situation model —that is, a mental representation of the situation described in the problem. The major subprocesses in problem solution are planning , which involves devising a plan for how to solve the problem; executing , which involves carrying out the plan; and monitoring , which involves evaluating and adjusting one’s problem solving.

For example, given an arithmetic word problem such as “Alice has three marbles. Sarah has two more marbles than Alice. How many marbles does Sarah have?” the process of representing involves building a situation model in which Alice has a set of marbles, there is set of marbles for the difference between the two girls, and Sarah has a set of marbles that consists of Alice’s marbles and the difference set. In the planning process, the problem solver sets a goal of adding 3 and 2. In the executing process, the problem solver carries out the computation, yielding an answer of 5. In the monitoring process, the problem solver looks over what was done and concludes that 5 is a reasonable answer. In most complex problem-solving episodes, the four cognitive processes may not occur in linear order, but rather may interact with one another. Although some research focuses mainly on the execution process, problem solvers may tend to have more difficulty with the processes of representing, planning, and monitoring.

Knowledge for Problem Solving

An important theme in problem-solving research is that problem-solving proficiency on any task depends on the learner’s knowledge (Anderson et al., 2001 ; Mayer, 1992 ). Five kinds of knowledge are as follows:

Facts —factual knowledge about the characteristics of elements in the world, such as “Sacramento is the capital of California” Concepts —conceptual knowledge, including categories, schemas, or models, such as knowing the difference between plants and animals or knowing how a battery works Procedures —procedural knowledge of step-by-step processes, such as how to carry out long-division computations Strategies —strategic knowledge of general methods such as breaking a problem into parts or thinking of a related problem Beliefs —attitudinal knowledge about how one’s cognitive processing works such as thinking, “I’m good at this”

Although some research focuses mainly on the role of facts and procedures in problem solving, complex problem solving also depends on the problem solver’s concepts, strategies, and beliefs (Mayer, 1992 ).

Historical Approaches to Problem Solving

Psychological research on problem solving began in the early 1900s, as an outgrowth of mental philosophy (Humphrey, 1963 ; Mandler & Mandler, 1964 ). Throughout the 20th century four theoretical approaches developed: early conceptions, associationism, Gestalt psychology, and information processing.

Early Conceptions

The start of psychology as a science can be set at 1879—the year Wilhelm Wundt opened the first world’s psychology laboratory in Leipzig, Germany, and sought to train the world’s first cohort of experimental psychologists. Instead of relying solely on philosophical speculations about how the human mind works, Wundt sought to apply the methods of experimental science to issues addressed in mental philosophy. His theoretical approach became structuralism —the analysis of consciousness into its basic elements.

Wundt’s main contribution to the study of problem solving, however, was to call for its banishment. According to Wundt, complex cognitive processing was too complicated to be studied by experimental methods, so “nothing can be discovered in such experiments” (Wundt, 1911/1973 ). Despite his admonishments, however, a group of his former students began studying thinking mainly in Wurzburg, Germany. Using the method of introspection, subjects were asked to describe their thought process as they solved word association problems, such as finding the superordinate of “newspaper” (e.g., an answer is “publication”). Although the Wurzburg group—as they came to be called—did not produce a new theoretical approach, they found empirical evidence that challenged some of the key assumptions of mental philosophy. For example, Aristotle had proclaimed that all thinking involves mental imagery, but the Wurzburg group was able to find empirical evidence for imageless thought .

Associationism

The first major theoretical approach to take hold in the scientific study of problem solving was associationism —the idea that the cognitive representations in the mind consist of ideas and links between them and that cognitive processing in the mind involves following a chain of associations from one idea to the next (Mandler & Mandler, 1964 ; Mayer, 1992 ). For example, in a classic study, E. L. Thorndike ( 1911 ) placed a hungry cat in what he called a puzzle box—a wooden crate in which pulling a loop of string that hung from overhead would open a trap door to allow the cat to escape to a bowl of food outside the crate. Thorndike placed the cat in the puzzle box once a day for several weeks. On the first day, the cat engaged in many extraneous behaviors such as pouncing against the wall, pushing its paws through the slats, and meowing, but on successive days the number of extraneous behaviors tended to decrease. Overall, the time required to get out of the puzzle box decreased over the course of the experiment, indicating the cat was learning how to escape.

Thorndike’s explanation for how the cat learned to solve the puzzle box problem is based on an associationist view: The cat begins with a habit family hierarchy —a set of potential responses (e.g., pouncing, thrusting, meowing, etc.) all associated with the same stimulus (i.e., being hungry and confined) and ordered in terms of strength of association. When placed in the puzzle box, the cat executes its strongest response (e.g., perhaps pouncing against the wall), but when it fails, the strength of the association is weakened, and so on for each unsuccessful action. Eventually, the cat gets down to what was initially a weak response—waving its paw in the air—but when that response leads to accidentally pulling the string and getting out, it is strengthened. Over the course of many trials, the ineffective responses become weak and the successful response becomes strong. Thorndike refers to this process as the law of effect : Responses that lead to dissatisfaction become less associated with the situation and responses that lead to satisfaction become more associated with the situation. According to Thorndike’s associationist view, solving a problem is simply a matter of trial and error and accidental success. A major challenge to assocationist theory concerns the nature of transfer—that is, where does a problem solver find a creative solution that has never been performed before? Associationist conceptions of cognition can be seen in current research, including neural networks, connectionist models, and parallel distributed processing models (Rogers & McClelland, 2004 ).

Gestalt Psychology

The Gestalt approach to problem solving developed in the 1930s and 1940s as a counterbalance to the associationist approach. According to the Gestalt approach, cognitive representations consist of coherent structures (rather than individual associations) and the cognitive process of problem solving involves building a coherent structure (rather than strengthening and weakening of associations). For example, in a classic study, Kohler ( 1925 ) placed a hungry ape in a play yard that contained several empty shipping crates and a banana attached overhead but out of reach. Based on observing the ape in this situation, Kohler noted that the ape did not randomly try responses until one worked—as suggested by Thorndike’s associationist view. Instead, the ape stood under the banana, looked up at it, looked at the crates, and then in a flash of insight stacked the crates under the bananas as a ladder, and walked up the steps in order to reach the banana.

According to Kohler, the ape experienced a sudden visual reorganization in which the elements in the situation fit together in a way to solve the problem; that is, the crates could become a ladder that reduces the distance to the banana. Kohler referred to the underlying mechanism as insight —literally seeing into the structure of the situation. A major challenge of Gestalt theory is its lack of precision; for example, naming a process (i.e., insight) is not the same as explaining how it works. Gestalt conceptions can be seen in modern research on mental models and schemas (Gentner & Stevens, 1983 ).

Information Processing

The information processing approach to problem solving developed in the 1960s and 1970s and was based on the influence of the computer metaphor—the idea that humans are processors of information (Mayer, 2009 ). According to the information processing approach, problem solving involves a series of mental computations—each of which consists of applying a process to a mental representation (such as comparing two elements to determine whether they differ).

In their classic book, Human Problem Solving , Newell and Simon ( 1972 ) proposed that problem solving involved a problem space and search heuristics . A problem space is a mental representation of the initial state of the problem, the goal state of the problem, and all possible intervening states (based on applying allowable operators). Search heuristics are strategies for moving through the problem space from the given to the goal state. Newell and Simon focused on means-ends analysis , in which the problem solver continually sets goals and finds moves to accomplish goals.

Newell and Simon used computer simulation as a research method to test their conception of human problem solving. First, they asked human problem solvers to think aloud as they solved various problems such as logic problems, chess, and cryptarithmetic problems. Then, based on an information processing analysis, Newell and Simon created computer programs that solved these problems. In comparing the solution behavior of humans and computers, they found high similarity, suggesting that the computer programs were solving problems using the same thought processes as humans.

An important advantage of the information processing approach is that problem solving can be described with great clarity—as a computer program. An important limitation of the information processing approach is that it is most useful for describing problem solving for well-defined problems rather than ill-defined problems. The information processing conception of cognition lives on as a keystone of today’s cognitive science (Mayer, 2009 ).

Classic Issues in Problem Solving

Three classic issues in research on problem solving concern the nature of transfer (suggested by the associationist approach), the nature of insight (suggested by the Gestalt approach), and the role of problem-solving heuristics (suggested by the information processing approach).

Transfer refers to the effects of prior learning on new learning (or new problem solving). Positive transfer occurs when learning A helps someone learn B. Negative transfer occurs when learning A hinders someone from learning B. Neutral transfer occurs when learning A has no effect on learning B. Positive transfer is a central goal of education, but research shows that people often do not transfer what they learned to solving problems in new contexts (Mayer, 1992 ; Singley & Anderson, 1989 ).

Three conceptions of the mechanisms underlying transfer are specific transfer , general transfer , and specific transfer of general principles . Specific transfer refers to the idea that learning A will help someone learn B only if A and B have specific elements in common. For example, learning Spanish may help someone learn Latin because some of the vocabulary words are similar and the verb conjugation rules are similar. General transfer refers to the idea that learning A can help someone learn B even they have nothing specifically in common but A helps improve the learner’s mind in general. For example, learning Latin may help people learn “proper habits of mind” so they are better able to learn completely unrelated subjects as well. Specific transfer of general principles is the idea that learning A will help someone learn B if the same general principle or solution method is required for both even if the specific elements are different.

In a classic study, Thorndike and Woodworth ( 1901 ) found that students who learned Latin did not subsequently learn bookkeeping any better than students who had not learned Latin. They interpreted this finding as evidence for specific transfer—learning A did not transfer to learning B because A and B did not have specific elements in common. Modern research on problem-solving transfer continues to show that people often do not demonstrate general transfer (Mayer, 1992 ). However, it is possible to teach people a general strategy for solving a problem, so that when they see a new problem in a different context they are able to apply the strategy to the new problem (Judd, 1908 ; Mayer, 2008 )—so there is also research support for the idea of specific transfer of general principles.

Insight refers to a change in a problem solver’s mind from not knowing how to solve a problem to knowing how to solve it (Mayer, 1995 ; Metcalfe & Wiebe, 1987 ). In short, where does the idea for a creative solution come from? A central goal of problem-solving research is to determine the mechanisms underlying insight.

The search for insight has led to five major (but not mutually exclusive) explanatory mechanisms—insight as completing a schema, insight as suddenly reorganizing visual information, insight as reformulation of a problem, insight as removing mental blocks, and insight as finding a problem analog (Mayer, 1995 ). Completing a schema is exemplified in a study by Selz (Fridja & de Groot, 1982 ), in which people were asked to think aloud as they solved word association problems such as “What is the superordinate for newspaper?” To solve the problem, people sometimes thought of a coordinate, such as “magazine,” and then searched for a superordinate category that subsumed both terms, such as “publication.” According to Selz, finding a solution involved building a schema that consisted of a superordinate and two subordinate categories.

Reorganizing visual information is reflected in Kohler’s ( 1925 ) study described in a previous section in which a hungry ape figured out how to stack boxes as a ladder to reach a banana hanging above. According to Kohler, the ape looked around the yard and found the solution in a flash of insight by mentally seeing how the parts could be rearranged to accomplish the goal.

Reformulating a problem is reflected in a classic study by Duncker ( 1945 ) in which people are asked to think aloud as they solve the tumor problem—how can you destroy a tumor in a patient without destroying surrounding healthy tissue by using rays that at sufficient intensity will destroy any tissue in their path? In analyzing the thinking-aloud protocols—that is, transcripts of what the problem solvers said—Duncker concluded that people reformulated the goal in various ways (e.g., avoid contact with healthy tissue, immunize healthy tissue, have ray be weak in healthy tissue) until they hit upon a productive formulation that led to the solution (i.e., concentrating many weak rays on the tumor).

Removing mental blocks is reflected in classic studies by Duncker ( 1945 ) in which solving a problem involved thinking of a novel use for an object, and by Luchins ( 1942 ) in which solving a problem involved not using a procedure that had worked well on previous problems. Finding a problem analog is reflected in classic research by Wertheimer ( 1959 ) in which learning to find the area of a parallelogram is supported by the insight that one could cut off the triangle on one side and place it on the other side to form a rectangle—so a parallelogram is really a rectangle in disguise. The search for insight along each of these five lines continues in current problem-solving research.

Heuristics are problem-solving strategies, that is, general approaches to how to solve problems. Newell and Simon ( 1972 ) suggested three general problem-solving heuristics for moving from a given state to a goal state: random trial and error , hill climbing , and means-ends analysis . Random trial and error involves randomly selecting a legal move and applying it to create a new problem state, and repeating that process until the goal state is reached. Random trial and error may work for simple problems but is not efficient for complex ones. Hill climbing involves selecting the legal move that moves the problem solver closer to the goal state. Hill climbing will not work for problems in which the problem solver must take a move that temporarily moves away from the goal as is required in many problems.

Means-ends analysis involves creating goals and seeking moves that can accomplish the goal. If a goal cannot be directly accomplished, a subgoal is created to remove one or more obstacles. Newell and Simon ( 1972 ) successfully used means-ends analysis as the search heuristic in a computer program aimed at general problem solving, that is, solving a diverse collection of problems. However, people may also use specific heuristics that are designed to work for specific problem-solving situations (Gigerenzer, Todd, & ABC Research Group, 1999 ; Kahneman & Tversky, 1984 ).

Current and Future Issues in Problem Solving

Eight current issues in problem solving involve decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific problem solving, everyday thinking, and the cognitive neuroscience of problem solving.

Decision Making

Decision making refers to the cognitive processing involved in choosing between two or more alternatives (Baron, 2000 ; Markman & Medin, 2002 ). For example, a decision-making task may involve choosing between getting $240 for sure or having a 25% change of getting $1000. According to economic theories such as expected value theory, people should chose the second option, which is worth $250 (i.e., .25 x $1000) rather than the first option, which is worth $240 (1.00 x $240), but psychological research shows that most people prefer the first option (Kahneman & Tversky, 1984 ).

Research on decision making has generated three classes of theories (Markman & Medin, 2002 ): descriptive theories, such as prospect theory (Kahneman & Tversky), which are based on the ideas that people prefer to overweight the cost of a loss and tend to overestimate small probabilities; heuristic theories, which are based on the idea that people use a collection of short-cut strategies such as the availability heuristic (Gigerenzer et al., 1999 ; Kahneman & Tversky, 2000 ); and constructive theories, such as mental accounting (Kahneman & Tversky, 2000 ), in which people build a narrative to justify their choices to themselves. Future research is needed to examine decision making in more realistic settings.

Intelligence and Creativity

Although researchers do not have complete consensus on the definition of intelligence (Sternberg, 1990 ), it is reasonable to view intelligence as the ability to learn or adapt to new situations. Fluid intelligence refers to the potential to solve problems without any relevant knowledge, whereas crystallized intelligence refers to the potential to solve problems based on relevant prior knowledge (Sternberg & Gregorenko, 2003 ). As people gain more experience in a field, their problem-solving performance depends more on crystallized intelligence (i.e., domain knowledge) than on fluid intelligence (i.e., general ability) (Sternberg & Gregorenko, 2003 ). The ability to monitor and manage one’s cognitive processing during problem solving—which can be called metacognition —is an important aspect of intelligence (Sternberg, 1990 ). Research is needed to pinpoint the knowledge that is needed to support intelligent performance on problem-solving tasks.

Creativity refers to the ability to generate ideas that are original (i.e., other people do not think of the same idea) and functional (i.e., the idea works; Sternberg, 1999 ). Creativity is often measured using tests of divergent thinking —that is, generating as many solutions as possible for a problem (Guilford, 1967 ). For example, the uses test asks people to list as many uses as they can think of for a brick. Creativity is different from intelligence, and it is at the heart of creative problem solving—generating a novel solution to a problem that the problem solver has never seen before. An important research question concerns whether creative problem solving depends on specific knowledge or creativity ability in general.

Teaching of Thinking Skills

How can people learn to be better problem solvers? Mayer ( 2008 ) proposes four questions concerning teaching of thinking skills:

What to teach —Successful programs attempt to teach small component skills (such as how to generate and evaluate hypotheses) rather than improve the mind as a single monolithic skill (Covington, Crutchfield, Davies, & Olton, 1974 ). How to teach —Successful programs focus on modeling the process of problem solving rather than solely reinforcing the product of problem solving (Bloom & Broder, 1950 ). Where to teach —Successful programs teach problem-solving skills within the specific context they will be used rather than within a general course on how to solve problems (Nickerson, 1999 ). When to teach —Successful programs teaching higher order skills early rather than waiting until lower order skills are completely mastered (Tharp & Gallimore, 1988 ).

Overall, research on teaching of thinking skills points to the domain specificity of problem solving; that is, successful problem solving depends on the problem solver having domain knowledge that is relevant to the problem-solving task.

Expert Problem Solving

Research on expertise is concerned with differences between how experts and novices solve problems (Ericsson, Feltovich, & Hoffman, 2006 ). Expertise can be defined in terms of time (e.g., 10 years of concentrated experience in a field), performance (e.g., earning a perfect score on an assessment), or recognition (e.g., receiving a Nobel Prize or becoming Grand Master in chess). For example, in classic research conducted in the 1940s, de Groot ( 1965 ) found that chess experts did not have better general memory than chess novices, but they did have better domain-specific memory for the arrangement of chess pieces on the board. Chase and Simon ( 1973 ) replicated this result in a better controlled experiment. An explanation is that experts have developed schemas that allow them to chunk collections of pieces into a single configuration.

In another landmark study, Larkin et al. ( 1980 ) compared how experts (e.g., physics professors) and novices (e.g., first-year physics students) solved textbook physics problems about motion. Experts tended to work forward from the given information to the goal, whereas novices tended to work backward from the goal to the givens using a means-ends analysis strategy. Experts tended to store their knowledge in an integrated way, whereas novices tended to store their knowledge in isolated fragments. In another study, Chi, Feltovich, and Glaser ( 1981 ) found that experts tended to focus on the underlying physics concepts (such as conservation of energy), whereas novices tended to focus on the surface features of the problem (such as inclined planes or springs). Overall, research on expertise is useful in pinpointing what experts know that is different from what novices know. An important theme is that experts rely on domain-specific knowledge rather than solely general cognitive ability.

Analogical Reasoning

Analogical reasoning occurs when people solve one problem by using their knowledge about another problem (Holyoak, 2005 ). For example, suppose a problem solver learns how to solve a problem in one context using one solution method and then is given a problem in another context that requires the same solution method. In this case, the problem solver must recognize that the new problem has structural similarity to the old problem (i.e., it may be solved by the same method), even though they do not have surface similarity (i.e., the cover stories are different). Three steps in analogical reasoning are recognizing —seeing that a new problem is similar to a previously solved problem; abstracting —finding the general method used to solve the old problem; and mapping —using that general method to solve the new problem.

Research on analogical reasoning shows that people often do not recognize that a new problem can be solved by the same method as a previously solved problem (Holyoak, 2005 ). However, research also shows that successful analogical transfer to a new problem is more likely when the problem solver has experience with two old problems that have the same underlying structural features (i.e., they are solved by the same principle) but different surface features (i.e., they have different cover stories) (Holyoak, 2005 ). This finding is consistent with the idea of specific transfer of general principles as described in the section on “Transfer.”

Mathematical and Scientific Problem Solving

Research on mathematical problem solving suggests that five kinds of knowledge are needed to solve arithmetic word problems (Mayer, 2008 ):

Factual knowledge —knowledge about the characteristics of problem elements, such as knowing that there are 100 cents in a dollar Schematic knowledge —knowledge of problem types, such as being able to recognize time-rate-distance problems Strategic knowledge —knowledge of general methods, such as how to break a problem into parts Procedural knowledge —knowledge of processes, such as how to carry our arithmetic operations Attitudinal knowledge —beliefs about one’s mathematical problem-solving ability, such as thinking, “I am good at this”

People generally possess adequate procedural knowledge but may have difficulty in solving mathematics problems because they lack factual, schematic, strategic, or attitudinal knowledge (Mayer, 2008 ). Research is needed to pinpoint the role of domain knowledge in mathematical problem solving.

Research on scientific problem solving shows that people harbor misconceptions, such as believing that a force is needed to keep an object in motion (McCloskey, 1983 ). Learning to solve science problems involves conceptual change, in which the problem solver comes to recognize that previous conceptions are wrong (Mayer, 2008 ). Students can be taught to engage in scientific reasoning such as hypothesis testing through direct instruction in how to control for variables (Chen & Klahr, 1999 ). A central theme of research on scientific problem solving concerns the role of domain knowledge.

Everyday Thinking

Everyday thinking refers to problem solving in the context of one’s life outside of school. For example, children who are street vendors tend to use different procedures for solving arithmetic problems when they are working on the streets than when they are in school (Nunes, Schlieman, & Carraher, 1993 ). This line of research highlights the role of situated cognition —the idea that thinking always is shaped by the physical and social context in which it occurs (Robbins & Aydede, 2009 ). Research is needed to determine how people solve problems in authentic contexts.

Cognitive Neuroscience of Problem Solving

The cognitive neuroscience of problem solving is concerned with the brain activity that occurs during problem solving. For example, using fMRI brain imaging methodology, Goel ( 2005 ) found that people used the language areas of the brain to solve logical reasoning problems presented in sentences (e.g., “All dogs are pets…”) and used the spatial areas of the brain to solve logical reasoning problems presented in abstract letters (e.g., “All D are P…”). Cognitive neuroscience holds the potential to make unique contributions to the study of problem solving.

Problem solving has always been a topic at the fringe of cognitive psychology—too complicated to study intensively but too important to completely ignore. Problem solving—especially in realistic environments—is messy in comparison to studying elementary processes in cognition. The field remains fragmented in the sense that topics such as decision making, reasoning, intelligence, expertise, mathematical problem solving, everyday thinking, and the like are considered to be separate topics, each with its own separate literature. Yet some recurring themes are the role of domain-specific knowledge in problem solving and the advantages of studying problem solving in authentic contexts.

Future Directions

Some important issues for future research include the three classic issues examined in this chapter—the nature of problem-solving transfer (i.e., How are people able to use what they know about previous problem solving to help them in new problem solving?), the nature of insight (e.g., What is the mechanism by which a creative solution is constructed?), and heuristics (e.g., What are some teachable strategies for problem solving?). In addition, future research in problem solving should continue to pinpoint the role of domain-specific knowledge in problem solving, the nature of cognitive ability in problem solving, how to help people develop proficiency in solving problems, and how to provide aids for problem solving.

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Further Reading

Baron, J. ( 2008 ). Thinking and deciding (4th ed). New York: Cambridge University Press.

Duncker, K. ( 1945 ). On problem solving. Psychological Monographs , 58(3) (Whole No. 270).

Holyoak, K. J. , & Morrison, R. G. ( 2005 ). The Cambridge handbook of thinking and reasoning . New York: Cambridge University Press.

Mayer, R. E. , & Wittrock, M. C. ( 2006 ). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ: Erlbaum.

Sternberg, R. J. , & Ben-Zeev, T. ( 2001 ). Complex cognition: The psychology of human thought . New York: Oxford University Press.

Weisberg, R. W. ( 2006 ). Creativity . New York: Wiley.

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6.2: Problem Solving Strategies

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  • Mehgan Andrade and Neil Walker
  • College of the Canyons

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 3 ). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Table 1. Problem Solving Strategies

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results.

Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

· When one is faced with too much information

· When the time to make a decision is limited

· When the decision to be made is unimportant

· When there is access to very little information to use in making the decision

· When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Link to Learning

What problem-solving method could you use to solve Einstein’s famous riddle?

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Everyday Connections: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Picture1.png

Here is another popular type of puzzle that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Picture2.png

Take a look at the “Puzzling Scales” logic puzzle below. Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Picture3.png

Were you able to determine how many marbles are needed to balance the scales in Figure 3 ? You need nine . Were you able to solve the problems in Figure 1 and Figure 2 ? Here are the answers.

Picture4.png

Book cover

Learning and Teaching for Teachers pp 59–80 Cite as

Strategies for Learning

  • Pradeep Kumar Misra 2  
  • First Online: 19 October 2021

430 Accesses

We have already discussed that people learn in many ways, and there are many types of learning. As an exciting addition, people also use different strategies to learn. These strategies are popularly named ‘learning strategies.’ Learning strategies are simply referred to as methods and approaches used by learners to learn. There is a long list of learning strategies, and more keep evolving as well. These learning strategies range from improving memory to better studying or thinking critically. For example, concrete examples help you remember the content better . The spaced practice supports you to study better, and lateral thinking guides you for thinking differently. This chapter will help you to learn about many more exciting and popular learning strategies. Learning about these strategies will be useful for you as a learner and a teacher as well. As a teacher, you can use these to support learners, and as a learner, you can use these to learn as per your needs and interests.

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Learning strategies: a synthesis and conceptual model

  • John A C Hattie 1 &
  • Gregory M Donoghue 1  

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The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases. A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further, it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.

There has been a long debate about the purpose of schooling. These debates include claims that schooling is about passing on core notions of humanity and civilisation (or at least one’s own society’s view of these matters). They include claims that schooling should prepare students to live pragmatically and immediately in their current environment, should prepare students for the work force, should equip students to live independently, to participate in the life of their community, to learn to ‘give back’, to develop personal growth. 1

In the past 30 years, however, the emphasis in many western systems of education has been more on enhancing academic achievement—in domains such as reading, mathematics, and science—as the primary purpose of schooling. 2 Such an emphasis has led to curricula being increasingly based on achievement in a few privileged domains, and ‘great’ students are deemed those who attain high levels of proficiency in these narrow domains.

This has led to many countries aiming to be in the top echelon of worldwide achievement measures in a narrow range of subjects; for example, achievement measures such as PISA (tests of 15-year olds in mathematics, reading and science, across 65 countries in 2012) or PIRLS (Year-5 tests of mathematics, reading and science, across 57 countries in 2011). Indeed, within most school systems there is a plethora of achievement tests; many countries have introduced accountability pressures based on high levels of testing of achievement; and communities typically value high achievement or levels of knowledge. 3 The mantra underpinning these claims has been cast in terms of what students know and are able to do; the curriculum is compartmentalised into various disciplines of achievement; and students, teachers, parents and policy makers talk in terms of success in these achievement domains.

Despite the recent emphasis on achievement, the day-to-day focus of schools has always been on learning—how to know, how to know more efficiently and how to know more effectively. The underlying philosophy is more about what students are now ready to learn, how their learning can be enabled, and increasing the ‘how to learn’ proficiencies of students. In this scenario, the purpose of schooling is to equip students with learning strategies, or the skills of learning how to learn. Of course, learning and achievement are not dichotomous; they are related. 4 Through growth in learning in specific domains comes achievement and from achievement there can be much learning. The question in this article relates to identifying the most effective strategies for learning.

In our search, we identified >400 learning strategies: that is, those processes which learners use to enhance their own learning. Many were relabelled versions of others, some were minor modifications of others, but there remained many contenders purported to be powerful learning strategies. Such strategies help the learner structure his or her thinking so as to plan, set goals and monitor progress, make adjustments, and evaluate the process of learning and the outcomes. These strategies can be categorised in many ways according to various taxonomies and classifications (e.g., references 5 , 6 , 7 ). Boekaerts, 8 for example, argued for three types of learning strategies: (1) cognitive strategies such as elaboration, to deepen the understanding of the domain studied; (2) metacognitive strategies such as planning, to regulate the learning process; and (3) motivational strategies such as self-efficacy, to motivate oneself to engage in learning. Given the advent of newer ways to access information (e.g., the internet) and the mountain of information now at students’ fingertips, it is appropriate that Dignath, Buettner and Langfeldt 9 added a fourth category—management strategies such as finding, navigating, and evaluating resources.

But merely investigating these 400-plus strategies as if they were independent is not defensible. Thus, we begin with the development of a model of learning to provide a basis for interpreting the evidence from our meta-synthesis. The argument is that learning strategies can most effectively enhance performance when they are matched to the requirements of tasks (cf. 10 ).

A model of learning

The model comprises the following components: three inputs and three outcomes; student knowledge of the success criteria for the task; three phases of the learning process (surface, deep and transfer), with surface and deep learning each comprising an acquisition phase and a consolidation phase; and an environment for the learning ( Figure 1 ). We are proposing that various learning strategies are differentially effective depending on the degree to which the students are aware of the criteria of success, on the phases of learning process in which the strategies are used, and on whether the student is acquiring or consolidating their understanding. The following provides an overview of the components of the model (see reference 11 for a more detailed explanation of the model).

figure 1

A model of learning.

Input and outcomes

The model starts with three major sources of inputs: the skill, the will and the thrill. The ‘skill’ is the student’s prior or subsequent achievement, the ‘will’ relates to the student’s various dispositions towards learning, and the ‘thrill’ refers to the motivations held by the student. In our model, these inputs are also the major outcomes of learning. That is, developing outcomes in achievement (skill) is as valuable as enhancing the dispositions towards learning (will) and as valuable as inviting students to reinvest more into their mastery of learning (thrill or motivations).

The first component describes the prior achievement the student brings to the task. As Ausubel 12 claimed ‘if I had to reduce all of educational psychology to just one principle, I would say this ‘The most important single factor influencing learning is what the leaner already knows. Ascertain this and teach him accordingly. Other influences related to the skills students bring to learning include their working memory, beliefs, encouragement and expectations from the student’s cultural background and home.

Dispositions are more habits of mind or tendencies to respond to situations in certain ways. Claxton 13 claimed that the mind frame of a ‘powerful learner’ is based on the four major dispositions: resilience or emotional strength, resourcefulness or cognitive capabilities, reflection or strategic awareness, and relating or social sophistication. These dispositions involve the proficiency to edit, select, adapt and respond to the environment in a recurrent, characteristic manner. 14 But dispositions alone are not enough. Perkins et al. 15 outlined a model with three psychological components which must be present in order to spark dispositional behaviour: sensitivity—the perception of the appropriateness of a particular behaviour; inclination—the felt impetus toward a behaviour; and ability—the basic capacity and confidence to follow through with the behaviour.

There can be a thrill in learning but for many students, learning in some domains can be dull, uninviting and boring. There is a huge literature on various motivational aspects of learning, and a smaller literature on how the more effective motivational aspects can be taught. A typical demarcation is between mastery and performance orientations. Mastery goals are seen as being associated with intellectual development, the acquisition of knowledge and new skills, investment of greater effort, and higher-order cognitive strategies and learning outcomes. 16 Performance goals, on the other hand, have a focus on outperforming others or completing tasks to please others. A further distinction has been made between approach and avoidance performance goals. 17 – 19 The correlations of mastery and performance goals with achievement, however, are not as high as many have claimed. A recent meta-analysis found 48 studies relating goals to achievement (based on 12,466 students), and the overall correlation was 0.12 for mastery and 0.05 for performance goals on outcomes. 20 Similarly, Hulleman et al. 21 reviewed 249 studies ( N =91,087) and found an overall correlation between mastery goal and outcomes of 0.05 and performance goals and outcomes of 0.14. These are small effects and show the relatively low importance of these motivational attributes in relation to academic achievement.

An alternative model of motivation is based on Biggs 22 learning processes model, which combines motivation (why the student wants to study the task) and their related strategies (how the student approaches the task). He outlined three common approaches to learning: deep, surface and achieving. When students are taking a deep strategy, they aim to develop understanding and make sense of what they are learning, and create meaning and make ideas their own. This means they focus on the meaning of what they are learning, aim to develop their own understanding, relate ideas together and make connections with previous experiences, ask themselves questions about what they are learning, discuss their ideas with others and compare different perspectives. When students are taking a surface strategy, they aim to reproduce information and learn the facts and ideas—with little recourse to seeing relations or connections between ideas. When students are using an achieving strategy, they use a ‘minimax’ notion—minimum amount of effort for maximum return in terms of passing tests, complying with instructions, and operating strategically to meet a desired grade. It is the achieving strategy that seems most related to school outcomes.

Success criteria

The model includes a prelearning phase relating to whether the students are aware of the criteria of success in the learning task. This phase is less about whether the student desires to attain the target of the learning (which is more about motivation), but whether he or she understands what it means to be successful at the task at hand. When a student is aware of what it means to be successful before undertaking the task, this awareness leads to more goal-directed behaviours. Students who can articulate or are taught these success criteria are more likely to be strategic in their choice of learning strategies, more likely to enjoy the thrill of success in learning, and more likely to reinvest in attaining even more success criteria.

Success criteria can be taught. 23 , 24 Teachers can help students understand the criteria used for judging the students’ work, and thus teachers need to be clear about the criteria used to determine whether the learning intentions have been successfully achieved. Too often students may know the learning intention, but do not how the teacher is going to judge their performance, or how the teacher knows when or whether students have been successful. 25 The success criteria need to be as clear and specific as possible (at surface, deep, or transfer level) as this enables the teacher (and learner) to monitor progress throughout the lesson to make sure students understand and, as far as possible, attain the intended notions of success. Learning strategies that help students get an overview of what success looks like include planning and prediction, having intentions to implement goals, setting standards for judgement success, advance organisers, high levels of commitment to achieve success, and knowing about worked examples of what success looks like. 23

Environment

Underlying all components in the model is the environment in which the student is studying. Many books and internet sites on study skills claim that it is important to attend to various features of the environment such as a quiet room, no music or television, high levels of social support, giving students control over their learning, allowing students to study at preferred times of the day and ensuring sufficient sleep and exercise.

The three phases of learning: surface, deep and transfer

The model highlights the importance of both surface and deep learning and does not privilege one over the other, but rather insists that both are critical. Although the model does seem to imply an order, it must be noted that these are fuzzy distinctions (surface and deep learning can be accomplished simultaneously), but it is useful to separate them to identify the most effective learning strategies. More often than not, a student must have sufficient surface knowledge before moving to deep learning and then to the transfer of these understandings. As Entwistle 26 noted, ‘The verb ‘to learn’ takes the accusative’; that is, it only makes sense to analyse learning in relation to the subject or content area and the particular piece of work towards which the learning is directed, and also the context within which the learning takes place. The key debate, therefore, is whether the learning is directed content that is meaningful to the student, as this will directly affect student dispositions, in particular a student’s motivation to learn and willingness to reinvest in their learning.

A most powerful model to illustrate this distinction between surface and deep is the structure of observed learning outcomes, or SOLO, 27 , 28 as discussed above. The model has four levels: unistructural, multistructural, relational and extended abstract. A unistructural intervention is based on teaching or learning one idea, such as coaching one algorithm, training in underlining, using a mnemonic or anxiety reduction. The essential feature is that this idea alone is the focus, independent of the context or its adaption to or modification by content. A multistructural intervention involves a range of independent strategies or procedures, but without integrating or orchestration as to the individual differences or demands of content or context (such as teaching time management, note taking and setting goals with no attention to any strategic or higher-order understandings of these many techniques). Relational interventions involve bringing together these various multistructural ideas, and seeing patterns; it can involve the strategies of self-monitoring and self-regulation. Extended abstract interventions aim at far transfer (transfer between contexts that, initally, appear remote to one another) such that they produce structural changes in an individual’s cognitive functioning to the point where autonomous or independent learning can occur. The first two levels (one then many ideas) refer to developing surface knowing and the latter two levels (relate and extend) refer to developing deeper knowing. The parallel in learning strategies is that surface learning refers to studying without much reflecting on either purpose or strategy, learning many ideas without necessarily relating them and memorising facts and procedures routinely. Deep learning refers to seeking meaning, relating and extending ideas, looking for patterns and underlying principles, checking evidence and relating it to conclusions, examining arguments cautiously and critically, and becoming actively interested in course content (see reference 29 ).

Our model also makes a distinction between first acquiring knowledge and then consolidating it. During the acquisition phase, information from a teacher or instructional materials is attended to by the student and this is taken into short-term memory. During the consolidation phase, a learner then needs to actively process and rehearse the material as this increases the likelihood of moving that knowledge to longer-term memory. At both phases there can be a retrieval process, which involves transferring the knowing and understanding from long-term memory back into short-term working memory. 30 , 31

Acquiring surface learning

In their meta-analysis of various interventions, Hattie et al. 32 found that many learning strategies were highly effective in enhancing reproductive performances (surface learning) for virtually all students. Surface learning includes subject matter vocabulary, the content of the lesson and knowing much more. Strategies include record keeping, summarisation, underlining and highlighting, note taking, mnemonics, outlining and transforming, organising notes, training working memory, and imagery.

Consolidating surface learning

Once a student has begun to develop surface knowing it is then important to encode it in a manner such that it can retrieved at later appropriate moments. This encoding involves two groups of learning strategies: the first develops storage strength (the degree to which a memory is durably established or ‘well learned’) and the second develops strategies that develop retrieval strength (the degree to which a memory is accessible at a given point in time). 33 ‘Encoding’ strategies are aimed to develop both, but with a particular emphasis on developing retrieval strength. 34 Both groups of strategies invoke an investment in learning, and this involves ‘the tendency to seek out, engage in, enjoy and continuously pursue opportunities for effortful cognitive activity. 35 Although some may not ‘enjoy’ this phase, it does involve a willingness to practice, to be curious and to explore again, and a willingness to tolerate ambiguity and uncertainty during this investment phase. In turn, this requires sufficient metacognition and a calibrated sense of progress towards the desired learning outcomes. Strategies include practice testing, spaced versus mass practice, teaching test taking, interleaved practice, rehearsal, maximising effort, help seeking, time on task, reviewing records, learning how to receive feedback and deliberate practice (i.e., practice with help of an expert, or receiving feedback during practice).

Acquiring deep learning

Students who have high levels of awareness, control or strategic choice of multiple strategies are often referred to as ‘self-regulated’ or having high levels of metacognition. In Visible Learning , Hattie 36 described these self-regulated students as ‘becoming like teachers’, as they had a repertoire of strategies to apply when their current strategy was not working, and they had clear conceptions of what success on the task looked like. 37 More technically, Pintrich et al. 38 described self-regulation as ‘an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate and control their cognition, motivation and behaviour, guided and constrained by their goals and the contextual features in the environment’. These students know the what, where, who, when and why of learning, and the how, when and why to use which learning strategies. 39 They know what to do when they do not know what to do. Self-regulation strategies include elaboration and organisation, strategy monitoring, concept mapping, metacognitive strategies, self-regulation and elaborative interrogation.

Consolidating deep learning

Once a student has acquired surface and deep learning to the extent that it becomes part of their repertoire of skills and strategies, we may claim that they have ‘automatised’ such learning—and in many senses this automatisation becomes an ‘idea’, and so the cycle continues from surface idea to deeper knowing that then becomes a surface idea, and so on. 40 There is a series of learning strategies that develop the learner’s proficiency to consolidate deeper thinking and to be more strategic about learning. These include self-verbalisation, self-questioning, self-monitoring, self-explanation, self-verbalising the steps in a problem, seeking help from peers and peer tutoring, collaborative learning, evaluation and reflection, problem solving and critical thinking techniques.

There are skills involved in transferring knowledge and understanding from one situation to a new situation. Indeed, some have considered that successful transfer could be thought as synonymous with learning. 41 , 42 There are many distinctions relating to transfer: near and far transfer, 43 low and high transfer, 44 transfer to new situations and problem solving transfer, 5 and positive and negative transfer. 45 Transfer is a dynamic, not static, process that requires learners to actively choose and evaluate strategies, consider resources and surface information, and, when available, to receive or seek feedback to enhance these adaptive skills. Reciprocal teaching is one program specifically aiming to teach these skills; for example, Bereiter and Scardamalia 46 have developed programs in the teaching of transfer in writing, where students are taught to identify goals, improve and elaborate existing ideas, strive for idea cohesion, present their ideas to groups and think aloud about how they might proceed. Similarly, Schoenfeld 47 outlined a problem-solving approach to mathematics that involves the transfer of skills and knowledge from one situation to another. Marton 48 argued that transfer occurs when the learner learns strategies that apply in a certain situation such that they are enabled to do the same thing in another situation when they realise that the second situation resembles (or is perceived to resemble) the first situation. He claimed that not only sameness, similarity, or identity might connect situations to each other, but also small differences might connect them as well. Learning how to detect such differences is critical for the transfer of learning. As Heraclitus claimed, no two experiences are identical; you do not step into the same river twice.

Overall messages from the model

There are four main messages to be taken from the model. First, if the success criteria is the retention of accurate detail (surface learning) then lower-level learning strategies will be more effective than higher-level strategies. However, if the intention is to help students understand context (deeper learning) with a view to applying it in a new context (transfer), then higher level strategies are also needed. An explicit assumption is that higher level thinking requires a sufficient corpus of lower level surface knowledge to be effective—one cannot move straight to higher level thinking (e.g., problem solving and creative thought) without sufficient level of content knowledge. Second, the model proposes that when students are made aware of the nature of success for the task, they are more likely to be more involved in investing in the strategies to attain this target. Third, transfer is a major outcome of learning and is more likely to occur if students are taught how to detect similarities and differences between one situation and a new situation before they try to transfer their learning to the new situation. Hence, not one strategy may necessarily be best for all purposes. Fourth, the model also suggests that students can be advantaged when strategy training is taught with an understanding of the conditions under which the strategy best works—when and under what circumstance it is most appropriate.

The current study

The current study synthesises the many studies that have related various learning strategies to outcomes. This study only pertains to achievement outcomes (skill, on the model of learning); further work is needed to identify the strategies that optimise the dispositions (will) and the motivation (thrill) outcomes. The studies synthesised here are from four sources. First, there are the meta-analyses among the 1,200 meta-analyses in Visible Learning that relate to strategies for learning. 36 , 49 , 50 Second, there is the meta-analysis conducted by Lavery 51 on 223 effect-sizes derived from 31 studies relating to self-regulated learning interventions. The third source is two major meta-analyses by a Dutch team of various learning strategies, especially self-regulation. And the fourth is a meta-analysis conducted by Donoghue et al. 52 based on a previous analysis by Dunlosky et al. 53

The data in Visible Learning is based on 800 meta-analyses relating influences from the home, school, teacher, curriculum and teaching methods to academic achievement. Since its publication in 2009, the number of meta-analyses now exceeds 1,200, and those influences specific to learning strategies are retained in the present study. Lavery 51 identified 14 different learning strategies and the overall effect was 0.46—with greater effects for organising and transforming (i.e., deliberate rearrangement of instructional materials to improve learning, d =0.85) and self-consequences (i.e., student expectation of rewards or punishment for success or failure, d =0.70). The lowest effects were for imagery (i.e., creating or recalling vivid mental images to assist learning, d =0.44) and environmental restructuring (i.e., efforts to select or arrange the physical setting to make learning easier, d =0.22). She concluded that the higher effects involved ‘teaching techniques’ and related to more ‘deep learning strategies’, such as organising and transforming, self-consequences, self-instruction, self-evaluation, help-seeking, keeping records, rehearsing/memorising, reviewing and goal-setting. The lower ranked strategies were more ‘surface learning strategies’, such as time management and environmental restructuring.

Of the two meta-analyses conducted by the Dutch team, the first study, by Dignath et al. 9 analysed 357 effects from 74 studies ( N =8,619). They found an overall effect of 0.73 from teaching methods of self-regulation. The effects were large for achievement (elementary school, 0.68; high school, 0.71), mathematics (0.96, 1.21), reading and writing (0.44, 0.55), strategy use (0.72, 0.79) and motivation (0.75, 0.92). In the second study, Donker et al. 54 reviewed 180 effects from 58 studies relating to self-regulation training, reporting an overall effect of 0.73 in science, 0.66 in mathematics and 0.36 in reading comprehension. The most effective strategies were cognitive strategies (rehearsal 1.39, organisation 0.81 and elaboration 0.75), metacognitive strategies (planning 0.80, monitoring 0.71 and evaluation 0.75) and management strategies (effort 0.77, peer tutoring 0.83, environment 0.59 and metacognitive knowledge 0.97). Performance was almost always improved by a combination of strategies, as was metacognitive knowledge. This led to their conclusion that students should not only be taught which strategies to use and how to apply them (declarative knowledge or factual knowledge) but also when (procedural or how to use the strategies) and why to use them (conditional knowledge or knowing when to use a strategy).

Donoghue et al. 52 conducted a meta-analysis based on the articles referenced in Dunlosky et al. 53 They reviewed 10 learning strategies and a feature of their review is a careful analysis of possible moderators to the conclusions about the effectiveness of these learning strategies, such as learning conditions (e.g., study alone or in groups), student characteristics (e.g., age, ability), materials (e.g., simple concepts to problem-based analyses) and criterion tasks (different outcome measures).

In the current study, we independently assigned all strategies to the various parts of the model—this was a straightforward process, and the few minor disagreements were resolved by mutual agreement. All results are presented in Appendix 1.

Results: the meta-synthesis of learning strategies

There are 302 effects derived from the 228 meta-analyses from the above four sources that have related some form of learning strategy to an achievement outcome. Most are experimental–control studies or pre–post studies, whereas some are correlations ( N =37). There are 18,956 studies (although some may overlap across meta-analyses). Only 125 meta-analyses reported the sample size ( N =11,006,839), but if the average (excluding the outlier 7 million from one meta-analysis) is used for the missing sample sizes, the best estimate of sample size is between 13 and 20 million students.

The average effect is 0.53 but there is considerable variance ( Figure 2 ), and the overall number of meta-analyses, studies, number of people (where provided), effects and average effect-sizes for the various phases of the model are provided in Table 1 . The effects are lowest for management of the environment and ‘thrill’ (motivation), and highest for developing success criteria across the learning phases. The variance is sufficiently large, however, that it is important to look at specific strategies within each phase of the model.

figure 2

The average and the distribution of all effect sizes.

Synthesis of the input phases of the model

The inputs: skills.

There are nine meta-analyses that have investigated the relation between prior achievement and subsequent achievement, and not surprisingly these relations are high ( Table 2 ). The average effect-size is 0.77 (s.e.=0.10), which translates to a correlation of 0.36—substantial for any single variable. The effects of prior achievement are lowest in the early years, and highest from high school to university. One of the purposes of school, however, is to identify those students who are underperforming relative to their abilities and thus to not merely accept prior achievement as destiny. The other important skill is working memory—which relates to the amount of information that can be retained in short-term working memory when engaged in processing, learning, comprehension, problem solving or goal-directed thinking. 55 Working memory is strongly related to a person’s ability to reason with novel information (i.e., general fluid intelligence. 56

The inputs: will

There are 28 meta-analyses related to the dispositions of learning from 1,304 studies and the average effect-size is 0.48 (s.e.=0.09; Table 3 ). The effect of self-efficacy is highest ( d =0.90), followed by increasing the perceived value of the task ( d = 0.46), reducing anxiety ( d =0.45) and enhancing the attitude to the content ( d =0.35). Teachers could profitably increase students’ levels of confidence and efficacy to tackle difficult problems; not only does this increase the probability of subsequent learning but it can also help reduce students’ levels of anxiety. It is worth noting the major movement in the anxiety and stress literature in the 1980s moved from a preoccupation on understanding levels of stress to providing coping strategies—and these strategies were powerful mediators in whether people coped or not. 57 Similarly in learning, it is less the levels of anxiety and stress but the development of coping strategies to deal with anxiety and stress. These strategies include being taught to effectively regulate negative emotions; 58 increasing self-efficacy, which relates to developing the students conviction in their own competence to attain desired outcomes; 59 focusing on the positive skills already developed; increasing social support and help seeking; reducing self-blame; and learning to cope with error and making mistakes. 60 Increasing coping strategies to deal with anxiety and promoting confidence to tackle difficult and challenging learning tasks frees up essential cognitive resources required for the academic work.

There has been much discussion about students having growth—or incremental—mindsets (human attributes are malleable not fixed) rather than fixed mindsets (attributes are fixed and invariant). 61 However, the evidence in Table 3 ( d =0.19) shows how difficult it is to change to growth mindsets, which should not be surprising as many students work in a world of schools dominated by fixed notions—high achievement, ability groups, and peer comparison.

The inputs: thrill

The thrill relates to the motivation for learning: what is the purpose or approach to learning that the student adopts? Having a surface or performance approach motivation (learning to merely pass tests or for short-term gains) or mastery goals is not conducive to maximising learning, whereas having a deep or achieving approach or motivation is helpful ( Table 4 ). A possible reason why mastery goals are not successful is that too often the outcomes of tasks and assessments are at the surface level and having mastery goals with no strategic sense of when to maximise them can be counter-productive. 62 Having goals, per se , is worthwhile—and this relates back to the general principle of having notions of what success looks like before investing in the learning. The first step is to teach students to have goals relating to their upcoming work, preferably the appropriate mix of achieving and deep goals, ensure the goals are appropriately challenging and then encourage students to have specific intentions to achieve these goals. Teaching students that success can then be attributed to their effort and investment can help cement this power of goal setting, alongside deliberate teaching.

The environment

Despite the inordinate attention, particularly by parents, on structuring the environment as a precondition for effective study, such effects are generally relatively small ( Table 5 ). It seems to make no differences if there is background music, a sense of control over learning, the time of day to study, the degree of social support or the use of exercise. Given that most students receive sufficient sleep and exercise, it is perhaps not surprising that these are low effects; of course, extreme sleep or food deprivation may have marked effects.

Knowing the success criteria

A prediction from the model of learning is that when students learn how to gain an overall picture of what is to be learnt, have an understanding of the success criteria for the lessons to come and are somewhat clear at the outset about what it means to master the lessons, then their subsequent learning is maximised. The overall effect across the 31 meta-analyses is 0.54, with the greatest effects relating to providing students with success criteria, planning and prediction, having intentions to implement goals, setting standards for self-judgements and the difficulty of goals ( Table 6 ). All these learning strategies allow students to see the ‘whole’ or the gestalt of what is targeted to learn before starting the series of lessons. It thus provides a ‘coat hanger’ on which surface-level knowledge can be organised. When a teacher provides students with a concept map, for example, the effect on student learning is very low; but in contrast, when teachers work together with students to develop a concept map, the effect is much higher. It is the working with students to develop the main ideas, and to show the relations between these ideas to allow students to see higher-order notions, that influences learning. Thus, when students begin learning of the ideas, they can begin to know how these ideas relate to each other, how the ideas are meant to form higher order notions, and how they can begin to have some control or self-regulation on the relation between the ideas.

Synthesis of the learning phases of the model

There are many strategies, such as organising, summarising, underlining, note taking and mnemonics that can help students master the surface knowledge ( Table 7 ). These strategies can be deliberately taught, and indeed may be the only set of strategies that can be taught irrespective of the content. However, it may be that for some of these strategies, the impact is likely to be higher if they are taught within each content domain, as some of the skills (such as highlighting, note taking and summarising) may require specific ideas germane to the content being studied.

While it appears that training working memory can have reasonable effects ( d =0.53) there is less evidence that training working memory transfers into substantial gains in academic attainment. 63 There are many emerging and popular computer games that aim to increase working memory. For example, CogMed is a computer set of adaptive routines that is intended to be used 30–40 min a day for 25 days. A recent meta-analysis (by the commercial owners 64 ) found average effect-sizes (across 43 studies) exceed 0.70, but in a separate meta-analysis of 21 studies on the longer term effects of CogMed, there was zero evidence of transfer to subjects such as mathematics or reading 65 . Although there were large effects in the short term, they found that these gains were not maintained at follow up (about 9 months later) and no evidence to support the claim that working memory training produces generalised gains to the other skills that have been investigated (verbal ability, word decoding or arithmetic) even when assessment takes place immediately after training. For the most robust studies, the effect of transfer is zero. It may be better to reduce working memory demands in the classroom. 66

The investment of effort and deliberate practice is critical at this consolidation phase, as are the abilities to listen, seek and interpret the feedback that is provided ( Table 8 ). At this consolidation phase, the task is to review and practice (or overlearn) the material. Such investment is more valuable if it is spaced over time rather than massed. Rehearsal and memorisation is valuable—but note that memorisation is not so worthwhile at the acquisition phase. The difficult task is to make this investment in learning worthwhile, to make adjustments to the rehearsal as it progresses in light of high levels of feedback, and not engage in drill and practice. These strategies relating to consolidating learning are heavily dependent on the student’s proficiency to invest time on task wisely, 67 to practice and learn from this practice and to overlearn such that the learning is more readily available in working memory for the deeper understanding.

Acquiring deeper learning

Nearly all the strategies at this phase are powerful in enhancing learning ( Table 9 ). The ability to elaborate and organise, monitor the uses of the learning strategies, and have a variety of metacognitive strategies are the critical determinants of success at this phase of learning. A major purpose is for the student to deliberately activate prior knowledge and then make relations and extensions beyond what they have learned at the surface phase.

At this phase, the power of working with others is most apparent ( Table 10 ). This involves skills in seeking help from others, listening to others in discussion and developing strategies to ‘speak’ the language of learning. It is through such listening and speaking about their learning that students and teachers realise what they do deeply know, what they do not know and where they are struggling to find relations and extensions. An important strategy is when students become teachers of others and learn from peers, as this involves high levels of regulation, monitoring, anticipation and listening to their impact on the learner.

There has been much research confirming that teaching help-seeking strategies is successful, but how this strategy then works in classrooms is more complex. Teachers have to welcome students seeking help, and there needs to be knowledgeable others (e.g., peers) from whom to seek the help—too often students left in unsupported environments can seek and gain incorrect help and not know the help is incorrect. 68 Ryan and Shin 69 also distinguished between adaptive help seeking (seeking help from others, such as an explanation, a hint, or an example, that would further learning and promote independent problem solving in the future) and expedient help seeking (seeking help that expedites task completion, such as help that provides the answer and is not focused on learning). They showed that adaptive help seeking from peers declines and expedient help seeking increases during early adolescence. Further, increases in expedient help seeking were associated with declines in achievement but changes in adaptive help seeking were unrelated to achievement. The key is for teachers to teach adaptive help seeking, to ensure the help is dependable and correct and to see this more of a student than a teacher skill. Help seeking needs to be welcomed before it can have an effect.

The transfer model promoted by Marton 48 seems to be supported in that a key in teaching for transfer involves understanding the patterns, similarities and differences in the transfer before applying the strategies to new task ( Table 11 ). Marton argued that transfer occurs when students learn strategies that apply in a certain situation such that they are enabled to do the same thing in another situation to the degree that they realise how the second situation does (or does not) resemble the first situation. It is learning to detect differences and similarities that is the key that leads to transfer of learning.

Discussion and Conclusions

There is much debate about the optimal strategies of learning, and indeed we identified >400 terms used to describe these strategies. Our initial aim was to rank the various strategies in terms of their effectiveness but this soon was abandoned. There was too much variability in the effectiveness of most strategies depending on when they were used during the learning process, and thus we developed the model of learning presented in this article. Like all models, it is a conjecture, it aims to say much and it is falsifiable. The efficacy of any model can be seen as an expression of its capacity to generate a scalable solution to a problem or need in ways that resolve more issues than prevailing theories or approaches. 70 The model posits that learning must be embedded in some content (something worth knowing) and thus the current claims about developing 21st century skills sui generis are most misleading. These skills often are promoted as content free and are able to be developed in separate courses (e.g., critical thinking, resilience). Our model, however, suggests that such skills are likely to be best developed relative to some content. There is no need to develop learning strategy courses, or teach the various strategies outside the context of the content. Instead, the strategies should be an integral part of the teaching and learning process, and can be taught within this process.

The model includes three major inputs and outcomes. These relate to what the students bring to the learning encounter (skill), their dispositions about learning (will) and their motivations towards the task (thrill). The first set of strategies relate to teaching students the standards for what is to be learned (the success criteria). We propose that effective learning strategies will be different depending on the phase of the learning—the strategies will be different when a student is first acquiring the matters to be learnt compared with when the student is embedding or consolidating this learning. That is, the strategies are differentially effective depending on whether the learning intention is surface learning (the content), deep learning (the relations between content) or the transfer of the skills to new situations or tasks. In many ways this demarcation is arbitrary (but not capricious) and more experimental research is needed to explore these conjectures. Further, the model is presented as linear whereas there is often much overlap in the various phases. For example, to learn subject matter (surface) deeply (i.e., to encode in memory) is helped by exploring and understanding its meaning; success criteria can have a mix of surface and deep and even demonstrate the transfer to other (real world) situations; and often deep learning necessitates returning to acquire specific surface level vocabulary and understanding. In some cases, there can be multiple overlapping processes. A reviewer provided a clear example: in learning that the internal angles of a quadrilateral add up to 360°, this might involve surface learning, which then requires rehearsal to consolidate, some self-questioning to apply, some detection of similarities to then work out what the internal angles of a hexagon might be, and spotting similarities to the triangle rule. There may be no easy way to know the right moment, or no easy demarcation of the various phases. The proposal in this paper is but a ‘model’ to help clarify the various phases of learning, and in many real world situations there can be considerable overlap.

We have derived six sets of propositions from our conceptual model of learning and the results of our meta-synthesis of research on learning strategies. The first set relates to the differential role played by what students bring to and take from the learning encounter—the inputs and outcomes. Second, there are some strategies that are more effective than others—but their relative effectiveness depends on the phase in the model of learning in which they take place. Third is the distinction between surface learning, deep learning and the transfer of learning. The fourth set relates to the skills of transfer, the fifth to how the model of learning can be used to resolve some unexpected findings about the effectiveness of some strategies, and the sixth set discusses the question ‘what is learning?’.

The intertwining role of skill, will, and thrill

Our first set of claims relates to the differential role of what students bring to and take from the learning encounter. Rather than arguing that many factors contribute to achievement (an important but sometimes the only privileged outcome of learning), we are promoting the notion that the skill, will and thrill can intertwine during learning and that these three inputs are also important outcomes of learning—the aim is to enhance the will (e.g., the willingness to reinvest in more and deeper learning), the thrill (e.g., the emotions associated with successful learning, the curiosity and the willingness to explore what one does not know) and the skills (e.g., the content and the deeper understanding). The relation between the thrill, will and skill can vary depending on the student and the requirements of the task. Certainly, negative emotions, such as those induced by fear, anxiety, and stress can directly and negatively affect learning and memory. Such negative emotions block learning: ‘If the student is faced with sources of stress in an educational context which go beyond the positive challenge threshold—for instance, aggressive teachers, bullying students or incomprehensible learning materials whether books or computers—it triggers fear and cognitive function is negatively affected. 71 Our argument is that learning can lead to enhanced skills, dispositions, motivations and excitements that can be reinvested in learning, and can lead to students setting higher standards for their success criteria. When skill, will, and thrill overlap, this should be considered a bonus; developing each is a worthwhile outcome of schooling in its own right.

It is all in the timing

Our second set of claims is that while it is possible to nominate the top 10 learning strategies the more critical conclusion is that the optimal strategies depend on where in the learning cycle the student is located. This strategic skill in using the strategies at the right moment is akin to the message in the Kenny Rogers song—you need to ‘know when to hold ‘em, know when to fold ‘em’. For example, when starting a teaching sequence, it is most important to be concerned that students have confidence they can understand the lessons, see value in the lessons and are not overly anxious about their skills to be mastered. Providing them early on with an overview of what successful learning in the lessons will look like (knowing the success criteria) will help them reduce their anxiety, increase their motivation, and build both surface and deeper understandings.

To acquire surface learning, it is worthwhile knowing how to summarise, outline and relate the learning to prior achievement; and then to consolidate this learning by engaging in deliberate practice, rehearsing over time and learning how to seek and receive feedback to modify this effort. To acquire deep understanding requires the strategies of planning and evaluation and learning to monitor the use of one’s learning strategies; and then to consolidate deep understanding calls on the strategy of self-talk, self-evaluation and self-questioning and seeking help from peers. Such consolidation requires the learner to think aloud, learn the ‘language of thinking’, 72 know how to seek help, self-question and work through the consequences of the next steps in learning. To transfer learning to new situations involves knowing how to detect similarities and differences between the old and the new problem or situations.

We recommend that these strategies are developed by embedding them into the cycle of teaching rather than by running separate sessions, such as ‘how to learn’ or study skills courses. There is a disappointing history of educational programs aimed at teaching students how to learn. 30 , 73 , 74 Wiliam 75 made this case for why teaching these learning strategies (e.g., critical thinking) out of context is unlikely to develop a generic skill applicable to many subjects. He noted that in a ‘mathematics proof, critical thinking might involve ensuring that each step follows from the previous one (e.g., by checking that there has not been a division by zero). In reading a historical account, critical thinking might involve considering the author of the account, the potential biases and limitations that the author may be bringing to the account, and what other knowledge the reader has about the events being described. The important point here is that although there is some commonality between the processes in mathematics and history, they are not the same. Developing a capacity for critical thinking in history does not make one better at critical thinking in mathematics. For all of the apparent similarities, critical thinking in history and critical thinking in mathematics are different, and they are developed in different ways’. Many others have noted that metacognition is not knowledge-free but needs to be taught in the context of the individual subject areas. 76 , 77 Perkins 78 also noted that there is a certain art to infusing the teaching of thinking into content learning. Sometimes, ‘teachers think it is enough simply to establish a generally thoughtful atmosphere in a classroom, with regular expectations for thinking critically and creatively...teaching for know-how about learning to learn is a much more time-consuming enterprise than teaching for just learning the ideas... Building active know-how requires much more attention’.

Another aspect to consider is the difference, identified in the model, between being first exposed to learning and the consolidation of this learning. This distinction is far from novel. Shuell, 79 for example, distinguished between initial, intermediate, and final phases of learning. In the initial phase, the students can encounter a ‘large array of facts and pieces of information that are more-or-less isolated conceptually... there appears to be little more than a wasteland with few landmarks to guide the traveller on his or her journey towards understanding and mastery’. Students can use existing schema to make sense of this new information, or can be guided to have more appropriate schema (and thus experience early stages of concept learning and relation between ideas) otherwise the information may remain as isolated facts, or be linked erroneously to previous understandings. At the intermediate phase, the learner begins to see similarities and relationships among these seemingly conceptually isolated pieces of information. ‘The fog continues to lift but still has not burnt off completely’. During the final phase, the knowledge structure becomes well integrated and functions more autonomously, and the emphasis is more on performance or exhibiting the outcome of learning.

Horses for courses: matching strategies with phases

The third set of claims relates to the distinction between surface, deep, and transfer of learning. Although not a hard and fast set of demarcations, surface learning refers more to the content and underlying skills; deep learning to the relationships between, and extensions of, ideas; and transfer to the proficiency to apply learning to new problems and situations. During the surface learning phase, an aim is to assist students to overlearn certain ideas and thus reduce the needs of their working memory to work with these new facts when moving into the deeper understanding phase. Note, for example, that Marton et al. 80 made an important distinction between memorising without understanding first and called this rote memorisation (which has long term effect), and memorisation when you have understood and called this meaningful memorisation (which can be powerful). The evidence in the current study supports this distinction.

It is when students have much information, or many seemingly unrelated ideas, that the learning strategies for the deep phase are optimally invoked. This is when they should be asked to integrate ideas with previous schema or modify their previous schema to integrate new ideas and ways of thinking. The key to this process is first gaining ideas—a fact often missed by those advocating deeper thinking strategies when they try to teach these skills prior to developing sufficient knowledge within the content domain. The students need to first have ideas before they can relate them. The model does not propose discarding the teaching or learning skills that have been developed to learn surface knowing, but advocates the benefits of a more appropriate balance of surface and deeper strategies and skills that then lead to transfer. The correct balance of surface to deep learning depends on the demands of the task. It is likely that more emphasis on surface strategies is probably needed as students learn new ideas, moving to an emphasis on deeper strategies as they become more proficient.

Pause and reflect: detecting similarities and differences

The fourth set of claims relate to the skills of transfer, and how important it is to teach students to pause and detect the similarities and differences between previous tasks and the new one, before attempting to answer a new problem. Such transfer can be positive, such as when a learner accurately remembers a learning outcome reached in a certain situation and appropriately applies it in a new and similar situation, or negative, such as when a learner applies a strategy used successfully in one situation in a new situation where this strategy is not appropriate. Too many (particularly struggling) students over-rehearse a few learning strategies (e.g., copying and highlighting) and apply them in situations regardless of the demands of new tasks. Certainly, the fundamental skill for positive transfer is stopping before addressing the problem and asking about the differences and similarities of the new to any older task situation. This skill can be taught.

This ability to notice similarities and differences over content is quite different for novices and experts 81 , 82 and we do not simply learn from experience but we also learn to experience. 83 Preparation for future learning involves opportunities to try our hunches in different contexts, receive feedback, engage in productive failure and learn to revise our knowing based on feedback. The aim is to solve problems more efficiently, and also to ‘let go’ of previously acquired knowledge in light of more sophisticated understandings—and this can have emotional consequences: ‘Failure to change strategies in new situations has been described as the tyranny of success’. 84 It is not always productive for students to try the same thing that worked last time. Hence there may need to be an emphasis on knowledge-building rather than knowledge-telling, 85 and systematic inquiry based on theory-building and disconfirmation rather than simply following processes for how to find some result.

Why some strategies do not work

The fifth set of claims relate to how the model can be used to resolve some of the unexpected findings about the impact of various teaching methods. In Visible Learning , 36 it was noted that many programs that seem to lead to developing deeper processing have very low effect sizes (e.g., inquiry based methods, d =0.31; problem-based learning, d =0.15). For example, there have been 11 meta-analyses relating to problem-based learning based on 509 studies, leading to an average small effect ( d =0.15). It hardly seems necessary to run another problem-based program (particularly in first-year medicine, where four of the meta-analyses were completed) to know that the effects of problem-based learning on outcomes are small. The reason for this low effect seems to be related to using problem-based methods before attaining sufficient surface knowledge. When problem-based learning is used in later medical years, the effects seem to increase. Albanese and Mitchell 86 claimed that increased years of exposure to medical education increases the effect of problem-based learning. They argued that lack of experience (and lack of essential surface knowledge) leads the student to make more errors in their knowledge base, add irrelevant material to their explanations and engage in backward reasoning (from the unknown to the givens), whereas experts engaged in forward reasoning (also see references 87 , 88 ). Walker et al. 89 also noted that novice problem-based learning students tended to engage in far more backward-driven reasoning, which results in more errors during problem solving and may persist even after the educational intervention is complete. It is likely that problem-based learning works more successfully when students engage in forward reasoning and this depends on having sufficient content knowledge to make connections.

Deep understanding in problem-based learning requires a differentiated knowledge structure, 90 and this may need to be explicitly taught—as there is no assumption that students will see similarities and differences in contexts by themselves. There is a limit to what we can reasonably expect students to discover, and it may require teaching students to make predictions based on features that were told to them and that they may not notice on their own. Deliberate teaching of these surface features can offer a higher level of explanation that would be difficult or time consuming to discover. A higher level explanation is important because it provides a generative framework that can extend one understanding beyond the specific cases that have been analysed and experienced. On the other hand, the problems need not be too overly structured, as then students do not gain experience of searching out conceptual tools or homing in on particular cases of application. 78

Another example of the different requirements of surface and deep learning is the effect of asking students to explore errors and misconceptions during their learning. Using meta-analysis, Keith and Frese 91 found that the average effect of using these strategies when the outcome was surface learning was −0.15 and when the outcome was deep learning and far transfer to new problems, it was 0.80.

So: what is learning?

The sixth set of claims relate to the notion of ‘what is learning?’. The argument in this article is that learning is the outcome of the processes of moving from surface to deep to transfer. Only then will students be able to go beyond the information given to ‘figure things out’, which is one of the few untarnishable joys of life. 92 One of the greatest triumphs of learning is what Perkins 78 calls ‘knowing one’s way around’ a particular topic or ‘playing the whole game’ of history, mathematics, science or whatever. This is a function of knowing much and then using this knowledge in the exploration of relations and to make extensions to other ideas, and being able to know what to do when one does not know what to do (the act of transfer).

Concluding comments

Like all models, the one proposed in this article invites as many conjectures and directions for further research as it provide a basis for interpreting the evidence from the meta-synthesis. It helps make sense of much of the current literature but it is speculative in that it also makes some untested predictions. There is much solace in Popper's 93 claim that ‘Bold ideas, unjustified anticipations, and speculative thought, are our only means for interpreting nature: our only organon, our only instrument, for grasping her. And we must hazard them to win our prize. Those among us who are unwilling to expose their ideas to the hazard of refutation do not take part in the scientific game.’ Further research is needed, for example, to better understand the optimal order through the various phases; there may be circumstances where it may be beneficial to learn the deeper notions before developing the surface knowledge. It is highly likely that as one develops many ideas and even relates and extends them, these become ‘ideas’ and the cycle continues. 94 We know much, but we need to know much more, and in particular we need to know how these many learning strategies might be better presented in another competing model. Such testing of a bold model and making predictions from models is, according to Popper, how science progresses.

Further research is needed that asks whether the distinction between the acquisition and the consolidation of learning is a distinctive difference, a melding from one to the other or whether both can occur simultaneously. If there is a difference, then more research on ascertaining the best time to move from acquisition to consolidation would be informative. Similarly, there is no hard rule in the model of a sequence from surface to deep to transfer. In some ways, teaching the strategies of knowing what success looks like upfront implies an exposure to both surface and deep learning. Also, the many arguments (but surprisingly there is a lack of evidence) for the popular notions of flipped classrooms could be supported with more evidence of introducing the success criteria upfront to students. A typical flipped lesson starts with students accessing online video lectures or resources prior to in-class sessions so that students are prepared to participate in more interactive and higher-order activities such as problem solving, discussions and debates. 95 The most needed research concerns transfer—the variation theory of Marton, 48 the claims by Perkins 78 and others need more focused attention and the usual (and often unsubstantiated) claims that doing x will assist learning y should come back as a focus of learning sciences.

We are proposing that it is worthwhile to develop the skill, will and thrill of learning, and that there are many powerful strategies for learning. Students can be taught these strategies (declarative knowledge), how to use them (procedural knowledge), under what conditions it may be more or less useful to apply them (conditional knowledge) and how to evaluate them. It may be necessary to teach when best to use these strategies according the nature of the outcomes (surface and deep), according to the timing of learning (first acquiring and then consolidating learning) and to teach the skill of transferring learning to new situations. We need to think in terms of ‘surface to deep’ and not one alone; we need to think in terms of developing dispositions, motivations and achievement, and not one alone. This invites considering multiple outcomes from our schools. Singapore, 96 for example, is now committed to developing an educational system which will produce young people who have the moral courage to stand up for what is right; pursue a healthy lifestyle and have an appreciation of aesthetics; are proud to be Singaporeans; are resilient in the face of difficulty, innovative and enterprising; are purposeful in the pursuit of excellence; are able to collaborate across cultures; and can think critically and communicate persuasively. Academic achievement is but one desirable learning outcomes of many.

Another important message is that developing a few learning strategies may not be optimal. The failure to change strategies in new situations has been described as the tyranny of success; 84 and the current meta-synthesis suggests that choosing different strategies as one progresses through the learning cycle (from first exposure to embedding, from surface to deep to transfer) demands cognitive flexibility. It may not be the best option for students to use the same strategies that worked last time, as when the context is changed the old strategies may no longer work.

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Acknowledgements

The Science of Learning Research Centre is a Special Research Initiative of the Australian Research Council. Project Number SR120300015. We thank the following for critiquing earlier drafts of this article: Dan Willingham, Jason Lodge, Debra Masters, Rob Hester, Jared Horvath and Luke Rowe.

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learning about problem solving refers to learning different strategies

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7 Thinking, Language, and Problem Solving

Three different artistic portrayals of a person in thought are shown. From left to right, a painting of a woman with an open book, a sculpture of a man hunched over, head on chin, and a ink painting of a man sitting cross-legged holding his head.

What is the best way to solve a problem? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these and are called cognitive psychologists.

In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.

Table of Contents

7.1 What is Cognition? 7.2 Language 7.3 Problem Solving

7.1 What is Cognition?

Learning Objectives

By the end of this section, you will be able to:

  • Describe cognition
  • Distinguish concepts and prototypes
  • Explain the difference between natural and artificial concepts
  • Describe how schemata are organized and constructed

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition . Simply put,  cognition  is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.

Cognitive psychology  is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.

Concepts and Prototypes

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the mind synthesizes information from emotions and memories ( Figure 7.2 ). Emotion and memory are powerful influences on both our thoughts and behaviors.

A flow chart is overlaid on a drawing of a head with a ponytail. The flowchart reads: Information, sensations (arrow) emotions, memories (arrow) thoughts (arrow) behavior. Thoughts is also connected to Emotions, memories via a feedback arrow.

Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A  prototype  is the best example or representation of a concept. For example, what comes to your mind when you think of a dog? Most likely your early experiences with dogs will shape what you imagine. If your first pet was a Golden Retriever, there is a good chance that this would be your prototype for the category of dogs.

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts  are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations, experiences with snow, or indirect knowledge (such as from films or books) ( Figure 7.3 ).

Two images labeled a and b. A depicts a snowy field on a sunny day. B depicts a sphere, rectangular prism, and triangular prism.

An  artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A  schema (plural: schemata)  is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A  role schema  makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An  event schema , also known as a  cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure 7.4 ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

A crowded elevator.

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure 7.5 ).

A hand holds a cellphone in front of a steering wheel and front-shield window of a car. The car is on a road.

Remember the elevator? It feels almost impossible to walk in and  not  face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

7.2 LAnguage

  • Define language and demonstrate familiarity with the components of language
  • Understand the development of language
  • Explain the relationship between language and thinking

Language  is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.

Components of Language

Language, be it spoken, signed, or written, has specific components: a lexicon and lexicon grammar .  Lexicon  refers to the words of a given language. Thus, lexicon is a language’s vocabulary.  Grammar  refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.

Words are formed by combining the various phonemes that make up the language. A  phoneme  (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. For example, the phoneme English speakers associate with the letter ‘L’ is not used in the Japanese language. Similarly, many Southern African languages use phonemes, sometimes referred to as ‘click consonants’ that are not used in English.

Phonemes are combined to form  morphemes , which are the smallest units of language that convey some type of meaning. Some words are morphemes, but not all morphemes are words.  For example, “-ed” is a morpheme used to convey the past-tense in English, but it is not a word. The word “review” contains two morphemes: re- (meaning to do something again) and view (to see). Finally, some words like “I” and “a” are both a phonemes and morphemes.

We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar.  Semantics  refers to the process by which we derive meaning from morphemes and words by connecting those morphemes and words to stored concepts.  Syntax  refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011). For example, you would never say “the dog walked I today” to let someone know you took your dog for a walk–that sentence does not obey English syntax and is therefore difficult to make sense of.

We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. Through our use of words and language, we are able to form, organize, and express ideas, schema, and artificial concepts.

Language Development

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F.  Skinner  (1957) proposed that language is learned through reinforcement. Noam  Chomsky  (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age ( Table 7.1 ). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

DIG DEEPER: The Case of Genie

In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.

Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.

You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).

Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.

As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of  overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).

Language and Thought

When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf, began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought. For example, in some languages there are many different words for love. However, in English we use the word love for all types of love. Does this affect how we think about love depending on the language that we speak (Whorf, 1956)? Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.

WHAT DO YOU THINK? The Meaning of Language

Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word  saudade  originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as  saudade  ( Figure 7.6 ) .  The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as  saudade  indicate that different languages produce different patterns of thought in people? What do you think??

Two paintings are depicted in a and b. A depicts a young boy leaning on a trunk. He looks forlornly past the viewer. B depicts a woman wrapped in a black shawl standing near a window. She reads a letter while holding the shawl to her mouth.

One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for  light  and one word for  dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)

7.3 Problem Solving

  • Describe problem solving strategies
  • Define algorithm and heuristic
  • Explain some common roadblocks to effective problem solving and decision making

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A  problem-solving strategy  is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is  trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An  algorithm  is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a  heuristic  is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

  • When one is faced with too much information
  • When the time to make a decision is limited
  • When the decision to be made is unimportant
  • When there is access to very little information to use in making the decision
  • When an appropriate heuristic happens to come to mind in the same moment

Working backwards  is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

EVERYDAY CONNECTION: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

A sudoku puzzle is pictured. The puzzle is a 4x4 square with each sub-square also divided into four. Inside the top left square, the numbers are 3, blank, blank, 4 from left-to-right and top-to-bottom. In the top right square, the numbers are blank, two, one, blank. In the bottom left square, the numbers are blank, 3, four, blank; and the bottom right square contains 2, blank, blank, 1.

Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Nine dots are arrayed in three rows of three.

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A  mental set  is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

The top figure shows a book of matches, a box of tacks, and a candle. The bottom figure shows the box tacked to the wall with the candle standing in the box.

Functional fixedness  is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to solve the problem ( Figure 7.10 ). During the  Apollo 13  mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An  anchoring bias  occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The  confirmation bias  is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis.  Hindsight bias  leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did.  Representative bias  describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the  availability heuristic  is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision .  Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in  Table 7.3 .

Were you able to determine how many marbles are needed to balance the scales in  Figure 7.9 ? You need nine. Were you able to solve the problems in  Figure 7.7  and  Figure 7.8 ? Here are the answers ( Figure 7.11 ).

image

Chapter Summary

7.1 what is cognition.

In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.

7.2 Language

Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

thinking; or, all of the processes associated with perception, knowledge, problem solving, judgement, language, and memory.

A modern school of psychological thought that empirically examines mental processes such as perception, memory, language, and judgement.

a category or grouping of linguistic information, images, ideas or memories, such as life experiences.

knowledge about words, concepts, and language-based knowledge and facts

the best example or representation of a concept, specific to an individual

concepts developed through direct or indirect experiences with the world

a concept defined by a specific set of characteristics.

a mental construct consisting of a cluster of related concepts

a set of ideas relating to how individuals in certain roles will behave.

also known as a cognitive script; a set of behaviors associated with a particular place or event

also known as an event schema; a set of behaviors associated with a particular place or event

a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another.

the words of a language

the rules of a language used to convey meaning through the use of the lexicon

the basic sounds that make up a language

the smallest unit of language that conveys meaning

the process by which we derive meaning from morphemes and words

the rules guiding the organization of morphemes into words and words into sentences.

Psychology 2e Copyright © 2020 by Openstax is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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  • v.31(3); 2021 Jun

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Effective Learning Behavior in Problem-Based Learning: a Scoping Review

Azril shahreez abdul ghani.

1 Department of Basic Medical Sciences, Kulliyah of Medicine, Bandar Indera Mahkota Campus, International Islamic University Malaysia, Kuantan, 25200 Pahang Malaysia

2 Department of Medical Education, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu, 16150 Kelantan Malaysia

Ahmad Fuad Abdul Rahim

Muhamad saiful bahri yusoff, siti nurma hanim hadie.

3 Department of Anatomy, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, 16150 Kota Bharu, Kelantan Malaysia

Problem-based learning (PBL) emphasizes learning behavior that leads to critical thinking, problem-solving, communication, and collaborative skills in preparing students for a professional medical career. However, learning behavior that develops these skills has not been systematically described. This review aimed to unearth the elements of effective learning behavior in a PBL context, using the protocol by Arksey and O’Malley. The protocol identified the research question, selected relevant studies, charted and collected data, and collated, summarized, and reported results. We discovered three categories of elements—intrinsic empowerment, entrustment, and functional skills—proven effective in the achievement of learning outcomes in PBL.

Introduction

Problem-based learning (PBL) is an educational approach that utilizes the principles of collaborative learning in small groups, first introduced by McMaster Medical University [ 1 ]. The shift of the higher education curriculum from traditional, lecture-based approaches to an integrated, student-centered approach was triggered by concern over the content-driven nature of medical knowledge with minimal clinical application [ 2 ]. The PBL pedagogy uses a systematic approach, starting with an authentic, real-life problem scenario as a context in which learning is not separated from practice as students collaborate and learn [ 3 ]. The tutor acts as a facilitator who guides the students’ learning, while students are required to solve the problems by discussing them with group members [ 4 ]. The essential aspect of the PBL process is the ability of the students to recognize their current knowledge, determine the gaps in their knowledge and experience, and acquire new knowledge to bridge the gaps [ 5 ]. PBL is a holistic approach that gives students an active role in their learning.

Since its inception, PBL has been used in many undergraduate and postgraduate degree programs, such as medicine [ 6 , 7 ], nursing [ 8 ], social work education [ 9 ], law [ 10 ], architecture [ 11 ], economics [ 12 ], business [ 13 ], science [ 14 ], and engineering [ 15 ]. It has also been applied in elementary and secondary education [ 16 – 18 ]. Despite its many applications, its implementation is based on a single universal workflow framework that contains three elements: problem as the initiator for learning, tutor as a facilitator in the group versions, and group work as a stimulus for collaborative interaction [ 19 ]. However, there are various versions of PBL workflow, such as the seven-step technique based on the Maastricht “seven jumps” process. The tutor’s role is to ensure the achievement of learning objectives and to assess students’ performance [ 20 , 21 ].

The PBL process revolves around four types of learning principles: constructive, self-directed, collaborative, and contextual [ 19 ]. Through the constructive learning process, the students are encouraged to think about what is already known and integrate their prior knowledge with their new understanding. This process helps the student understand the content, form a new opinion, and acquire new knowledge [ 22 ]. The PBL process encourages students to become self-directed learners who plan, monitor, and evaluate their own learning, enabling them to become lifelong learners [ 23 ]. The contextualized collaborative learning process also promotes interaction among students, who share similar responsibilities to achieve common goals relevant to the learning context [ 24 ]. By exchanging ideas and providing feedback during the learning session, the students can attain a greater understanding of the subject matter [ 25 ].

Dolmans et al. [ 19 ] pointed out two issues related to the implementation of PBL: dominant facilitators and dysfunctional PBL groups. These problems inhibit students’ self-directed learning and reduce their satisfaction level with the PBL session. A case study by Eryilmaz [ 26 ] that evaluated engineering students’ and tutors’ experience of PBL discovered that PBL increased the students’ self-confidence and improved essential skills such as problem-solving, communications, critical thinking, and collaboration. Although most of the participants in the study found PBL satisfactory, many complained about the tutor’s poor guidance and lack of preparation. Additionally, it was noted that 64% of the first-year students were unable to adapt to the PBL system because they had been accustomed to conventional learning settings and that 43% of students were not adequately prepared for the sessions and thus were minimally involved in the discussion.

In a case study by Cónsul-giribet [ 27 ], newly graduated nursing professionals reported a lack of perceived theoretical basic science knowledge at the end of their program, despite learning through PBL. The nurses perceived that this lack of knowledge might affect their expertise, identity, and professional image.

Likewise, a study by McKendree [ 28 ] reported the outcomes of a workshop that explored the strengths and weaknesses of PBL in an allied health sciences curriculum in the UK. The workshop found that problems related to PBL were mainly caused by students, the majority of whom came from conventional educational backgrounds either during high school or their first degree. They felt anxious when they were involved in PBL, concerned about “not knowing when to stop” in exploring the learning needs. Apart from a lack of basic science knowledge, the knowledge acquired during PBL sessions remains unorganized [ 29 ]. Hence, tutors must guide students in overcoming this situation by instilling appropriate insights and essential skills for the achievement of the learning outcomes [ 30 ]. It was also evident that the combination of intention and motivation to learn and desirable learning behavior determined the quality of learning outcomes [ 31 , 32 ]. However, effective learning behaviors that help develop these skills have not been systematically described. Thus, this scoping review aimed to unearth the elements of effective learning behavior in the PBL context.

Scoping Review Protocol

This scoping review was performed using a protocol by Arksey and O’Malley [ 33 ]. The protocol comprises five phases: (i) identification of research questions, (ii) identification of relevant articles, (iii) selection of relevant studies, (iv) data collection and charting, and (v) collating, summarizing, and reporting the results.

Identification of Research Questions

This scoping review was designed to unearth the elements of effective learning behavior that can be generated from learning through PBL instruction. The review aimed to answer one research question: “What are the effective learning behavior elements related to PBL?” For the purpose of the review, an operational definition of effective learning behavior was constructed, whereby it was defined as any learning behavior that is related to PBL instruction and has been shown to successfully attain the desired learning outcomes (i.e., cognitive, skill, or affective)—either quantitatively or qualitatively—in any intervention conducted in higher education institutions.

The positive outcome variables include student viewpoint or perception, student learning experience and performance, lecturer viewpoint and expert judgment, and other indirect variables that may be important indicators of successful PBL learning (i.e., attendance to PBL session, participation in PBL activity, number of interactions in PBL activity, and improvement in communication skills in PBL).

Identification of Relevant Articles

An extensive literature search was conducted on articles published in English between 2015 and 2019. Three databases—Google Scholar, Scopus, and PubMed—were used for the literature search. Seven search terms with the Boolean combination were used, whereby the keywords were identified from the Medical Subject Headings (MeSH) and Education Resources Information Center (ERIC) databases. The search terms were tested and refined with multiple test searches. The final search terms with the Boolean operation were as follows: “problem-based learning” AND (“learning behavior” OR “learning behaviour”) AND (student OR “medical students” OR undergraduate OR “medical education”).

Selection of Relevant Articles

The articles from the three databases were exported manually into Microsoft Excel. The duplicates were removed, and the remaining articles were reviewed based on the inclusion and exclusion criteria. These criteria were tested on titles and abstracts to ensure their robustness in capturing the articles related to learning behavior in PBL. The shortlisted articles were reviewed by two independent researchers, and a consensus was reached either to accept or reject each article based on the set criteria. When a disagreement occurred between the two reviewers, the particular article was re-evaluated independently by the third and fourth researchers (M.S.B.Y and A.F.A.R), who have vast experience in conducting qualitative research. The sets of criteria for selecting abstracts and final articles were developed. The inclusion and exclusion criteria are listed in Table ​ Table1 1 .

Inclusion and exclusion criteria

Data Charting

The selected final articles were reviewed, and several important data were extracted to provide an objective summary of the review. The extracted data were charted in a table, including the (i) title of the article, (ii) author(s), (iii) year of publication, (iv) aim or purpose of the study, (v) study design and method, (iv) intervention performed, and (v) study population and sample size.

Collating, Summarizing, and Reporting the Results

A content analysis was performed to identify the elements of effective learning behaviors in the literature by A.S.A.G and S.N.H.H, who have experience in conducting qualitative studies. The initial step of content analysis was to read the selected articles thoroughly to gain a general understanding of the articles and extract the elements of learning behavior which are available in the articles. Next, the elements of learning behavior that fulfil the inclusion criteria were extracted. The selected elements that were related to each other through their content or context were grouped into subtheme categories. Subsequently, the combinations of several subthemes expressing similar underlying meanings were grouped into themes. Each of the themes and subthemes was given a name, which was operationally defined based on the underlying elements. The selected themes and subthemes were presented to the independent researchers in the team (M.S.B.Y and A.F.A.R), and a consensus was reached either to accept or reformulate each of the themes and subthemes. The flow of the scoping review methods for this study is illustrated in Fig.  1 .

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The flow of literature search and article selection

Literature Search

Based on the keyword search, 1750 articles were obtained. Duplicate articles that were not original articles found in different databases and resources were removed. Based on the inclusion and exclusion criteria of title selection, the eligibility of 1750 abstracts was evaluated. The articles that did not fulfil the criteria were removed, leaving 328 articles for abstract screening. A total of 284 articles were screened according to the eligibility criteria for abstract selection. Based on these criteria, 284 articles were selected and screened according to the eligibility criteria for full article selection. Fourteen articles were selected for the final review. The information about these articles is summarized in Table ​ Table2 2 .

Studies characteristics

Study Characteristics

The final 14 articles were published between 2015 and 2019. The majority of the studies were conducted in Western Asian countries ( n  = 4), followed by China ( n  = 3), European countries ( n  = 2), Thailand ( n  = 2), Indonesia ( n  = 1), Singapore ( n  = 1), and South Africa ( n  = 1). Apart from traditional PBL, some studies incorporated other pedagogic modalities into their PBL sessions, such as online learning, blended learning, and gamification. The majority of the studies targeted a single-profession learner group, and one study was performed on mixed interprofessional health education learners.

Results of Thematic Analysis

The thematic analysis yielded three main themes of effective learning behavior: intrinsic empowerment, entrustment, and functional skills. Intrinsic empowerment overlies four proposed subthemes: proactivity, organization, diligence, and resourcefulness. For entrustment, there were four underlying subthemes: students as assessors, students as teachers, feedback-giving, and feedback-receiving. The functional skills theme contains four subthemes: time management, digital proficiency, data management, and collaboration.

Theme 1: Intrinsic Empowerment

Intrinsic empowerment enforces student learning behavior that can facilitate the achievement of learning outcomes. By empowering the development of these behaviors, students can become lifelong learners [ 34 ]. The first element of intrinsic empowerment is proactive behavior. In PBL, the students must be proactive in analyzing problems [ 35 , 36 ] and their learning needs [ 35 , 37 ], and this can be done by integrating prior knowledge and previous experience through a brainstorming session [ 35 , 38 ]. The students must be proactive in seeking guidance to ensure they stay focused and confident [ 39 , 40 ]. Finding ways to integrate content from different disciplines [ 35 , 41 ], formulate new explanations based on known facts [ 34 , 35 , 41 ], and incorporate hands-on activity [ 35 , 39 , 42 ] during a PBL session are also proactive behaviors.

The second element identified is “being organized” which reflects the ability of students to systematically manage their roles [ 43 ], ideas, and learning needs [ 34 ]. The students also need to understand the task for each learning role in PBL, such as chairperson or leader, scribe, recorder, and reflector. This role needs to be assigned appropriately to ensure that all members take part in the discussion [ 43 ]. Similarly, when discussing ideas or learning needs, the students need to follow the steps in the PBL process and organize and prioritize the information to ensure that the issues are discussed systematically and all aspects of the problems are covered accordingly [ 34 , 37 ]. This team organization and systematic thought process is an effective way for students to focus, plan, and finalize their learning tasks.

The third element of intrinsic empowerment is “being diligent.” Students must consistently conduct self-revision [ 40 ] and keep track of their learning plan to ensure the achievement of their learning goal [ 4 , 40 ]. The students must also be responsible for completing any given task and ensuring good understanding prior to their presentation [ 40 ]. Appropriate actions need to be undertaken to find solutions to unsolved problems [ 40 , 44 ]. This effort will help them think critically and apply their knowledge for problem-solving.

The fourth element identified is “being resourceful.” Students should be able to acquire knowledge from different resources, which include external resources (i.e., lecture notes, textbooks, journal articles, audiovisual instructions, the Internet) [ 38 , 40 , 45 ] and internal resources (i.e., students’ prior knowledge or experience) [ 35 , 39 ]. The resources must be evidence-based, and thus should be carefully selected by evaluating their cross-references and appraising them critically [ 37 ]. Students should also be able to understand and summarize the learned materials and explain them using their own words [ 4 , 34 ]. The subthemes of the intrinsic empowerment theme are summarized in Table ​ Table3 3 .

 Intrinsic empowerment subtheme with the learning behavior elements

Theme 2: Entrustment

Entrustment emphasizes the various roles of students in PBL that can promote effective learning. The first entrusted role identified is “student as an assessor.” This means that students evaluate their own performance in PBL [ 46 ]. The evaluation of their own performance must be based on the achievement of the learning outcomes and reflect actual understanding of the content as well as the ability to apply the learned information in problem-solving [ 46 ].

The second element identified in this review is “student as a teacher.” To ensure successful peer teaching in PBL, students need to comprehensively understand the content of the learning materials and summarize the content in an organized manner. The students should be able to explain the gist of the discussed information using their own words [ 4 , 34 ] and utilize teaching methods to cater to differences in learning styles (i.e., visual, auditory, and kinesthetic) [ 41 ]. These strategies help capture their group members’ attention and evoke interactive discussions among them.

The third element of entrustment is to “give feedback.” Students should try giving constructive feedback on individual and group performance in PBL. Feedback on individual performance must reflect the quality of the content and task presented in the PBL. Feedback on group performance should reflect the ways in which the group members communicate and complete the group task [ 47 ]. To ensure continuous constructive feedback, students should be able to generate feedback questions beforehand and immediately deliver them during the PBL sessions [ 44 , 47 ]. In addition, the feedback must include specific measures for improvement to help their peers to take appropriate action for the future [ 47 ].

The fourth element of entrustment is “receive feedback.” Students should listen carefully to the feedback given and ask questions to clarify the feedback [ 47 ]. They need to be attentive and learn to deal with negative feedback [ 47 ]. Also, if the student does not receive feedback, they should request it either from peers or teachers and ask specific questions, such as what aspects to improve and how to improve [ 47 ]. The data on the subthemes of the entrustment theme are summarized in Table ​ Table4 4 .

Entrustment subtheme with the learning behavior elements

Theme 3: Functional Skills

Functional skills refer to essential skills that can help students learn independently and competently. The first element identified is time management skills. In PBL, students must know how to prioritize learning tasks according to the needs and urgency of the tasks [ 40 ]. To ensure that students can self-pace their learning, a deadline should be set for each learning task within a manageable and achievable learning schedule [ 40 ].

Furthermore, students should have digital proficiency, the ability to utilize digital devices to support learning [ 38 , 40 , 44 ]. The student needs to know how to operate basic software (e.g., Words and PowerPoints) and the basic digital tools (i.e., social media, cloud storage, simulation, and online community learning platforms) to support their learning [ 39 , 40 ]. These skills are important for peer learning activities, which may require information sharing, information retrieval, online peer discussion, and online peer feedback [ 38 , 44 ].

The third functional skill identified is data management, the ability to collect key information in the PBL trigger and analyze that information to support the solution in a problem-solving activity [ 39 ]. Students need to work either individually or in a group to collect the key information from a different trigger or case format such as text lines, an interview, an investigation, or statistical results [ 39 ]. Subsequently, students also need to analyze the information and draw conclusions based on their analysis [ 39 ].

The fourth element of functional skill is collaboration. Students need to participate equally in the PBL discussion [ 41 , 46 ]. Through discussion, confusion and queries can be addressed and resolved by listening, respecting others’ viewpoints, and responding professionally [ 35 , 39 , 43 , 44 ]. In addition, the students need to learn from each other and reflect on their performance [ 48 ]. Table ​ Table5 5 summarizes the data on the subthemes of the functional skills theme.

Functional skills subtheme with the learning behavior elements

This scoping review outlines three themes of effective learning behavior elements in the PBL context: intrinsic empowerment, entrustment, and functional skills. Hence, it is evident from this review that successful PBL instruction demands students’ commitment to empower themselves with value-driven behaviors, skills, and roles.

In this review, intrinsic empowerment is viewed as enforcement of students’ internal strength in performing positive learning behaviors related to PBL. This theme requires the student to proactively engage in the learning process, organize their learning activities systematically, persevere in learning, and be intelligently resourceful. One of the elements of intrinsic empowerment is the identification and analysis of problems related to complex scenarios. This element is aligned with a study by Meyer [ 49 ], who observed students’ engagement in problem identification and clarification prior to problem-solving activities in a PBL session related to multiple engineering design. Rubenstein and colleagues [ 50 ] discovered in a semi-structured interview the importance of undergoing a problem identification process before proposing a solution during learning. It was reported that the problem identification process in PBL may enhance the attainment of learning outcomes, specifically in the domain of concept understanding [ 51 ].

The ability of the students to acquire and manage learning resources is essential for building their understanding of the learned materials and enriching discussion among team members during PBL. This is aligned with a study by Jeong and Hmelo-Silver [ 52 ], who studied the use of learning resources by students in PBL. The study concluded that in a resource-rich environment, the students need to learn how to access and understand the resources to ensure effective learning. Secondly, they need to process the content of the resources, integrate various resources, and apply them in problem-solving activities. Finally, they need to use the resources in collaborative learning activities, such as sharing and relating to peer resources.

Wong [ 53 ] documented that excellent students spent considerably more time managing academic resources than low achievers. The ability of the student to identify and utilize their internal learning resources, such as prior knowledge and experience, is also important. A study by Lee et al. [ 54 ] has shown that participants with high domain-specific prior knowledge displayed a more systematic approach and high accuracy in visual and motor reactions in solving problems compared to novice learners.

During the discussion phase in PBL, organizing ideas—e.g., arranging relevant information gathered from the learning resources into relevant categories—is essential for communicating the idea clearly [ 34 ]. This finding is in line with a typology study conducted by Larue [ 55 ] on second-year nursing students’ learning strategies during a group discussion. The study discovered that although the content presented by the student is adequate, they unable to make further progress in the group discussion until they are instructed by the tutor on how to organize the information given into a category [ 55 ].

Hence, the empowerment of student intrinsic behavior may enhance students’ learning in PBL by allowing them to make a decision in their learning objectives and instilling confidence in them to achieve goals. A study conducted by Kirk et al. [ 56 ] proved that highly empowered students obtain better grades, increase learning participation, and target higher educational aspirations.

Entrustment is the learning role given to students to be engaging and identify gaps in their learning. This theme requires the student to engage in self-assessment, prepare to teach others, give constructive feedback, and value the feedback received. One of the elements of entrustment is the ability to self-assess. In a study conducted by Mohd et al. [ 57 ] looking at the factors in PBL that can strengthen the capability of IT students, they discovered that one of the critical factors that contribute to these skills is the ability of the student to perform self-assessment in PBL. As mentioned by Daud, Kassim, and Daud [ 58 ], the self-assessment may be more reliable if the assessment is performed based on the objectives set beforehand and if the criteria of the assessment are understood by the learner. This is important to avoid the fact that the result of the self-assessment is influenced by the students’ perception of themselves rather than reflecting their true performance. However, having an assessment based on the learning objective only focuses on the immediate learning requirements in the PBL. To foster lifelong learning skills, it should also be balanced with the long-term focus of assessment, such as utilizing the assessment to foster the application of knowledge in solving real-life situations. This is aligned with the review by Boud and Falchikov [ 59 ] suggesting that students need to become assessors within the concept of participation in practice, that is, the kind that is within the context of real life and work.

The second subtheme of entrustment is “students as a teacher” in PBL. In our review, the student needs to be well prepared with the teaching materials. A cross-sectional study conducted by Charoensakulchai and colleagues discovered that student preparation is considered among the important factors in PBL success, alongside other factors such as “objective and contents,” “student assessment,” and “attitude towards group work” [ 60 ]. This is also aligned with a study conducted by Sukrajh [ 61 ] using focus group discussion on fifth-year medical students to explore their perception of preparedness before conducting peer teaching activity. In this study, the student in the focus group expressed that the preparation made them more confident in teaching others because preparing stimulated them to activate and revise prior knowledge, discover their knowledge gaps, construct new knowledge, reflect on their learning, improve their memory, inspire them to search several resources, and motivate them to learn the topics.

The next element of “student as a teacher” is using various learning styles to teach other members in the group. A study conducted by Almomani [ 62 ] showed that the most preferred learning pattern by the high school student is the visual pattern, followed by auditory pattern and then kinesthetic. However, in the university setting, Hamdani [ 63 ] discovered that students prefer a combination of the three learning styles. Anbarasi [ 64 ] also explained that incorporating teaching methods based on the student’s preferred learning style further promotes active learning among the students and significantly improved the long-term retrieval of knowledge. However, among the three learning styles group, he discovered that the kinesthetic group with the kinesthetic teaching method showed a significantly higher post-test score compared to the traditional group with the didactic teaching method, and he concluded that this is because of the involvement of more active learning activity in the kinesthetic group.

The ability of students to give constructive feedback on individual tasks is an important element in promoting student contribution in PBL because feedback from peers or teachers is needed to reassure themselves that they are on the right track in the learning process. Kamp et al. [ 65 ] performed a study on the effectiveness of midterm peer feedback on student individual cognitive, collaborative, and motivational contributions in PBL. The experimental group that received midterm peer feedback combined with goal-setting with face-to-face discussion showed an increased amount of individual contributions in PBL. Another element of effective feedback is that the feedback is given immediately after the observed behavior. Parikh and colleagues survey student feedback in PBL environments among 103 final-year medical students in five Ontario schools, including the University of Toronto, McMaster University, Queens University, the University of Ottawa, and the University of Western Ontario. They discovered that there was a dramatic difference between McMaster University and other universities in the immediacy of feedback they practiced. Seventy percent of students at McMaster reported receiving immediate feedback in PBL, compared to less than 40 percent of students from the other universities, in which most of them received feedback within one week or several weeks after the PBL had been conducted [ 66 ]. Another study, conducted among students of the International Medical University of Kuala Lumpur examining the student expectation on feedback, discovered that immediate feedback is effective if the feedback is in written form, simple but focused on the area of improvement, and delivered by a content expert. If the feedback is delivered by a content non-expert and using a model answer, it must be supplemented with teacher dialogue sessions to clarify the feedback received [ 67 ].

Requesting feedback from peers and teachers is an important element of the PBL learning environment, enabling students to discover their learning gaps and ways to fill them. This is aligned with a study conducted by de Jong and colleagues [ 68 ], who discovered that high-performing students are more motivated to seek feedback than low-performing students. The main reason for this is because high-performing students seek feedback as a tool to learn from, whereas low-performing students do so as an academic requirement. This resulted in high-performing students collecting more feedback. A study by Bose and Gijselaers [ 69 ] examined the factors that promote feedback-seeking behavior in medical residency. They discovered that feedback-seeking behavior can be promoted by providing residents with high-quality feedback to motivate them to ask for feedback for improvement.

By assigning an active role to students as teachers, assessors, and feedback providers, teachers give them the ownership and responsibility to craft their learning. The learner will then learn the skills to monitor and reflect on their learning to achieve academic success. Furthermore, an active role encourages students to be evaluative experts in their own learning, and promoting deep learning [ 70 ].

Functional skills refer to essential abilities for competently performing a task in PBL. This theme requires the student to organize and plan time for specific learning tasks, be digitally literate, use data effectively to support problem-solving, and work together efficiently to achieve agreed objectives. One of the elements in this theme is to have a schedule of learning tasks with deadlines. In a study conducted by Tadjer and colleagues [ 71 ], they discovered that setting deadlines with a restricted time period in a group activity improved students’ cognitive abilities and soft skills. Although the deadline may initially cause anxiety, coping with it encourages students to become more creative and energetic in performing various learning strategies [ 72 , 73 ]. Ballard et al. [ 74 ] reported that students tend to work harder to complete learning tasks if they face multiple deadlines.

The students also need to be digitally literate—i.e., able to demonstrate the use of technological devices and tools in PBL. Taradi et al. [ 75 ] discovered that incorporating technology in learning—blending web technology with PBL—removes time and place barriers in the creation of a collaborative environment. It was found that students who participated in web discussions achieved a significantly higher mean grade on a physiology final examination than those who used traditional methods. Also, the incorporation of an online platform in PBL can facilitate students to develop investigation and inquiry skills with high-level cognitive thought processes, which is crucial to successful problem-solving [ 76 ].

In PBL, students need to work collaboratively with their peers to solve problems. A study by Hidayati et al. [ 77 ] demonstrated that effective collaborative skills improve cognitive learning outcomes and problem-solving ability among students who undergo PBL integrated with digital mind maps. To ensure successful collaborative learning in PBL, professional communication among students is pertinent. Research by Zheng and Huang [ 78 ] has proven that co-regulation (i.e., warm and responsive communication that provides support to peers) improved collaborative effort and group performance among undergraduate and master’s students majoring in education and psychology. This is also in line with a study by Maraj and colleagues [ 79 ], which showed the strong team interaction within the PBL group leads to a high level of team efficacy and academic self-efficacy. Moreover, strengthening communication competence, such as by developing negotiation skills among partners during discussion sessions, improves student scores [ 80 ].

PBL also includes opportunities for students to learn from each other (i.e., peer learning). A study by Maraj et al. [ 79 ] discovered that the majority of the students in their study perceived improvement in their understanding of the learned subject when they learned from each other. Another study by Lyonga [ 81 ] documented the successful formation of cohesive group learning, where students could express and share their ideas with their friends and help each other. It was suggested that each student should be paired with a more knowledgeable student who has mastered certain learning components to promote purposeful structured learning within the group.

From this scoping review, it is clear that functional skills equip the students with abilities and knowledge needed for successful PBL. Studies have shown that strong time management skills, digital literacy, data management, and collaborative skills lead to positive academic achievement [ 77 , 82 , 83 ].

Limitation of the Study

This scoping review is aimed to capture the recent effective learning behavior in problem-based learning; therefore, the literature before 2015 was not included. Without denying the importance of publication before 2015, we are relying on Okoli and Schabram [ 84 ] who highlighted the impossibility of retrieving all the published articles when conducting a literature search. Based on this ground, we decided to focus on the time frame between 2015 and 2019, which is aligned with the concepts of study maturity (i.e., the more mature the field, the higher the published articles and therefore more topics were investigated) by Kraus et al. [ 85 ]. In fact, it was noted that within this time frame, a significant number of articles have been found as relevant to PBL with the recent discovery of effective learning behavior. Nevertheless, our time frame did not include the timing of the coronavirus disease 19 (COVID-19) pandemic outbreak, which began at the end of 2019. Hence, we might miss some important elements of learning behavior that are required for the successful implementation of PBL during the COVID-19 pandemic.

Surprisingly, the results obtained from this study are also applicable for the PBL sessions administration during the COVID-19 pandemic situation as one of the functional skills identified is digital proficiency. This skill is indeed important for the successful implementation of online PBL session.

This review identified the essential learning behaviors required for effective PBL in higher education and clustered them into three main themes: (i) intrinsic empowerment, (ii) entrustment, and (iii) functional skills. These learning behaviors must coexist to ensure the achievement of desired learning outcomes. In fact, the findings of this study indicated two important implications for future practice. Firstly, the identified learning behaviors can be incorporated as functional elements in the PBL framework and implementation. Secondly, the learning behaviors change and adaption can be considered to be a new domain of formative assessment related to PBL. It is noteworthy to highlight that these learning behaviors could help in fostering the development of lifelong skills for future workplace challenges. Nevertheless, considerably more work should be carried out to design a solid guideline on how to systematically adopt the learning behaviors in PBL sessions, especially during this COVID-19 pandemic situation.

This study was supported by Postgraduate Incentive Grant-PhD (GIPS-PhD, grant number: 311/PPSP/4404803).

Declarations

The study has received an ethical approval from the Human Research Ethics Committee of Universiti Sains Malaysia.

No informed consent required for the scoping review.

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

  • Cognitive Learning
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  • Learning Theories

This guide gives you a clear understanding of how cognitive learning can positively impact your life in a corporate environment.

Training of cognitive learning to employees in organizations enhances and strengthens their expertise in handling more complex tasks.

What is Cognitive Learning?

Components of cognitive learning, cognitive learning theory, social cognitive theory, cognitive behavioral theory, benefits of cognitive learning, cognitive learning strategies, cognitive learning examples.

Cognitive learning is an active style of learning that focuses on helping you learn how to maximize your brain’s potential. It makes it easier for you to connect new information with existing ideas hence deepening your memory and retention capacity.

The ability of the brain’s mental processes to absorb and retain information through experience, senses, and thought is known as cognition .

Employers need to expose employees to training on cognitive learning—an organization whose employees have strong cognitive skills is likely successful.

Well-trained and fully engaged employees are capable of learning quickly and being highly productive by handling multiple complex tasks without the necessity of a supervisor.

There is a young branch of psychology known as cognitive psychology. It is the study of one’s internal processes.

These are the things going on in your brain, such as thinking, attention, learning, problem-solving, perception, among others.

Traditional learning mainly focuses on memorization instead of trying to achieve mastery in a particular subject.

The following are fundamental aspects of cognitive learning:

1. Comprehension

For cognitive learning to be efficient and benefit you, understand the reason why you are learning a specific subject in the first place.

Cognitive learning discourages cramming of information, which is very ineffective in education. Having a deep understanding of a subject improves your ability to relate new knowledge with previous experiences or information.

3. Application

Cognitive learning strategies help you apply new information or skills in life situations. They encourage you as you continue to develop problem-solving skills.

Cognitive learning loop, fundamental aspects: Comprehension, Memory, Application.

1. Enhances learning

Cognitive learning theory enhances lifelong learning. Workers can build upon previous ideas and apply new concepts to already existing knowledge.

2. Boosts confidence

Employees become more confident in approaching tasks as they get a deeper understanding of new topics and learn new skills.

3. Enhances Comprehension

Cognitive learning improves learners’ comprehension of acquiring new information. They can develop a deeper understanding of new learning materials.

4. Improves problem-solving skills

Cognitive learning equips employees with the skills they need to learn effectively. They are thereby able to develop problem-solving skills they can apply under challenging tasks.

5. Help learn new things faster

Through the experience of learning, the employee will be able to recycle and use the same learning methods that worked previously. This will help them learn new things a lot faster as they already know what works for them when it comes to obtaining new knowledge.

6. Teaches to form concept formation (think abstract)

Cognitive learning can also teach your employees to form a range of different concepts such as easily perceiving and interpreting information that could boost creativity and lead to innovations at the workplace.

Several psychologists have shaped the concept of cognitive learning through research. They came up with theories and learning strategies that can be implemented in a corporate learning environment.

Learner-centered strategy

Jean Piaget termed learning as relating information to already existing knowledge. And each learner starts with their own knowledge and experience.

According to his theories, learning begins with the accumulation of some basic knowledge and advancing deeper into the field with time.

Piaget suggested three vital components of learning:

  • Accommodation – taking new information into account by modifying what we already know.
  • Assimilation – the arrangement of new knowledge inside our heads beside what we know.
  • Equilibration – balancing what we already know with the new information that we are trying to acquire.

Each company should develop their training programs with a personalized learning approach to make it engaging for their employees to achieve better results.

To achieve that L&D professionals should focus on the following points:

  • Develop and introduce their programs based on already existing knowledge.
  • Provide more analogies to connect new knowledge with already existing knowledge.
  • Divide learning materials into stages and maintain a logical flow of lessons taught.
  • Provide examples or practical tasks that show how new information or principles can connect with previous knowledge, or enhance it.
  • Encourage questions and comments from trainees.

Meaningful Experiences strategy

David Ausubel made a clear distinction between meaningful learning and rote learning.

According to him, material that was closely related to what the learner knew was meaningful and always turned out to be effective.

Learners with relevant background knowledge find it easier to add new information.

During the training of learners in an organization:

  • There should be an emphasis on the meaningfulness of each session to the task at hand.
  • Background information on new material is essential.
  • New information should be instilled in learners in a sequence to build on what is already understood.

Learning Through Discovery strategy

Jerome Bruner is a psychologist who built his theory on top of Piaget’s theory of cognitive development that was focusing on learning through discovery.

His theory identified three stages of cognitive representation which are enactive, iconic, and symbolic. Enactive defining the representation of knowledge through actions, iconic being the visual summarization of images, and symbolic which is the use of words and symbols to describe experiences.

Through his study of cognitive learning in children, he suggested that they should be allowed to discover information for themselves. He believed that learners review previously learned material even as they gain new knowledge.

His interpretation of Cognitive Learning Theory in a corporate environment can be put by:

  • Allow employees to learn new skills and get new knowledge through new tasks and challenges.
  • Challenge trainees to solve real-world problems your organization faces.

Personalized learning strategy

All of these strategies can be combined into one personalized learning approach. Each learner is unique and has their own experience, knowledge, and perception. Which can greatly influence the way they interpret and consume new information.

Creating learning experiences that fit each individual based on their own knowledge that is meaningful for their role which encourages them to discover new solutions can drive great results and improve their overall performance.

L&D professionals should try to organize a learning environment, to allow employees to learn at their own pace, and with a variety of learning opportunities.

A common practice in recent years to create personalized learning is the use of modern technologies: AI recommendations, learning paths, machine learning, natural language processing.

For example, a digital learning assistant has the capability to recognize what the skills of the employees are, what they have learned so far, and automatically suggest to them what they should learn next. The reason why such modern technology is essential in employee development, because it can offer information they need without them even anticipating the need for it. Overall, it’s a fantastic tool that can ensure better employee education and eventually drive greater performance.

Moreover, the company can save a significant amount of resources with a learning assistant as it can make your organization’s learning environment flawless even if scalability is required.

Furthermore, having the ultimate access to a range of company resources, no two employees need to learn or go through the same learning process as they can cherry-pick what they want to learn from.

Now you have a clear idea of what cognitive learning means. The following are various examples of cognitive learning.

1. Explicit Learning

It happens when you intentionally seek knowledge to attempt and learn a new skill or process that may be vital to your work. It requires you to be attentive and take action to acquire knowledge.

An example of explicit learning would be undertaking an in-depth video editing course to understand the functionality of the software in order to be able to use it appropriately for the needs of your work.

2. Implicit Learning

Sometimes you passively gain new knowledge and learn some new skills. It is known as implicit learning , where you are unaware of the entire process until you realize you have retained something new.

This type of learning may occur when you are working, talking, or going about your normal life.

Typing fast and without looking at your keyboard is one good example of implicit learning that comes automatically over time.

3. Meaningful Learning

Meaningful learning is when you are capable of acquiring new information and relating it to past experiences.

This is because this cognitive learning approach teaches employees to build transferable problem-solving skills that can be applied in other areas.

An example of meaningful learning is when you work in procurement and decide to take an advanced course in your department to deepen your understanding of the subject.

4. Discovery Learning

It happens when you actively seek new knowledge by researching new concepts, processes, and subjects.

For example, if someone is set the task to proofread a particular report and they need to make use of a specific tool such as Grammarly, by using this tool in hand with the manuals, this would cause them to learn the features and abilities of the tool through discovery.

5. Receptive Learning

Lectures where you sit in groups and a speaker feeds the audience with information on a specific subject is an example of receptive learning. It requires the learner to be active by asking questions and taking down short notes.

During training in your workplace, this type of learning comes in handy where you get a deeper understanding of new information by being active and responsive to the speaker.

6. Non-Associative Learning (Habituation and Sensitization)

It is a type of learning that enables humans to adapt to something by facing it frequently.

When you get a new job at a factory where there are many machines making noise, it irritates for the first few days, but you later learn how to live with it. This is known as habituation.

Sensitization is the vice versa whereby your reaction towards something increases as you get frequent exposure towards it.

This type of learning happens in your typical situations in life and work. Working in an office teaches you to be more responsive to things like telephone calls.

7. Emotional Learning

Developing emotional intelligence is crucial to help us maintain friendly relationships with friends at work and in life.

Emotional learning helps people learn how to take charge of their emotions and also understand others’.

An employer requires to have control over their emotions so as to handle customers and also their superiors in a courteous manner.

8. Experiential Learning

Our experiences in life are our best lessons.

Your interactions with other people always teach you some precious life lessons. What you learn depends on how you interpret it.

For example, an intern learns by shadowing an experienced senior employer to gain experience. He acquires new skills that are relevant to his line of work.

9. Observation Learning

One of the significant components of the social cognitive theory is observational learning.

It is handy among employees since it mainly involves imitation of skills from colleagues and superiors.

Observing your friends or work colleagues is an efficient way to learn a new skill.

Your successful manager at work can help you improve your leadership qualities as you embrace and practice his habits.

10. Cooperative and Collaborative Learning

Working and learning in groups is encouraged in many institutions.

Cooperative learning helps bring out one’s best skills and deepens the collaboration between a group of people (read more about collaborative learning ).

However, for an individual to learn this way, he/she has to be an active and equal participant and interact with fellow group members.

Some companies select individuals to train on new strategies that improve the success of an organization. The trained employees are then encouraged to pass on this knowledge to their team members.

The types of cognitive learning above are vital in using your brain’s features as much as possible. They make it easier for you to acquire new skills and knowledge in life.

Develop and Maintain Learning Culture

In this workbook, we put together tips and exercises to help you develop your organisation’s learning culture.

learning about problem solving refers to learning different strategies

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7. Thinking and Intelligence

Problem solving, learning objectives.

By the end of this section, you will be able to:

  • Describe problem solving strategies
  • Define algorithm and heuristic
  • Explain some common roadblocks to effective problem solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

PROBLEM-SOLVING STRATEGIES

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( [link] ). For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

  • When one is faced with too much information
  • When the time to make a decision is limited
  • When the decision to be made is unimportant
  • When there is access to very little information to use in making the decision
  • When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( [link] ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

A four column by four row Sudoku puzzle is shown. The top left cell contains the number 3. The top right cell contains the number 2. The bottom right cell contains the number 1. The bottom left cell contains the number 4. The cell at the intersection of the second row and the second column contains the number 4. The cell to the right of that contains the number 1. The cell below the cell containing the number 1 contains the number 2. The cell to the left of the cell containing the number 2 contains the number 3.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

Here is another popular type of puzzle ( [link] ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

A square shaped outline contains three rows and three columns of dots with equal space between them.

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

Take a look at the “Puzzling Scales” logic puzzle below ( [link] ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

A puzzle involving a scale is shown. At the top of the figure it reads: “Sam Loyds Puzzling Scales.” The first row of the puzzle shows a balanced scale with 3 blocks and a top on the left and 12 marbles on the right. Below this row it reads: “Since the scales now balance.” The next row of the puzzle shows a balanced scale with just the top on the left, and 1 block and 8 marbles on the right. Below this row it reads: “And balance when arranged this way.” The third row shows an unbalanced scale with the top on the left side, which is much lower than the right side. The right side is empty. Below this row it reads: “Then how many marbles will it require to balance with that top?”

PITFALLS TO PROBLEM SOLVING

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Link to Learning

Check out this Apollo 13 scene where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in [link] .

Please visit this site to see a clever music video that a high school teacher made to explain these and other cognitive biases to his AP psychology students.

Were you able to determine how many marbles are needed to balance the scales in [link] ? You need nine. Were you able to solve the problems in [link] and [link] ? Here are the answers ( [link] ).

The first puzzle is a Sudoku grid of 16 squares (4 rows of 4 squares) is shown. Half of the numbers were supplied to start the puzzle and are colored blue, and half have been filled in as the puzzle’s solution and are colored red. The numbers in each row of the grid, left to right, are as follows. Row 1:  blue 3, red 1, red 4, blue 2. Row 2: red 2, blue 4, blue 1, red 3. Row 3: red 1, blue 3, blue 2, red 4. Row 4: blue 4, red 2, red 3, blue 1.The second puzzle consists of 9 dots arranged in 3 rows of 3 inside of a square. The solution, four straight lines made without lifting the pencil, is shown in a red line with arrows indicating the direction of movement. In order to solve the puzzle, the lines must extend beyond the borders of the box. The four connecting lines are drawn as follows. Line 1 begins at the top left dot, proceeds through the middle and right dots of the top row, and extends to the right beyond the border of the square. Line 2 extends from the end of line 1, through the right dot of the horizontally centered row, through the middle dot of the bottom row, and beyond the square’s border ending in the space beneath the left dot of the bottom row. Line 3 extends from the end of line 2 upwards through the left dots of the bottom, middle, and top rows. Line 4 extends from the end of line 3 through the middle dot in the middle row and ends at the right dot of the bottom row.

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

Self Check Questions

Critical thinking questions.

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question

3. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

1. Functional fixedness occurs when you cannot see a use for an object other than the use for which it was intended. For example, if you need something to hold up a tarp in the rain, but only have a pitchfork, you must overcome your expectation that a pitchfork can only be used for garden chores before you realize that you could stick it in the ground and drape the tarp on top of it to hold it up.

2. An algorithm is a proven formula for achieving a desired outcome. It saves time because if you follow it exactly, you will solve the problem without having to figure out how to solve the problem. It is a bit like not reinventing the wheel.

  • Psychology. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:1/Psychology . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/content/col11629/latest/.
  • Teaching Tips

17 Learning Strategies to Implement In Your Classroom

Learning strategies are a critical element in ensuring students grasp course concepts and are especially important in blended and online learning environments

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Danielle Leboff

17 Learning Strategies to Implement In Your Classroom

Learning strategies are methods used by instructors to initiate students into effective learning by using a variety of engaging learning techniques, activities and practices. These methods are all derived from years of meticulous research into how people learn best.

In any lesson plan, instructors can incorporate multiple learning strategies. By catering to different learning styles and varying your approach, you can better engage students while helping them master new concepts.

Top Hat’s 2021 Online Teaching Toolkit gives you easy-to-use teaching templates, active learning strategies and more to engage your students in an online or hybrid learning environment. Get free access today.

Why are learning strategies important?

Learning strategies are an essential component of creating an effective learning experience. They can help learners develop proficiency in various subject matter areas and develop new skill sets. They also help learners develop confidence in their own knowledge, proficiency and learning abilities.

The following describes some common strategies for achieving various learning outcomes, along with practical examples you can incorporate directly into your learning environment.

Think-pair-share

This active learning exercise is designed to activate any prior knowledge a student may have on a subject by having them share their thoughts and beliefs with their fellow learners.

A think-pair-share exercise is structured to help students first organize their thoughts, then share these with a partner followed by the broader class.

  • Think : Students take a moment to contemplate the new concept or idea on their own. They can also write down their thoughts to help develop their note-taking skills.
  • Pair : Students break off into pairs to share their thoughts and beliefs on the topic with another learner.
  • Share : Students then share their takeaways from this conversation with one or more successively larger groups, up to and including the whole class.

Putting think-pair-share into action

To execute think-pair-share in your class, define the exercise for the group and display the prompts you’d like to pose for discussion. Once students have completed the exercise, you can then facilitate a larger class discussion.

Make a point of listening to student responses before offering your own ideas. You can also pose probing questions while encouraging other students to offer their own responses and reactions to each other’s ideas.

Tests and quizzes

There are several ways instructors can use tests and quizzes as effective strategies for learning.

Individual plus group quizzes : Have learners complete independent quizzes for grading. Following this, place learners into small groups and give them the same quiz as a form of cooperative learning. This time, allow the groups to discuss their answers and come up with an answer for each question. Then, grade the group as a whole on their collective performance.

Not every student likes group assignments, since this may raise concerns about their individual grades. To avoid penalizing more diligent learners, take an average of each student’s two scores if the group score is higher than their individual score. If the student’s individual score is higher than the group score, let that individual score stand as the average. This process encourages students to be accountable for their own learning while helping develop their test-taking and collaboration skills.

Tests and quizzes with distractors : Distractors are common preconceptions or misconceptions about a topic. Have students answer various questions and, then, discuss their answers with a fellow student. After this discussion, have each student answer the same question again and see if their answers are any different. To close off the activity, initiate a group discussion about why the correct answer is actually the correct one. This acts as a form of metacognition by encouraging students to think about their own learning.

Retrieval practice

The process of bringing information to mind, or retrieval practice, is an effective strategy in boosting learning. In these exercises, students put away all learning materials and answer questions or discuss a topic purely based on their own recall of the information. Students can then refer to learning to evaluate how accurately they conveyed the information. Retrieval practice exercises also work well using the think-pair-share format.

Elaboration

In elaboration, students demonstrate the depth of their knowledge of a given topic by describing and explaining as much as they know about it, including as many relevant details as they can call to mind. This strategy extends the concept of rote memorization by encouraging students to draw connections within the content and between the content and other knowledge they already possess.

Interleaving

Interleaving is the process by which students mix multiple subjects or topics while they study. This allows students the opportunity to practice different modes of thinking and problem-solving as opposed to ‘blocked practice,’ which involves studying one topic thoroughly before moving on to the next.

Interleaving has been shown to improve test scores in a number of studies. As a best practice, it is important to use interleaving for related topics. For example, interleaving works well when switching between different algebra problems but is not nearly as effective when switching between radically different subject matter areas, such as literature and math equations.

Muddiest point

This form of assessment helps educators understand which elements of their course pose difficulties that may impede student progress and performance.

In this exercise, instructors ask students to note the “muddiest points” of the lesson, or the most confusing or difficult to grasp. Have students rate their degree of understanding and capture where the difficulty lies.

While the exercise shouldn’t take more than a few minutes, it has additional benefits beyond helping the instructor understand where the obstacles are for students. It also helps students more effectively analyze their own learning and to zero in on the exact issue that may be holding them back.

Peer instruction

Also known as ‘reciprocal teaching,’ this structured teaching practice asks students to reflect on new concepts they may be confused about and then share their responses to those prompts with a small group. Each group then derives a consensus response to share collectively with the rest of the class.

Peer instruction offers a number of benefits, including:

  • Increasing a student’s problem-solving skills and conceptual understanding abilities
  • Deepening student understanding of a topic and encouraging greater knowledge retention
  • Bolstering student engagement and raising student course satisfaction

Not only does this exercise call upon students to explain their thinking, it asks them to defend it against alternative arguments and modes. This helps reveal for students as much about how they think and process information as it does about the information itself.

Differentiated instruction

Not all students learn the same way. Differentiated instruction recognizes and accommodates for this by tailoring the learning process to individual needs. This is accomplished by altering the content, process, product or the learning environment itself.

With differentiated instruction, instructors consider the different learning styles of their students before devising their teaching strategies. That way, they can incorporate multiple modalities to allow all students to succeed equally in learning the material.

Some other ways to implement differentiated learning include:

  • Grouping students together for assignments by shared topics, interests, learning abilities or styles
  • Using formative assessment tools to assess individual student learning styles and progress and then adjusting lesson plans accordingly
  • Using classroom management tools to create safe and supportive learning environments for all students

Gamification

Sometimes turning a lesson into a game can better engage students in learning and comprehending the material. Gamification essentially incorporates reward-based activities and teaching tools into the lesson plan. Examples of gamification include:

  • Earning points for finishing tasks
  • Competing against peers toward a goal
  • Playing games that teach particular academic skills

Project-based learning

Through project-based learning, students work together on a project over an extended period, generally between one week and an entire semester. The project ideally involves solving a real-world problem or addressing a complex question. The finished product is a public presentation or product they can present to a live audience.

Problem-based learning

Problem-based learning involves incorporating real-world situations as a vehicle to help students apply course concepts in a practical application. This helps make learning more relevant by connecting concepts to the world outside the classroom and can add variety to the learning process itself.

Formative assessments

Formative assessments are designed to monitor learning and provide feedback on each student’s progress on an ongoing basis. The steady stream of feedback allows instructors to refine and improve their teaching strategies to keep the class on track. At the same time, students can practice their test-taking skills, improve information recall while honing in on their areas of strength and weakness.

Formative assessments are typically considered “low stakes.” The primary goal is not a letter grade but generating feedback for the instructor and the student. Examples of formative assessments include:

  • Self-assessments
  • Entry and exit slips
  • Low-stakes polls and quizzes
  • Exercises incorporating art or other visual representations of learning content
  • Misconception and errors
  • Interview assessments

Summative assessments

Instructors use summative assessments to evaluate how thoroughly students learned an area of study. Summative assessments usually come upon the completion of an instructional unit and compare student knowledge and achievement against a previously determined set of benchmarks.

Considered “high stakes,” summative assessments are commonly used to determine a student’s subsequent course work and educational progress. Examples of summative assessments include:

  • Final projects
  • Term papers
  • Midterm, final or standardized exams
  • Performance or recital

Educators may sometimes use summative assessments in a formative manner to guide student activities and efforts throughout their coursework.

Quick write

In this exercise, pose a prompt to the group to respond to in writing. Only allow five minutes for this exercise, so students can quickly reflect on their initial thoughts on a subject.

Uses and benefits of a quick write include helping to:

  • Determine whether students completed their assigned homework
  • Prime students to think about topics to be introduced or developed in the upcoming lesson
  • Give students the chance to access previous knowledge they may have on a subject
  • Instructors can opt to grade the quick write or simply collect it as a means of confirming attendance.

Pose a question to be answered or explained, and then take an anonymous poll to see how many students favor particular answers or explanations to the question.

Afterward, initiate a group discussion of the question and the poll’s results to see why students voted the way they did. Following the discussion, take the same poll again to gauge whether any students changed their answers and, if so, to what extent and why.

Hearing why students chose a particular explanation or answer helps the instructor understand how students think about that topic. It also helps them determine if additional explanation or clarification may be required before moving on in the lesson plan.

Turn and talk

In this exercise, instructors pose a question to the group, then instruct students to choose a partner to discuss their thoughts on the question with. This can create a comfortable atmosphere for sharing ideas before bringing ideas before the whole group.

Make sure the questions students are asked to discuss are clear and that the understanding of each participant is there in order to contribute to the conversation both as a speaker and listener.

This exercise is performed in small groups in which students read a preselected passage of course material. Students in each group divide up the material so that each member reads a portion of it silently and then shares what they’ve learned with the rest of the group.

Some questions participants can use as points of focus include:

  • What’s the big idea here?
  • What do you believe it means, and why does it matter?
  • How can someone apply this idea to help understand a larger topic?
  • What part(s) of the reading do you agree and/or disagree with?
  • What questions does the reading raise for you?

Instructors can implement jigsaws in a number of ways. In an ‘expert and cooperative group’ format, assign different groups different pieces of the material to read individually and discuss. Each group then becomes the expert group on that portion of the material. Following this, groups are redivided so that each new cooperative group contains one or two representatives from each of the previous expert groups. Each cooperative group then reviews the material with the expert representative. The jigsaw method is a great way to get students up to speed quickly on material while honing their critical thinking and communication skills.

Learning strategies help you better engage students in active learning by using a variety of activities such as reading, writing, discussion or problem-solving. Easy to execute, these activities promote analysis, synthesis, and the evaluation of class content. Equally important, they provide students with opportunities for feedback on how well they understand course material, ensuring they are making meaningful progress toward achieving course objectives.

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IMAGES

  1. 5 step problem solving method

    learning about problem solving refers to learning different strategies

  2. Introduction to Problem Solving Skills

    learning about problem solving refers to learning different strategies

  3. How to improve your problem solving skills and strategies

    learning about problem solving refers to learning different strategies

  4. What are the problem solving steps?

    learning about problem solving refers to learning different strategies

  5. 8 Important Problem Solving Skills

    learning about problem solving refers to learning different strategies

  6. Introduction to Problem Solving Skills

    learning about problem solving refers to learning different strategies

COMMENTS

  1. Metacognition

    When Pintrich (2002) asserts that "Students who know about the different kinds of strategies for learning, thinking, and problem solving will be more likely to use them" (p. 222), notice the students must "know about" these strategies, not just practice them.

  2. TEAL Center Fact Sheet No. 4: Metacognitive Processes

    Metacognitive knowledge refers to what individuals know about themselves as cognitive processors, about different approaches that can be used for learning and problem solving, and about the demands of a particular learning task.

  3. 7.3 Problem-Solving

    A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, "If at first you don't succeed, try, try again" describes trial and error.

  4. The Problem-Solving Process

    Allocate Resources Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue. The best strategy for solving a problem depends largely on the unique situation.

  5. Problem-Solving Strategies and Obstacles

    Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options. Functional fixedness: This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be ...

  6. Problem Solving

    Cognitive—Problem solving occurs within the problem solver's cognitive system and can only be inferred indirectly from the problem solver's behavior (including biological changes, introspections, and actions during problem solving).. Process—Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of ...

  7. Solving Problems

    A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error. The old adage, "If at first you don't succeed, try, try again" describes trial and error.

  8. 6.2: Problem Solving Strategies

    Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution. A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (Table 3). For ...

  9. Strategies for Learning

    Richards and Platt ( 1992) state that learning strategy is an intentional behavior to help learners understand, learn, or remember new information. In comparison, Schumaker and Deshler ( 1992) see learning strategy as an individual's way of organizing and using a particular set of skills to learn content more effectively.

  10. Learning strategies: a synthesis and conceptual model

    Deep learning refers to seeking meaning, ... Lavery 51 identified 14 different learning strategies and the overall effect was 0.46—with greater ... T. J. Teaching and learning as problem solving

  11. Thinking, Language, and Problem Solving

    Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution. A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them . For example ...

  12. Effective Learning Behavior in Problem-Based Learning: a Scoping Review

    Problem-based learning (PBL) emphasizes learning behavior that leads to critical thinking, problem-solving, communication, and collaborative skills in preparing students for a professional medical career. However, learning behavior that develops these skills has not been systematically described. This review aimed to unearth the elements of ...

  13. Cognitive Learning Theory: Benefits, Strategies and Examples

    5. Help learn new things faster. Through the experience of learning, the employee will be able to recycle and use the same learning methods that worked previously. This will help them learn new things a lot faster as they already know what works for them when it comes to obtaining new knowledge. 6.

  14. Self-regulated learning instruction and the relationships among self

    1.2. Problem Solving Ability. Self-regulated learning enables students to acquire good problem solving skills and behave like strategic learners (Cleary & Zimmerman, Citation 2004).Problem solving refers to the affective, cognitive and behavioral processes and to the particular set of skills people use in order to find solutions for the problems of their everyday life (Heppner et al., Citation ...

  15. What is Problem Solving? Steps, Process & Techniques

    1. Define the problem Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes. The sections below help explain key problem-solving steps.

  16. Problem Solving

    A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( [link] ). For example, a well-known strategy is trial and error. The old adage, "If at first you don't succeed, try, try again" describes trial and error.

  17. Analyzing the effects of the problem solving approach to the

    Further, Laal and Ghodsi (2012) emphasize in their review that learners involved in collaborative learning develop valuable problem solving skills. Problem solving skills refer to the ability of an individual to find meaningful solutions to solve problems using effective and timely strategies (Karabacak, Nalbant, & Topçuoğlu, 2015).

  18. PDF MATHEMATICAL PROBLEM-SOLVING STRATEGIES AMONG STUDENT TEACHERS

    Guzman Gurat M. (2018) "Mathematical problem-solving strategies among student teachers", Journal on Efficiency and ... One of the main difficulties in learning problem-solving is the fact that many skills are needed for a learner to be an effective ... problem-solving refers to the common situational

  19. Learning new problem-solving strategies leads to changes in problem

    Mathematics With development, children often shift from using incorrect or inefficient strategies for solving problems to using correct or more efficient strategies. This shift has been observed in many domains, including conservation, categorization, memory, and arithmetic, to name a few.

  20. 17 Learning Strategies to Implement In Your Classroom

    Problem-based learning. Problem-based learning involves incorporating real-world situations as a vehicle to help students apply course concepts in a practical application. This helps make learning more relevant by connecting concepts to the world outside the classroom and can add variety to the learning process itself. Formative assessments

  21. MTT 202-03 Quiz 2 Flashcards

    Study with Quizlet and memorize flashcards containing terms like T/F: Learning THROUGH problem solving means that students learn mathematics while they are solving problems., T/F: Learning FOR problem solving refers to developing skills to apply to real-life problems., T/F: Children's literature may present situations for problem solving. and more.

  22. Brain-Based Learning: Theory, Strategies, And Concepts

    It involves specific strategies for learning which are designed based on how human attention, memory, motivation, and conceptual knowledge acquisition work. Brain-based learning and teaching can optimize learning holistically. Historically, teaching and learning are largely based on what the students, teachers, and policy-makers think.

  23. MTT Quiz 2 Flashcards

    Learning about problem solving refers to learning different strategies. (True/False) True Learning through problem solving means that students learn the four-step Polya method and apply it in all situations. (True/False) False One value of drawing a picture or diagram asa problem-solving strategy is that it: