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Overview of the Problem-Solving Mental Process

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

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

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. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

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

Learning Is a Complex and Active Process That Occurs Throughout the Life Span, New Report Says

How People Learn II: Learners, Contexts, and Cultures (2018)

Chapter: 4 processes that support learning, 4 processes that support learning.

Learning is supported by an array of cognitive processes that must be coordinated for successful learning to occur. This chapter examines key processes that support learning. We first look at the ways that learners orchestrate processes essential to learning, such as attention, emotion regulation, and inhibition of incorrect or inappropriate responses. We then discuss memory—an essential component of most, if not all, types of learning.

The committee has drawn on both laboratory- and classroom-based research for this chapter. The research related to executive function and self-regulation draws on a mix of field- and classroom-based research from cognitive science and education involving learners of various ages, as well as on laboratory-based studies. Historically, much of the research on memory was conducted with adult populations, primarily in college settings, though younger populations have also been studied. There are historical reasons why college populations have been heavily relied on in research on memory (see Appendix C ). Psychology departments recruit thousands of students in introductory psychology classes to participate in experiments, and memory has been a particularly popular subject for such experiments ( Benassi et al., 2014 ; Pashler et al., 2007 ). Much of the research on memory discussed in this chapter is based on college student populations, but the committee also examined available research that included more diverse populations and learning contexts.

ORCHESTRATING LEARNING

In Chapters 2 and 3 , we discussed many of the resources on which learners draw and suggested that learners are able to coordinate these varied capacities—both consciously and unconsciously—as they are needed to meet learning challenges. How do people orchestrate their own learning? Three key ways are through metacognition, executive function, and self-regulation.

Metacognition is the ability to monitor and regulate one’s own cognitive processes and to consciously regulate behavior, including affective behavior. The term, which derives from cognitive theory, encompasses the awareness individuals have of their own mental processes (cognitive and affective) and their consequent ability to monitor, regulate, and direct their thinking to achieve a desired objective. This capacity has been studied since the early 1980s, and How People Learn: Mind Brain, Experience, and School: Expanded Edition ( HPL I 1 ) noted how important it is for educators to teach learners strategies for increasing their awareness of their learning and their capacity to direct it.

Also important is executive function , which is more frequently addressed by psychologists and neuroscientists and refers to cognitive and neural processing that involves the overall regulation of thinking and behavior and the higher-order processes that enable people to plan, sequence, initiate, and sustain their behavior toward some goal, incorporating feedback and making adjustments.

S elf-regulation refers to learning that is focused by means of metacognition, strategic action, and motivation to learn. Self-regulation is seen as involving management of cognitive, affective, motivational, and behavioral components that allow the individual to adjust actions and goals to achieve desired results.

Understanding the integration and interplay of these various levels of processing is important to understanding how learners orchestrate their learning in the context of their complex cognitive and social environments. The integration and interrelation of these dimensions of processing is also critical for deeper or higher-order learning , and for the development of complex skills and knowledge such as reasoning, problem solving, and critical thinking.

Executive Function

The processes involved in executive function include the abilities to hold information in mind, inhibit incorrect or premature responses, and sustain or switch attention to meet a goal. These processes are highly interrelated: successful application of executive function requires that the processes operate

___________________

1 As noted in Chapter 1 , this report uses the abbreviation “ HPL I ” for How People Learn: Brain, Mind, Experience, and School: Expanded Edition ( National Research Council, 2000 ).

in coordination with one another. Many of the same processes are involved in socioemotional development, which contributes to children’s classroom success ( Institute of Medicine and National Research Council, 2015 ). Like Kayla, the hypothetical geometry student we discussed in Chapter 3 , all learners need to choose among competing interests and then sustain attention to the chosen ones long enough to make progress, hold in mind multiple pieces of information (e.g., the equation Kayla had to apply and the symbolic notation that was the target for application), manipulate them productively, and monitor their own progress.

The fundamental neural bases of executive function are relatively well known. Early research suggested that the frontal lobes were the site of this capacity ( Chung et al., 2014 ; Damasio, 1994 ), but more recent neuroimaging research has shown that the various components of executive function use many areas and networks across the brain ( Collette et al., 2006 , Jurado and Rosselli, 2007 ; Marvel and Desmond, 2010 ). Like the positive and negative changes in prefrontal cortical thickness and connectivity with other neural structures described in Chapter 3 , the component processes of executive function develop rapidly during the preschool years, continue to develop into adolescence and even beyond, and undergo characteristic changes throughout adulthood.

Executive function is a focus of intense interest—as well as targeted educational interventions (see Box 4-1 )—because impaired executive function is a feature of several conditions that may negatively affect learning, including learning disabilities (both reading and mathematical disabilities); attention deficit/hyperactivity disorder, and autism. Conversely, well-developed cognitive control is correlated with numerous positive developmental outcomes, including physical health and socioeconomic status—and even absence of a criminal conviction by age 32 ( Moffitt et al., 2011 ). Moreover, recent research suggests that executive function (indicated by behaviors such as paying attention and following rules, for example) may be a better predictor of school readiness and academic achievement than general intelligence is (e.g., Blair and Razza, 2007 ; Eigsti et al., 2006 ; McClelland et al., 2007 ). Interventions that target social and emotional learning may be beneficial in part because they improve executive function ( Riggs et al., 2006 ).

Other work on executive functioning focuses on so-called “intrinsic” executive control, or a person’s ability to direct herself, change course when needed, and strategize in the absence of explicit rules to follow. For example, one study showed that 9-year-old middle-class children from Denver, Colorado, who spent more time in adult-led activities (such as piano lessons and playing on coached sports teams), and less time in self-directed and peer-negotiated activities (such as playing “pick up” sports games with other children) showed worse intrinsic executive functioning ( Barker et al., 2014 ). The researchers concluded that the time these children spent in structured learning activities

BOX 4-1 A Curriculum-Based Executive Function Intervention

limited their opportunities to learn to manage themselves in natural and informal learning contexts, which are critical for effective learning in the real world. Components of executive function develop and decline in neither linear nor binary (all or nothing) fashion. Both positive and negative age-related neurocognitive changes depend on the specific executive processes being engaged ( Spreng et al., 2010 ; Turner and Spreng, 2012 ). Across many domains, older adults often achieve good performance by recruiting different processes than those engaged by younger adults.

Self-Regulation of Learning

The capacity to understand and direct one’s own learning is important not only in school but also throughout life. When learners are self-regulated, they have more control over the strategies and behaviors they use to learn. Self-regulation allows them to more effectively direct their cognitive activity by voluntarily setting learning goals, identifying methods for achieving them, actively pursuing those methods, and tracking progress toward the goals. Regulating one’s learning requires monitoring of activities, thoughts, and emo-

tions and making the adjustments necessary to achieve goals ( Loyens et al., 2008 ). It also is facilitated when the expectations of educators accommodate learners’ interests and developmentally appropriate work, so that learners take responsibility for their goals and perceive that they have the power to make important decisions related to their mode of learning ( Patall, 2013 ).

Self-regulation is a key element of the broader concept of metacognition, the capacity to reflect on and monitor one’s own cognitive processes. Monitoring and regulating cognition are sets of interrelated processes. Monitoring processes are those involved in assessing one’s own cognitive activities, including learning and memory. The processes of regulation allow the individual to control the decision processes and actions in ways directed by his monitoring ( Bjork et al., 2013 ; Dunlosky and Metcalfe, 2009 ).

The growing body of research in this area has highlighted how difficult it is for people to regulate their own learning in formal educational settings and the corresponding value of training to improve this capacity. The complex processes involved have been the subject of a considerable amount of theoretical and experimental work in the past decade ( Vohs and Bauminster [2017] is a comprehensive handbook of recent research). A number of models have been proposed to characterize self-regulation processes, which suggest directions for interventions to improve learners’ capacity to direct their learning ( Panadero, 2017 ). For example, Hattie and Donoghue (2016) identified more than 400 strategies found in the research literature on learning strategies. This body of work has explored the basic regulatory processes and the influence of emotion, desire, and habits; the role of personality traits; the physiological processes involved in self-regulation and how they develop; and many other issues. (Ways that educators can foster self-regulation in their students are discussed in Chapter 7 .)

Growing understanding of the variety of variables that contribute to an individual’s capacity to regulate her learning complicates the task of succinctly defining what is involved. Nevertheless, the concept is generally understood to encompass personal characteristics, learning contexts, and motivational and regulatory processes, and all of these factors influence learning outcomes. Self-regulation is both a self-directive process and a set of thought patterns through which learners organize their activities to build skills. Successful self-regulated learners have developed the skills and habits to be effective learners, exhibiting effective learning strategies, effort, and persistence.

In one formulation, self-regulation is described as the interplay of the will to invest in learning, curiosity and a willingness to explore what one does not know, and the skills to pursue a deeper understanding of content ( Hattie and Donoghue, 2016 ). Put another way, it is the “self-corrective adjustments [that] are taking place as needed [for the learner] to stay on track, whatever [the learner’s] purpose is” ( Carver and Scheier, 2017 , p. 3). This capacity is driven from within, by intrinsic goals and responses to experience. Many

factors influence self-regulation, ranging from sleep to personality traits to social and cultural influences and beyond. Research is ongoing in this field and continues to enlarge the picture of the importance and complexity of self-regulation for learning.

HPL I summarized research by neuroscientists and cognitive scientists on memory processes ( National Research Council, 2000 ). This work had shown that memory is not a unitary construct that occurs in a single area of the brain. Instead, it comprises distinct types of processes associated with different memory functions. Not only are the processes of memory complex in themselves; but they also interact with other learning processes, such as the capacity to generalize (e.g., discrimination, categorization) and reason (e.g., comprehension, sense-making, causal inference).

A metaphor people commonly use to think about encoding and retrieving memories is that of spatial storage and search ( Roediger, 1980 ). In this metaphor, the mind is imagined as a physical space and bits of knowledge (memories) are imagined as objects stored in that space. For instance, the knowledge might be pictured as a collection of books stored on shelves in a library, files stored in cabinets, or digital files stored on a computer hard drive. Accordingly, learning is imagined as a process of creating and storing new files containing different sorts of knowledge, with the hope that those files can be found when needed.

This mental file cabinet view of the mind and memory is compelling, but researchers have rejected the idea that knowledge (memories) consists of copies of experiences stored in one’s mind. Instead, learning and memory systems give people the ability to produce knowledge without storing copies of it. Many other systems of the body work in a similar way. For instance, the visual system gives us the ability to perceive objects in the world, but copies of those objects are not stored in the eye. Sensory systems give us the ability to experience a wide variety of sensations without storing them in the body. Consider what happens if you were to pinch your arm and experience pain. It would be strange to say that when your arm was pinched, the pain was “retrieved” from some place where it was “stored” in your arm. Instead, sensory systems provide the appropriate architecture to convey information to the brain, which then constructs the sensory stimulation into an experience.

Reconstructing Memories

What the storage metaphor does not capture is the fact that learning actually involves skills for reconstructing memories based on past experiences and cues in the present environment, rather than reproducing copies of an

experience. Reconstruction is made possible by the way memories are encoded and stored throughout the brain. Each individual processes memories from a subjective perspective, so that his own memories of the same information or episode will not be identical to those of another person. An important point is that reconstruction of some kinds of knowledge is so implicit and automatic that it feels fluent rather than rebuilt: for a skilled reader and writer, for example, it is not necessary to continually, consciously reconstruct memories of grammar (see Chapter 3 for discussion of types of learning).

When an individual constructs an experience, a representation of that experience is left behind in the brain that she may be able to draw upon in the future. The representation is not a perfect copy of the world but rather a partial record of the individual’s subjective interpretation and perception, which is in turn shaped by prior knowledge, experiences, perceptual capabilities, and brain processes. The processes involved in transforming “what happens” into mental representations are known as encoding . Over time and with sleep, an encoded memory may be consolidated , a process whereby the neural connections associated with it are strengthened and the memory, or representation of the experience, is stabilized, or stored. Retrieval refers to the processes involved in reconstructing memories of past experiences. Retrieval processes are triggered and guided by retrieval cues in the learner’s environment (e.g., prompts, questions, or problems to be solved) or in the learner’s mind (other thoughts or ideas that have some relationship to the memory).

For example, in practicing the guitar, a student’s eyes pass over the spots of ink on the sheet music and visual inputs register in the primary visual areas at the back of the brain, creating the visual part of the pattern of the music. At the same time, the sounds the student creates as he strums the guitar contribute to the pattern by registering in his auditory areas, some of which are consonant with the spots on the page and others less so. Somatosensory areas also contribute to the pattern by registering the position of the fingers on the neck of the guitar as the student plays. Although the inputs from each of the sense modalities register in different areas of the brain (together called the information-processing system), they are pulled together in what are called association areas, contributing to the unified experience of “playing music.” At the same time, the association and sensory-motor brain areas contain traces of patterns remaining from previous experiences of playing the guitar and other activities and knowledge, and these are retroactivated , allowing the current guitar-playing experience to be enriched by the person’s prior learning and expectations. For long-term skill development and learning to occur, the distributed pattern of inputs contributing to the current experience (visual, motor, auditory, emotional, etc.) must be consolidated and integrated with stored memory representations from prior experiences. This is why deliberate practice is needed for long-term robust learning.

Because they are reconstructed, memories are not frozen in time; they are

reconstructed anew each time a person recalls something, and the reconstruction takes into account current knowledge, expectations, and context. For this reason, memories are not fixed but instead morph over time, and they may omit details or include fabricated details that did not occur. This is especially evident when people repeatedly remember the same event: what people report will change over time as new information and suggestions become incorporated into the rich, potentially multisensory tapestry of representations physically consolidated across the brain.

Reconstructive processes are at work even when a person remembers highly emotional and unique events, such as the attack in the United States on September 11, 2001 (9/11), as a study by Hirsch and colleagues demonstrates ( Hirst et al., 2015 ). These researchers asked people to report on their memories of 9/11: the circumstances in which they learned about the event as well as details about the attack itself. People were surveyed about their memories at four intervals beginning approximately 1 week after the event and concluding approximately 10 years later. The researchers found that the study participants forgot many of the details they reported during the first year and that their reports, even of emotionally charged and distinctive “flashbulb memories,” changed as time passed.

However, it is not only complex knowledge and events that must be reconstructed through the processes of memory. Even a simple task such as remembering a short list of words for a short amount of time requires active reconstruction. For example, when people were asked in a 1995 study to listen to short lists of related words, such as bed , rest, tired, awake, dream , and snooze , and later recall as many of the words as they could, they were highly likely to recall related words that were not on the list, such as sleep ( Roediger and McDermott, 1995 ). This study showed that rather than simply reproducing encoded copies of the words, the study participants actively attempted to reconstruct even an event as simple as encountering a short word list.

The fact that the processes involved in reconstructing knowledge are driven by cues is well established in the study of memory. As early as 1923, a researcher demonstrated differences in people’s capacity to recall the (then 48) U.S. states when asked to do so twice at a 30-minute interval: the only difference in the two tests was the retrieval context ( Brown, 1923 ). The retrieval cues available in a learner’s environment are critical for what she will be able to recall, and changing the retrieval context and cueing environment changes what a person expresses at any given moment in time ( Tulving and Thomson, 1973 ). Thus, if a person fails to remember a fact or skill at a particular time, that does not necessarily mean he does not possess the necessary knowledge.

The importance of retrieval cueing has been shown for complex as well as simple learning scenarios. In another classic study, Anderson and Pichert (1978) had students read a story about a series of events in a house and then recall details from the story from one of two perspectives: the perspective of

a burglar or the perspective of a person buying a home. When students shifted perspectives, they recalled new information that they had not recalled the first time. Only the retrieval conditions had changed. Students had encoded and stored the same story, but what they recalled depended on the cues to which they were attending. In a similar study, Gick and Holyoak (1980 , 1983 ) showed that people’s ability to solve a problem differed significantly with changes in the retrieval environment—in this case the instructions they received about how to use the materials they were to draw on in solving the problem.

There are two related implications of this work for educators and others interested in assessing people’s learning. First, undue weight should not be placed on any single assessment of a learner’s knowledge and skills. Second, memories are reconstructed more easily in situations that feel conducive and relevant to the content of the memory. The way a learner will retrieve particular knowledge and skills varies with the cues that trigger the reconstruction; the cues, in turn, are partly dependent on the emotional, social, and cognitive state of the learner at that moment. For example, a student who prides herself on baseball skills may have no trouble calling up knowledge of statistics during a game but may draw a blank in a high-stakes math test. In part to circumvent this problem, some researchers have proposed the use of dynamic assessments that present learners with multiple assessments and that may allow some form of instruction or feedback between attempts ( Koedinger et al., 2012 ). Another strategy is to help learners recognize and leverage their strengths in other contexts. For example, an educator might remind a baseball player to think about baseball when he has trouble remembering what he knows about statistics during a math test, or encourage a young child who helps with cooking at home to connect her understanding of the proportions of ingredients to call on this knowledge when learning about formal proportionality in math class.

Working and Long-Term Memory

Information may be rehearsed in the mind just for short periods of time, for use in a particular activity, or it may be retained long term so it can be retrieved together with other experiences far in the future. Long-term memory has obvious importance for learning, but short-term, or working, memory also plays a prominent role in complex cognitive tasks and daily activities, such as mental arithmetic (e.g., calculating a tip) and reading ( Moscovitch, 1992 ).

Working Memory

In practice, working memory is associated with academic achievement, including both math and reading skills (e.g., Bull and Scerif, 2001 ; Nevo and Breznitz, 2011 ). Keeping information temporarily in mind and manipulating it is necessary for key learning tasks such as remembering lengthy instructions

or keeping track of a problem being solved, and low working-memory capacity puts children at risk for poor academic progress ( Alloway and Gathercole, 2006 ; Alloway et al., 2009 ). Low working memory has also been associated with learning disabilities (e.g., Gathercole et al., 2006 ; Geary et al., 2012 ; Smith-Spark and Fisk, 2007 ; Wang and Gathercole, 2013 ) and such developmental disorders as attention deficit/hyperactivity disorder (e.g., Willcutt et al., 2005 ), specific language impairment (e.g., Briscoe and Rankin, 2009 ), and autism (e.g., Williams et al., 2006 ).

Working-memory performance declines beginning in middle age ( Bopp and Verhaeghen, 2005 ; Park et al., 2002 ; Verhaeghen and Salthouse, 1997 ). The primary cause of this decline seems to be age-related difficulty in attentional control ( Fabiani et al., 2006 ; Hasher et al., 2008 ). Individual differences in working-memory capacity are relatively stable over time, but recent studies suggest that intervention during childhood may have benefits for specific working-memory outcomes ( Holmes et al., 2009 ; Thorell et al., 2009 ).

Long-Term Memory

There are three types of long-term memory: procedural, episodic, and semantic. Procedural or implicit memory is unconscious, but the other two involve conscious awareness of past events as episodes in one’s individual history (e.g., episodic memory of meeting a friend for the first time) or facts and concepts not drawn from personal experience (e.g., semantic memory of state capitals). A complex operation such as learning to play the guitar involves the gradual and incremental processes of motor learning (using implicit memory) to improve finger work, as well as the episodic memory processes involved in trying to internalize and later repeat specific skills taught in a lesson, such as playing a particular chord sequence, semantic memory for information such as key signatures, and emotional memories of successfully playing beautiful music.

Although some memories may last a lifetime, all are reworked over time, and most fall victim to disruption and interference and are rapidly forgotten. If at some later time, the guitar student is reminded of a particular practice episode by a relevant cue or prompt and tries to recall it, he will not be able to recreate the entire episode in his memory or to play as he did before because some of the necessary representations and motor sequences will no doubt have been weakened or lost. Moreover, he will have experienced other, similar episodes of music and of playing the guitar; his memory of the practice episode may feature bits of information that were not actually part of that particular episode but are consistent with it.

The fact that new learning starts off as a distributed pattern of neural activation that must be stabilized and integrated with existing knowledge stores to be retained as long-term memory contributes to challenges for young learn-

ers. One reason is that the neural machinery they have available to register the experiences, stabilize and integrate them, and later retrieve the stored products, is relatively immature and therefore works less efficiently and effectively. Young learners (and beginners in a domain) also have fewer memories of previous experiences in similar situations to call upon, or retroactivate. Metaphorically, although the learning experience itself may be richly textured, by the time it is processed through an immature neural architecture, with a less well-developed set of cognitive, cultural, and social-emotional expectations or schema, it may lose many of its attributes and features, so that the representation of the experience (the memory) is impoverished. An adult’s more mature neural structures and networks manage to retain many more of the features of the original experience. For this reason, for many domains of formal learning, young learners generally require more support, relative to older learners. At the same time, young learners may be exquisitely sensitive to certain kinds of learning, such as what they learn from parents’ emotional reactions to their behaviors.

Cultural differences in long-term memory capacities have been observed, such as in several studies that compared the capacity for detailed recall of specific events among European Americans, Asians, and Asian Americans ( Han et al., 1998 ; Mullen, 1994 ; Wang, 2004 ; Wang and Conway, 2004 ; for a review, see Wang and Ross, 2007 , but also see Ji et al., 2009 , for the opposite pattern in an academic context). These researchers have identified differences in recall in preschoolers through adults and have suggested several hypotheses to explain them. Among the hypotheses are that cultural traditions and differences, such as in the way adults talk with preschoolers about personal experiences, may lead learners to attend to different aspects of events they experience (e.g., Leichtman et al., 2000 ; Wang, 2009 ) or to tend to use personal memories differently—for example, to guide decisions or to learn moral lessons and norms (e.g., Alea and Wang, 2015 ; Alea et al., 2015 ; Basso, 1996 ; Kulkofsky et al., 2009 ; Maki et al., 2015 ; Nile and Van Bergen, 2015 ; Wang and Conway, 2004 ).

This research has not definitively established the existence of or basis for cultural differences, and we note the risk of overgeneralizing between-group differences. However, it does suggest that the nature and form of memory for episodes may be influenced by culture.

Memory for Episodes of Learning

Memory for episodes of new learning is critically important because it allows for rapid, even one-trial, learning and retention of new information (e.g., Bauer and Varga, 2015 ). It is one of the building blocks for cognitive growth during development and throughout the life span. One of the most significant changes learners experience in the first two decades of life is an increase

in the amount of information they remember. As young learners develop, their memories also become more deliberate and strategic and they impose increased organization on the material they are learning (e.g., Bjorklund et al., 2009 ). The organizations they use to conceptualize material and to focus on different features and processes depend on their development and their environment and are therefore deeply cultural and situated. Children become increasingly aware of their own and others’ memory processes as they develop (i.e., their metamemory improves), which enables them to recruit information-processing resources to assist with increased memory demands (see Box 4-2 ).

Though many memories of distinct learning episodes persist even into old age, people tend to have increased difficulty in forming memories of new episodes as they age. Normal aging is accompanied by a gradual decline in episodic memory that begins as early as the twenties and accelerates precipitously after the age of 60 ( Salthouse, 2009 ). This decline is associated with degradation in a key aspect of episodic memory: the ability to anchor or bind an event to one’s personal past and to a location (e.g., Fandakova et al., 2014 ; Wheeler et al., 1997 ). This deficit can be manifested in a number of ways.

BOX 4-2 Helping Children Develop Memory Skills

Older adults are more likely than younger adults to forget where or when an event occurred or to erroneously combine elements from different events ( Spencer and Raz, 1995 ). Older adults may also be more likely than younger adults to bind irrelevant details ( Campbell et al., 2010 ).

As people grow older, changes in memory consolidation and retrieval processes also may affect learning. Aging affects the ability to integrate information together as a memory is consolidated. These deficits can emerge even while information is still being held within working memory, which suggests that the deficits may at least in part reflect a lowered ability to maintain and encode the features of an experience into consolidated representations (e.g., Mitchell et al., 2000 ; Peich et al., 2013 ; van Geldorp et al., 2015 ). The finding that deficits in older adults’ binding can be reduced when they are given strategies that enhance memory consolidation supports this idea (e.g., Craik and Rose, 2012 ; Naveh-Benjamin and Kilb, 2012 ; Naveh-Benjamin et al., 2007 ; Old and Naveh-Benjamin, 2012 ). Another possible explanation is that older adults have a bias toward pattern completion : the process by which a partial or degraded memory cue triggers an individual to use other prior knowledge and experiences to reconstitute a complete memory representation ( Stark et al., 2010 ).

Binding and pattern completion are likely to be part of the explanation for why older adults are more likely than younger adults to retain the “gist” of an event but not its specific details. For instance, after reading a list of associated words, older adults will be less likely than younger adults to remember each individual word presented on the list, but they will be at least as likely as younger adults to remember the themes of the list or to falsely remember nonpresented words that are thematically associated with the presented words (reviewed by Schacter et al., 1997 ). Similarly, older adults are more likely to remember the moral of a story rather than its details ( Adams et al., 1990 ) and to report general rather than specific details of past autobiographical events (e.g., Schacter et al., 2013 ). Studies show that declines in the specificity of memory likely begin in middle age, with increases in gist-based false memory already apparent by the time an adult is in her 50s ( Alexander et al., 2015 ).

Although these age differences are often framed as deficits, they do not always result in declines and can in fact be useful. The shift toward gist-based memory with age can lead older adults to be more likely than younger adults to remember the “big picture” or important implications ( McGinnis et al., 2008 ). The shift toward pattern completion also may enable older adults to note connections among events and to integrate across experiences, abilities that often are considered part of the wisdom that is acquired with age ( Baltes and Staudinger, 2000 ).

CONCLUSIONS

Executive function and self-regulation are critical processes for supporting learning. Both involve sets of processes that are related to success in school. Self-regulation involves many complex components, and researchers are actively working to understand how these components interact and how to support their development.

Memory is an important foundation for most types of learning. People’s learning and memory systems give them the ability to use past experiences to adapt and solve problems in the present. This ability to use the past by retrieving memories when they are needed is reconstructive in nature. It is not a process of searching for stored copies of mental representations of information and experiences but a set of processes triggered by cues in the learner’s environment through which he reconstructs these experiences and forges new connections for them. The retrieval cues available in a learner’s environment are critical for what she will be able to recall and also play a role in the way the learner begins to integrate new information as knowledge.

CONCLUSION 4-1: Successful learning requires coordination of multiple cognitive processes that involve different networks in the brain. In order to coordinate these processes, an individual needs to be able to monitor and regulate his own learning. The ability to monitor and regulate learning changes over the life span and can be improved through interventions.

CONCLUSION 4-2: Memory is an important foundation for most types of learning. Memory involves reconstruction rather than retrieval of exact copies of encoded mental representations. The cues available in a learner’s environment are critical for what she will be able to recall; they also play a role in the way the learner begins to integrate new information as knowledge.

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There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy.

In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom.

Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments.

How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.

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Problem-Based Learning: What and How Do Students Learn?

• Published: September 2004
• Volume 16 , pages 235–266, ( 2004 )

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• Cindy E. Hmelo-Silver 1  

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Problem-based approaches to learning have a long history of advocating experience-based education. Psychological research and theory suggests that by having students learn through the experience of solving problems, they can learn both content and thinking strategies. Problem-based learning (PBL) is an instructional method in which students learn through facilitated problem solving. In PBL, student learning centers on a complex problem that does not have a single correct answer. Students work in collaborative groups to identify what they need to learn in order to solve a problem. They engage in self-directed learning (SDL) and then apply their new knowledge to the problem and reflect on what they learned and the effectiveness of the strategies employed. The teacher acts to facilitate the learning process rather than to provide knowledge. The goals of PBL include helping students develop 1) flexible knowledge, 2) effective problem-solving skills, 3) SDL skills, 4) effective collaboration skills, and 5) intrinsic motivation. This article discusses the nature of learning in PBL and examines the empirical evidence supporting it. There is considerable research on the first 3 goals of PBL but little on the last 2. Moreover, minimal research has been conducted outside medical and gifted education. Understanding how these goals are achieved with less skilled learners is an important part of a research agenda for PBL. The evidence suggests that PBL is an instructional approach that offers the potential to help students develop flexible understanding and lifelong learning skills.

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

Abrandt Dahlgren, M., and Dahlgren, L. O. (2002). Portraits of PBL: Students' experiences of the characteristics of problem-based learning in physiotherapy, computer engineering, and psychology. Instr. Sci. 30: 111-127.

Google Scholar  

Albanese, M. A., and Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issues. Acad. Med. 68: 52-81.

Ames, C. (1992). Classrooms: Goals, structures, and student motivation. J. Educ. Psychol. 84: 261-271.

Bandura, A. (1997). Self-Efficacy: The Exercise of Control , Freeman, New York.

Barron, B. J. S. (2002). Achieving coordination in collaborative problem-solving groups. J. Learn. Sci. 9: 403-437.

Barrows, H. S. (2000). Problem-Based Learning Applied to Medical Education , Southern Illinois University Press, Springfield.

Barrows, H., and Kelson, A. C. (1995). Problem-Based Learning in Secondary Education and the Problem-Based Learning Institute (Monograph 1), Problem-Based Learning Institute, Springfield, IL.

Barrows, H. S., and Tamblyn, R. (1980). Problem-Based Learning: An Approach to Medical Education , Springer, New York.

Bereiter, C., and Scardamalia, M. (1989). Intentional learning as a goal of instruction. In Resnick, L. B. (ed.), Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser , Erlbaum, Hillsdale, NJ, pp. 361-392.

Biggs, J. B. (1985). The role of metalearning in study processes. Br. J. Educ. Psychol. 55: 185-212.

Blumberg, P., and Michael, J. A. (1992). Development of self-directed learning behaviors in a partially teacher-directed problem-based learning curriculum. Teach. Learn. Med. 4: 3-8.

Blumenfeld, P. C., Marx, R. W., Soloway, E., and Krajcik, J. S. (1996). Learning with peers: From small group cooperation to collaborative communities. Educ. Res. 25(8): 37-40.

Boud, D., and Feletti, G. (1991). The Challenge of Problem Based Learning , St. Martin's Press, New York.

Bransford, J. D., Brown, A. L., and Cocking, R. (2000). How People Learn , National Academy Press, Washington, DC.

Bransford, J. D., and McCarrell, N. S. (1977). A sketch of a cognitive approach to comprehension: Some thoughts about understanding what it means to comprehend. In Johnson-Laird, P. N., and Wason, P. C. (eds.), Thinking: Readings in Cognitive Science , Cambridge University Press, Cambridge, UK, pp. 377-399.

Bransford, J. D., Vye, N., Kinzer, C., and Risko, R. (1990). Teaching thinking and content knowledge: Toward an integrated approach. In Jones, B. F., and Idol, L. (eds.), Dimensions of Thinking and Cognitive Instruction , Erlbaum, Hillsdale, NJ, pp. 381-413.

Bridges, E. M. (1992). Problem-Based Learning for Administrators , ERIC Clearinghouse on Educational Management, Eugene, OR.

Brown, A. L. (1995). The advancement of learning. Educ. Res. 23(8): 4-12.

Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., and Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cogn. Sci. 13: 145-182.

Chi, M. T. H., DeLeeuw, N., Chiu, M., and LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cogn. Sci. 18: 439-477.

Chi, M. T. H., Feltovich, P., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cogn. Sci. 5: 121-152.

Cognition and Technology Group at Vanderbilt (1997). The Jasper Project: Lessons in Curriculum, Instruction, Assessment, and Professional Development , Erlbaum, Mahwah, NJ.

Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Rev. Educ. Res. 64: 1-35.

Collins, A., Brown, J. S., and Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In Resnick, L. B. (ed.), Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser , Erlbaum, Hillsdale, NJ, pp. 453-494.

DeGrave, W. S., Boshuizen, H. P. A., and Schmidt, H. G. (1996). Problem-based learning: Cognitive and metacognitive processes during problem analysis. Instr. Sci. 24: 321-341.

Derry, S. J., Lee, J., Kim, J.-B., Seymour, J., and Steinkuehler, C. A. (2001, April). From ambitious vision to partially satisfying reality: Community and collaboration in teacher education . Paper presented at the Annual Meeting of the American Educational Research Association, Seattle, WA.

Derry, S. J., Levin, J. R., Osana, H. P., Jones, M. S., and Peterson, M. (2000). Fostering students' statistical and scientific thinking: Lessons learned from an innovative college course. Am. Educ. Res. J. 37: 747-773.

Derry, S. J., Siegel, M., Stampen, J., and the STEP team (2002). The STEP system for collaborative case-based teacher education: Design, evaluation, and future directions. In Stahl, G. (ed.), Proceedings of CSCL 2002 , Erlbaum, Hillsdale, NJ, pp. 209-216.

Dewey, J. (1938). Experience and Education , Macmillan, New York.

Dochy, F., Segers, M., Van den Bossche, P., and Gijbels, D. (2003). Effects of problem-based learning: A meta-analysis. Learn. Instr. 13: 533-568.

Dods, R. F. (1997). An action research study of the effectiveness of problem-based learning in promoting the acquisition and retention of knowledge. J. Educ. Gifted 20: 423-437.

Dolmans, D. H. J. M., and Schmidt, H. G. (2000). What directs self-directed learning in a problem-based curriculum? In Evensen, D. H., and Hmelo, C. E. (eds.), Problem-Based Learning: A Research Perspective on Learning Interactions Erlbaum, Mahwah, NJ, pp. 251-262.

Duch, B. J., Groh, S. E., and Allen, D. E. (2001). The Power of Problem-Based Learning , Stylus, Steerling, VA.

Dweck, C. S. (1991). Self-theories and goals: Their role in motivation, personality, and development. In Nebraska Symposium on Motivation, 1990 , University of Nebraska Press, Lincoln, pp. 199-235.

Ertmer, P., Newby, T. J., and MacDougall, M. (1996). Students' responses and approaches to case-based instruction: The role of reflective self-regulation. Am. Educ. Res. J. 33: 719-752.

Evensen, D. (2000). Observing self-directed learners in a problem-based learning context: Two case studies. In Evensen, D., and Hmelo, C. E. (eds.), Problem-Based Learning: A Research Perspective on Learning Interactions , Erlbaum, Mahwah, NJ, pp. 263-298.

Evensen, D. H., Salisbury-Glennon, J., and Glenn, J. (2001). A qualitative study of 6 medical students in a problem-based curriculum: Towards a situated model of self-regulation. J. Educ. Psychol. 93: 659-676.

Faidley, J., Evensen, D. H., Salisbury-Glennon, J., Glenn, J., and Hmelo, C. E. (2000). How are we doing? Methods of assessing group processing in a problem-based learning context. In Evensen, D. H., and Hmelo, C. E. (eds.), Problem-Based Learning: A Research Perspective on Learning Interactions , Erlbaum, Mahwah, NJ, pp. 109-135.

Ferrari, M., and Mahalingham, R. (1998). Personal cognitive development and its implications for teaching and learning. Educ. Psychol. 33: 35-44.

Gallagher, S., and Stepien, W. (1996). Content acquisition in problem-based learning: Depth versus breadth in American studies. J. Educ. Gifted 19: 257-275.

Gallagher, S. A., Stepien, W. J., and Rosenthal, H. (1992). The effects of problem-based learning on problem solving. Gifted Child Q. 36: 195-200.

Gick, M. L., and Holyoak, K. J. (1980). Analogical problem solving. Cogn. Psychol. 12: 306-355.

Gick, M. L., and Holyoak, K. J. (1983). Schema induction and analogical transfer. Cogn. Psychol. 15: 1-38.

Goodman, L. J., Erich, E., Brueschke, E. E., Bone, R. C., Rose, W. H., Williams, E. J., and Paul, H. A. (1991). An experiment in medical education: A critical analysis using traditional criteria. JAMA 265: 2373-2376.

Greeno, J. G., Collins, A., and Resnick, L. B. (1996). Cognition and learning. In Berliner, D. C., and Calfee, R. C. (eds.), Handbook of Educational Psychology , Macmillan, New York, pp. 15-46.

Hmelo, C. E. (1994). Development of Independent Thinking and Learning Skills: A Study of Medical Problem-Solving and Problem-Based Learning , Unpublished Doctoral Dissertation, Vanderbilt University, Nashville, TN.

Hmelo, C. E. (1998). Problem-based learning: Effects on the early acquisition of cognitive skill in medicine. J. Learn. Sci. 7: 173-208.

Hmelo, C. E., and Ferrari, M. (1997). The problem-based learning tutorial: Cultivating higher-order thinking skills. J. Educ. Gifted 20: 401-422.

Hmelo, C. E., Gotterer, G. S., and Bransford, J. D. (1997). A theory-driven approach to assessing the cognitive effects of PBL. Instr. Sci. 25: 387-408.

Hmelo, C. E., and Guzdial, M. (1996). Of black and glass boxes: Scaffolding for learning and doing. In Edelson, D. C., and Domeshek, E. A. (eds.), Proceedings of ICLS 96 , AACE, Charlottesville, VA, pp. 128-134.

Hmelo, C. E., Holton, D., and Kolodner, J. L. (2000). Designing to learn about complex systems. J. Learn. Sci. 9: 247-298.

Hmelo, C. E., and Lin, X. (2000). The development of self-directed learning strategies in problem-based learning. In Evensen, D., and Hmelo, C. E. (eds.), Problem-Based Learning: Research Perspectives on Learning Interactions , Erlbaum, Mahwah, NJ, pp. 227-250.

Hmelo, C., Shikano, T., Bras, B., Mulholland, J., Realff, M., and Vanegas, J. (1995). A problem-based course in sustainable technology. In Budny, D., Herrick, R., Bjedov, G., and Perry, J. B. (eds.), Frontiers in Education 1995 , American Society for Engineering Education, Washington, DC.

Hmelo-Silver, C. E. (2000). Knowledge recycling: Crisscrossing the landscape of educational psychology in a Problem-Based Learning Course for Preservice Teachers. J. Excell. Coll. Teach. 11: 41-56.

Hmelo-Silver, C. E. (2002). Collaborative ways of knowing: Issues in facilitation. In Stahl, G. (ed.), Proceedings of CSCL 2002 , Erlbaum, Hillsdale, NJ, pp. 199-208.

Hmelo-Silver, C. E., and Barrows, H. S. (2003). Facilitating collaborative ways of knowing . Manuscript submitted for publication.

Hmelo-Silver, C. E., and Barrows, H.S. (2002, April). Goals and strategies of a constructivist teacher . Paper presented at American Educational Research Association Annual Meeting, New Orleans, LA.

Kilpatrick, W. H. (1918). The project method. Teach. Coll. Rec. 19: 319-335.

Kilpatrick, W. H. (1921). Dangers and difficulties of the project method and how to overcome them: Introductory statement: Definition of terms. Teach. Coll. Rec. 22: 282-288.

Kolodner, J. L. (1993). Case-Based Reasoning , Morgan Kaufmann, San Mateo, CA.

Kolodner, J. L., Hmelo, C. E., and Narayanan, N. H. (1996). Problem-based learning meets case-based reasoning. In Edelson, D. C., and Domeshek, E. A. (eds.), Proceedings of ICLS 96 , AACE, Charlottesville, VA, pp. 188-195.

Koschmann, T. D., Myers, A. C., Feltovich, P. J., and Barrows, H. S. (1994). Using technology to assist in realizing effective learning and instruction: A principled approach to the use of computers in collaborative learning. J. Learn. Sci. 3: 225-262.

Krajcik, J., Blumenfeld, P., Marx, R., and Soloway, E. (2000). Instructional, curricular, and technological supports for inquiry in science classrooms. In Minstrell, J., and Van Zee, E. H. (eds.), Inquiring Into Inquiry Learning and Teaching in Science , American Association for the Advancement of Science, Washington, DC, pp. 283-315.

Krajcik, J., Marx, R., Blumenfeld, P., Soloway, E., and Fishman, B. (2000, April). Inquiry-based science supported by technology: Achievement among urban middle school students . Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.

Lampert, M. (2001). Teaching Problems and the Problems of Teaching , Yale University Press, New Haven, CT.

Leontiev, A. N. (1978). Activity, Consciousness, and Personality (M. J. Hall, Trans.), Prentice-Hall, Englewood Cliffs, NJ.

Lesgold, A., Rubinson, H., Feltovich, P., Glaser, R., Klopfer, D., and Wang, Y. (1988). Expertise in a complex skill: Diagnosing x-ray pictures. In Chi, M. T. H., Glaser, R., and Farr, M. J. (eds.), The Nature of Expertise , Erlbaum. Hillsdale, NJ, pp. 311-342.

Linn, M. C., and Hsi, S. (2000). Computers, Teachers, Peers: Science Learning Partners , Erlbaum, Mahwah, NJ.

Mennin, S. P., Friedman, M., Skipper, B., Kalishman, S., and Snyder, J. (1993). Performances on the NBME I, II, and III by medical students in the problem-based and conventional tracks at the University of New Mexico. Acad. Med. 68: 616-624.

Needham, D. R., and Begg, I. M. (1991). Problem-oriented training promotes spontaneous analogical transfer. Memory-oriented training promotes memory for training. Mem. Cogn. 19: 543-557.

Norman, G. R., Brooks, L. R., Colle, C., and Hatala, H. (1998). Relative effectiveness of instruction in forward and backward reasoning . Paper presented at the Annual Meeting of the American Educational Research Association, San Diego, CA.

Norman, G. R., Trott, A. D., Brooks, L. R., and Smith, E. K. (1994). Cognitive differences in clinical reasoning related to postgraduate training. Teach. Learn. Med. 6: 114-120.

Novick, L. R., and Hmelo, C. E. (1994). Transferring symbolic representations across nonisomorphic problems. J. Exp. Psychol. Learn. Mem. Cogn. 20: 1296-1321.

Novick, L. R., and Holyoak, K. J. (1991). Mathematical problem solving by analogy. J. Exp. Psychol. Learn. Mem. Cogn. 17: 398-415.

O'Donnell, A. M. (1999). Structuring dyadic interaction through scripted cooperation. In O'Donnell, A. M., and King, A. (eds.), Cognitive Perspectives on Peer Learning , Erlbaum, Mahwah, NJ, pp. 179-196.

Palincsar, A. S., and Herrenkohl, L. R. (1999). Designing collaborative contexts: Lessons from three research programs. In O'Donnell, A. M., and King, A. (eds.), Cognitive Perspectives on Peer Learning ,Erlbaum, Mahwah, NJ, pp. 151-178.

Patel, V. L., Groen, G. J., and Norman, G. R. (1991). Effects of conventional and problem-based medical curricula on problem solving. Acad. Med. 66: 380-389.

Patel, V. L., Groen, G. J., and Norman, G. R. (1993). Reasoning and instruction in medical curricula. Cogn. Instr. 10: 335-378.

Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In Salomon, G., and Perkins, D. (eds.), Distributed Cognitions: Psychological and Educational Considerations , Cambridge University Press, New York, pp. 47-87.

Perfetto, G. A., Bransford, J. D., and Franks, J. J. (1983). Constraints on access in a problem-solving context. Mem. Cogn. 11: 24-31.

Puntambekar, S., and Kolodner, J. L. (1998). The design diary: A tool to support students in learning science by design. In Bruckman, A. S., Guzdial, M., Kolodner, J., and Ram, A. (eds.), Proceedings of ICLS 98 , AACE, Charlottesville, VA, pp. 230-236.

Ram, P. (1999). Problem-based learning in undergraduate instruction: A sophomore chemistry laboratory. J. Chem. Educ. 76: 1122-1126.

Ramsden, P. (1992). Learning to Teach in Higher Education , Routledge, New York.

Salomon, G. (1993). No distribution without individual cognition: A dynamic interactional view. In Salomon, G., and Perkins, D. (eds.), Distributed Cognitions: Psychological and Educational Considerations ,Cambridge University Press, New York, pp. 111-138.

Salomon, G., and Perkins, D. N. (1989). Rocky roads to transfer: Rethinking mechanisms of a neglected phenomenon. Educ. Psychol. 24: 113-142.

Schmidt, H. G., DeVolder, M. L., De Grave, W. S., Moust, J. H. C., and Patel, V. L. (1989). Explanatory models in the processing of science text: The role of prior knowledge activation through small-group discussion. J. Educ. Psychol. 81: 610-619.

Schmidt, H. G., Machiels-Bongaerts, M., Hermans, H., ten Cate, T. J., Venekamp, R., and Boshuizen, H. P. A. (1996). The development of diagnostic competence: Comparison of a problem-based, an integrated, and a conventional medical curriculum. Acad. Med. 71: 658-664.

Schmidt, H. G., and Moust, J. H. C. (2000). Factors affecting small-group tutorial learning: A review of research. In Evensen, D., and Hmelo, C. E. (eds.), Problem-Based Learning: A Research Perspective on Learning Interactions , Erlbaum, Mahwah, NJ, pp. 19-51.

Schwartz, D. L., and Bransford, J. D. (1998). A time for telling. Cogn. Instr. 16: 475-522.

Schoenfeld, A. H. (1985). Mathematical Problem Solving , Academic Press, Orlando, FL.

Shikano, T., and Hmelo, C. E. (1996, April). Students' learning strategies in a problem-based curriculum for sustainable technology . Paper presented at American Educational Research Association Annual Meeting, New York.

Steinkuehler, C. A., Derry, S. J., Hmelo-Silver, C. E., and DelMarcelle, M. (2002). Cracking the resource nut with distributed problem-based learning in secondary teacher education. J. Distance Educ. 23: 23-39.

Stepien, W. J., and Gallagher, S. A. (1993). Problem-based learning: As authentic as it gets. Educ. Leadersh. 50(7): 25-29.

Torp, L., and Sage, S. (2002). Problems as Possibilities: Problem-Based Learning for K-12 Education , 2nd edn., ASCD, Alexandria, VA.

Vernon, D. T., and Blake, R. L. (1993). Does problem-based learning work?: A meta-analysis of evaluative research. Acad. Med. 68: 550-563.

Vye, N. J., Goldman, S. R., Voss, J. F., Hmelo, C., and Williams, S. (1997). Complex math problem-solving by individuals and dyads: When and why are two heads better than one? Cogn. Instr. 15: 435-484.

Webb, N. M., and Palincsar, A. S. (1996). Group processes in the classroom. In Berliner, D., and Calfee, R. (eds.), Handbook of Educational Psychology , MacMillan, New York, pp. 841-876.

Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity , Cambridge University Press, New York.

White, B. Y., and Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cogn. Instr. 16: 3-118.

Williams, S. M. (1992). Putting case based learning into context: Examples from legal, business, and medical education. J. Learn. Sci. 2: 367-427.

Williams, S. M., Bransford, J. D., Vye, N. J., Goldman, S. R., and Carlson, K. (1993). Positive and negative effects of specific knowledge on mathematical problem solving . Paper presented at the American Educational Research Association Annual Meeting, Atlanta, GA.

Zimmerman, B. (2002). Becoming a self-regulated learner: An overview. Theory Pract . 41, 64-71.

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Hmelo-Silver, C.E. Problem-Based Learning: What and How Do Students Learn?. Educational Psychology Review 16 , 235–266 (2004). https://doi.org/10.1023/B:EDPR.0000034022.16470.f3

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Teaching problem solving.

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Tips and Techniques

Expert vs. novice problem solvers, communicate.

• Have students  identify specific problems, difficulties, or confusions . Don’t waste time working through problems that students already understand.
• If students are unable to articulate their concerns, determine where they are having trouble by  asking them to identify the specific concepts or principles associated with the problem.
• In a one-on-one tutoring session, ask the student to  work his/her problem out loud . This slows down the thinking process, making it more accurate and allowing you to access understanding.
• When working with larger groups you can ask students to provide a written “two-column solution.” Have students write up their solution to a problem by putting all their calculations in one column and all of their reasoning (in complete sentences) in the other column. This helps them to think critically about their own problem solving and helps you to more easily identify where they may be having problems. Two-Column Solution (Math) Two-Column Solution (Physics)

Encourage Independence

• Model the problem solving process rather than just giving students the answer. As you work through the problem, consider how a novice might struggle with the concepts and make your thinking clear
• Have students work through problems on their own. Ask directing questions or give helpful suggestions, but  provide only minimal assistance and only when needed to overcome obstacles.
• Don’t fear  group work ! Students can frequently help each other, and talking about a problem helps them think more critically about the steps needed to solve the problem. Additionally, group work helps students realize that problems often have multiple solution strategies, some that might be more effective than others

Be sensitive

• Frequently, when working problems, students are unsure of themselves. This lack of confidence may hamper their learning. It is important to recognize this when students come to us for help, and to give each student some feeling of mastery. Do this by providing  positive reinforcement to let students know when they have mastered a new concept or skill.

Encourage Thoroughness and Patience

• Try to communicate that  the process is more important than the answer so that the student learns that it is OK to not have an instant solution. This is learned through your acceptance of his/her pace of doing things, through your refusal to let anxiety pressure you into giving the right answer, and through your example of problem solving through a step-by step process.

Experts (teachers) in a particular field are often so fluent in solving problems from that field that they can find it difficult to articulate the problem solving principles and strategies they use to novices (students) in their field because these principles and strategies are second nature to the expert. To teach students problem solving skills,  a teacher should be aware of principles and strategies of good problem solving in his or her discipline .

The mathematician George Polya captured the problem solving principles and strategies he used in his discipline in the book  How to Solve It: A New Aspect of Mathematical Method (Princeton University Press, 1957). The book includes  a summary of Polya’s problem solving heuristic as well as advice on the teaching of problem solving.

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3 Learning Theories: Understanding How People Learn

Introduction.

Learning theories describe the conditions and processes through which learning occurs, providing teachers with models to develop instruction sessions that lead to better learning. These theories explain the processes that people engage in as they make sense of information, and how they integrate that information into their mental models so that it becomes new knowledge. Learning theories also examine what motivates people to learn, and what circumstances enable or hinder learning.

Sometimes people are skeptical of having to learn theory, believing those theories will not be relevant in the real world, but learning theories are widely applicable. The models and processes that they describe tend to apply across different populations and settings, and provide us with guidelines to develop exercises, assignments, and lesson plans that align with how our students learn best. Learning theories can also be engaging. People who enjoy teaching often find the theories interesting and will be excited when they start to see connections between the theory and the learning they see happening in their own classrooms.

General Learning Theories

With a basic understanding of learning theories, we can create lessons that enhance the learning process. This understanding helps us explain our instructional choices, or the “why” behind what and how we teach. As certain learning theories resonate with us and we consciously construct lessons based on those theories, we begin to develop a personal philosophy of teaching that will guide our instructional design going forward. This chapter provides a bridge from theory to practice by providing specific examples of how the theories can be applied in the library classroom. These theories provide a foundation to guide the instructional design and reflective practices presented in the rest of this textbook.

As you read, you might consider keeping track of the key points of each theory and thinking about how these theories could be applied to your practice. Figure 3.1 provides you with an example of a graphic organizer, one of the instructional materials that will be discussed in Chapter 11, that you could use to take notes as you read this chapter.  In addition to the examples in practice that are provided in this chapter, you might add some of your own.

Figure 3.1: Graphic Organizer for Major Learning Theories

Behaviorism

Behaviorism is based largely on the work of John B. Watson and B. F. Skinner. Behaviorists were concerned with establishing psychology as a science and focused their studies on behaviors that could be empirically observed, such as actions that could be measured and tested, rather than on internal states such as emotions (McLeod, 2015). According to behaviorists, learning is dependent on a person’s interactions with their external environment. As people experience consequences from their interactions with the environment, they modify their behaviors in reaction to those consequences. For instance, if a person hurts their hand when touching a hot stove, they will learn not to touch the stove again, and if they are praised for studying for a test, they will be likely to study in the future

According to behavioral theorists, we can change people’s behavior by manipulating the environment in order to encourage certain behaviors and discourage others, a process called conditioning (Popp, 1996). Perhaps the most famous example of conditioning is Pavlov’s dog. In his classic experiment, Pavlov demonstrated that a dog could be conditioned to associate the sound of a bell with food, so that eventually the dog would salivate whenever it heard the bell, regardless of whether it received food. Watson adapted stimulus conditioning to humans (Jensen, 2018). He gave an 11-month-old baby a rat, and the baby seemed to enjoy playing with it. Over time, Watson caused a loud, unpleasant sound each time he brought out the rat. Eventually, the baby associated the rat with the noise and cried when he saw the rat. Although Watson’s experiment is now considered ethically questionable, it did establish that people’s behavior could be modified through control of environmental stimuli.

Skinner (1938) examined how conditioning could shape behavior in longer-term and more complex ways by introducing the concept of reinforcement. According to Skinner, when people receive positive reinforcement, such as praise and rewards for certain behaviors, those behaviors are strengthened, while negative reinforcement will deter behaviors. According to Skinner, by carefully controlling the environment and establishing a system of reinforcements, teachers, parents, and others can encourage and develop desired behaviors (Jensen, 2018). A simple example of behaviorism in the classroom is a point system in which students are awarded points for good behavior and deducted points for unwanted behavior. Eventually, accumulated points might be traded in for rewards like small gifts or homework passes. This approach assumes that motivation is external, in that students will engage in certain behaviors in order to gain the rewards.

Because it emphasizes the external environment, behaviorism largely ignores or discounts the role of internal influences such as prior knowledge and emotion (Popp, 1996).  To an extent, behaviorists view learners as blank slates and emphasize the role of the teacher in the classroom. In this teacher-centered approach, instructors hold the knowledge, decide what will be learned, and establish the rewards for learning. Since their experience and prior knowledge are not considered relevant, learners are passive participants simply expected to absorb the knowledge transmitted by the teacher. While the idea of learners as blank slates has fallen out of favor, many of the conditioning aspects of behaviorism remain popular. As almost any student can attest, behavioral methods of reinforcement, such as the point system described above, are still common, especially in younger grades. Recent trends toward gaming in the classroom, where certain behaviors are rewarded with points and leveling up, are based in a behaviorist approach to learning. See Activity 3.1 for a brief activity on behaviorism.

Activity 3.1: Reflecting on Behaviorism

Think of some of your own learning experiences, whether they were in a traditional classroom, through professional development training, or related to personal interests, such as dance or photography lessons. Try to identify a few examples of behaviorism from those experiences and reflect on the following questions:

• How did your instructors use behavioral practice in their classrooms?
• Did you find those practices motivating? Why or why not?
• If you can think of examples of behaviorism from several different learning experiences, were they more appropriate in some situations than others? How so?
• Have you ever used, or can you imagine using, behaviorism in your own teaching practice? How so?

Humanism recognizes the basic dignity and worth of each individual and believes people should be able to exercise some control over their environment. Although humanism as an educational philosophy has its roots in the Italian Renaissance, the more modern theorists associated with this approach include John Dewey, Carl Rogers, Maria Montessori, Paolo Freire, and Abraham Maslow. Humanist learning theory is a whole-person approach to education that centers on the individual learners and their needs, and that considers affective as well as cognitive aspects of learning. At its essence, “humanism in education traditionally has referred to a broad, diffuse outlook emphasizing human freedom, dignity, autonomy, and individualism” (Lucas, 1996). Within this broader context, humanism is also characterized by the following tenets (Madsen & Wilson, 2012; Sharp, 2012):

• Students are whole people, and learning must attend to their emotional as well as their cognitive state.
• Teachers should be empathetic.
• Learners are self-directed and internally motivated.
• The outcome of learning is self-actualization.

Humanism centers the individual person as the subject and recognizes learners as whole beings with emotional and affective states that accompany their cognitive development. Recognizing the role of students’ emotions means understanding how those emotions impact learning. Student anxiety, say around a test or a research paper, can interfere with the cognitive processes necessary to be successful. Empathetic teachers recognize and try to understand students’ emotional states, taking steps to alleviate negative emotions that might detract from learning by creating a supportive learning environment.

In a library context, Mellon (1986) identified the phenomenon of library anxiety, or the negative emotions that some people experience when doing research or interacting with library tools and services. This anxiety can distract learners and make it difficult to engage in the processes necessary to search for, evaluate, and synthesize the information they need to complete their task. Similarly, in her Information Search Process, Kuhlthau (1990) describes the affective states as well as the cognitive processes students engage in when doing research, acknowledging that their emotions fluctuate among anxiety, optimism, and, ultimately, satisfaction or disappointment.

A humanist approach to education recognizes these affective states and seeks to limit their negative impact. For instance, we can acknowledge that feelings of anxiety are common so learners recognize that they are not alone. We can also explain how the skills students learn are relevant to their lives in and outside of the classroom.

Because humanists see people as autonomous beings, they believe that learning should be self-directed, meaning students should have some choice in what and how they learn. Humanistic education is often connected with student-centered pedagogical approaches such as differentiated curricula, self-paced learning, and discovery learning (Lucas, 1996). Self-directed learning can take many forms, but it generally means that the instructor acts as a guide, and learners are given the freedom to take responsibility for their own learning. Teachers will provide the materials and opportunities for learning, but students will engage with the learning on their own terms. In a library classroom, we can give students choices about the topics they will research or offer learners different types of activities to practice skills and demonstrate what they have learned.

Humanists also believe that learning is part of a process of self-actualization. They maintain that learning should be internally motivated and driven by students’ interests and goals, rather than externally motivated and focused on a material end goal such as achievement on tests, or employment (Sharp, 2012). The expectation is that when students are allowed to follow their interests and be creative, and when learning takes place within a supportive environment, students will engage in learning for its own sake. This emphasis on self-actualization is largely based on Maslow’s (1943) hierarchy of needs. Maslow identified five levels of needs: basic physiological needs such as food, water, and shelter; safety and security needs; belongingness and love needs, including friends and intimate relationships; esteem needs, including feelings of accomplishment; and self-actualization, when people achieve their full potential. Importantly, these needs are hierarchical, meaning a person cannot achieve the higher needs such as esteem and self-actualization until more basic needs such as food and safety are met. The role of the humanist teacher is to facilitate the student’s self-actualization by helping to ensure needs such as safety and esteem are met through empathetic teaching and a supportive classroom.

In his book, Pedagogy of the Oppressed , Freire (2000) brings together many of the student-centered elements of humanistic education, with a strong emphasis on social justice aspects of learning and teaching. In contrast to behaviorist approaches, Freire emphasizes the importance of students’ life experience to their learning. He criticizes what he describes as the “banking model” of education, in which students are viewed as passive and empty vessels into which teachers simply deposit bits of knowledge that students are expected to regurgitate on exams or papers without any meaningful interaction. Freire insists that learning must be relevant to the student’s life and the student should be an active participant in order for learning to be meaningful. Freire also emphasized the emancipatory role of education, arguing that the purpose of education was for learners to gain agency to challenge oppressive systems and improve their lives, and praxis, in which learners put abstract and theoretical knowledge into practice in the real world.

While a student-centered approach and choice can be introduced in any classroom, observers note that in an age of curriculum frameworks and standardized tests, where teachers are often constrained by the material, the ability to provide students with choice and allow for exploration is limited (Sharp, 2012; Zucca-Scott, 2010). Librarians often face similar constraints. School librarians also must meet state and district curriculum standards. Academic librarians generally depend on faculty invitations to conduct instruction and need to adapt their sessions to fit the content, time frame, and learning objectives of the faculty member. Nevertheless, we can always find ways to integrate some self-direction. For instance, rather than using planned examples to demonstrate searches, we might have students suggest topics to search. If we plan hands-on practice activities, we could allow learners to explore their own interests as they engage in the activity, rather than limiting them to preselected topics.

Cognitivism

Cognitivism, or cognitive psychology, was pioneered in the mid-twentieth century by scientists including George Miller, Ulric Neisser, and Noam Chomsky. Whereas behaviorists focus on the external environment and observable behavior, cognitive psychologists are interested in mental processes (Codington-Lacerte, 2018). They assert that behavior and learning entail more than just response to environmental stimuli and require rational thought and active participation in the learning process (Clark, 2018). To cognitivists, learning can be described as “acquiring knowledge and skills and having them readily available from memory so you can make sense of future problems and opportunities” (Brown et al., 2014, p. 2).

Cognitivists view the brain as an information processor somewhat like a computer that functions on algorithms that it develops in order to process information and make decisions. According to cognitive psychology, people acquire and store knowledge, referred to as schema, in their long-term memory. In addition to storing knowledge, people organize their knowledge into categories, and create connections across categories or schema that help them retrieve relevant pieces of information when needed (Clark, 2018). When individuals encounter new information, they process it against their existing knowledge or schema in order to make new connections. Cognitivists are interested in the specific functions that allow the brain to store, recall, and use information, as well as in mental processes such as pattern recognition and categorization, and the circumstances that influence people’s attention (Codington-Lacerte, 2018).

Because cognitivists view memory and recall as the key to learning, they are interested in the processes and conditions that enhance memory and recall. According to cognitive psychology research, traditional methods of study, including rereading texts and drilling practice, or the repetition of terms and concepts, are not effective for committing information to memory (Brown et al., 2014). Rather, cognitivists assert that activities that require learners to recall information from memory, sometimes referred to as “retrieval practice,” lead to better memory and ultimately better learning. For example, they suggest that language learners use flash cards to practice vocabulary words, rather than writing the words out over and over or reading and rereading a list of words, because the flash cards force the learner to recall information from memory.

While testing has fallen out of favor with many educators and education theorists, cognitivists find tests can be beneficial as both a retrieval practice and a diagnostic tool. They view tests not only as a way to measure what has been learned but as a way to practice retrieval of important concepts, and as a way to identify gaps or weaknesses in knowledge so that learners know where to concentrate their efforts (Brown et al., 2014). Cognitivists encourage “spaced practice,” or recalling previously learned information at regular intervals, and “interleaving,” or learning related concepts together to establish connections among them. Their research has found that retrieval is more effective when the brain is forced to recall information after some time has passed, and when the recall involves two or more related subjects or concepts. Finally, cognitivists also promote problem-based learning, maintaining that “trying to solve a problem before being taught the solution leads to better learning, even when errors are made in the attempt” (Brown et al., 2014, p.4).

These processes that enhance memory and recall, and thus learning, have some implications for instructors in creating an optimal environment for learning. Gagné (1985) proposed nine conditions for learning, referred to as the external conditions of learning, or the nine events of instruction:

• Gain attention. Engage students’ attention by tying learning to relevant events in their lives and asking stimulating questions.
• Inform the learner of the objective.  Begin by sharing the learning goals with the students, thus setting expectations and providing a map of the learning.
• Stimulate recall of prior learning.  Encourage students to remember previously learned relevant skills and knowledge before introducing new information.
• Present the stimulus.  Share new information. This step depends on the content of the lesson. For instance, a lesson on Boolean operators might begin with a Venn diagram and examples of the uses of and , or , and not .
• Provide learner guidance.  Facilitate learning by demonstration and explanation.
• Elicit performance.  Allow time for students to practice skills and demonstrate their abilities. Ideally, students would be given low-stakes opportunities for practice, so they feel comfortable if they do not succeed immediately.
• Provide feedback.  Offer students input on what they are doing well and where they can improve.
• Assess performance.  Employ measures such as assignments, activities, and projects to gauge whether learning has occurred.
• Enhance retention and transfer.  Give students opportunities to practice skills in new contexts, which improves retention and helps students see how the skills are applied to different areas.

Cognitivism remains a popular approach to learning. However, one criticism of cognitive psychology is that, unlike humanism, it does not account for the role of emotions in learning (Codington-Lacerte, 2018). Further, some critics believe that cognitivism overemphasizes memorization and recall of facts to the detriment of higher-order skills such as creativity and problem solving. However, cognitivists argue that the ability to recall facts and concepts is essential to higher-order thinking, and therefore the two are not mutually exclusive but actually interdependent (Brown et al., 2014). Finally, cognitivism is considered teacher-centered, rather than learner-centered, since it emphasizes the role of the instructor in organizing learning activities and establishing the conditions of learning (Clark, 2018). Activity 3.2 is a brief exercise on cognitivism.

Activity 3.2: Reflecting on Cognitivism

Cognitive scientists recommend retrieval practice, including spaced practice and interleaving, over drilling.

Questions for Reflection and Discussion:

• What kind of study practices do you tend to use? Do your practices vary depending on the content or material you are studying? How so?
• Can you think of ways to integrate retrieval practices into your work for this class?
• Spaced practice involves returning to previously learned concepts at later times, but information professionals often teach one-shot sessions. Can you think of ways to integrate spaced practice into a one-shot session?

Constructivism

Constructivism posits that individuals create knowledge and meaning through their interactions with the world. Like cognitivism, and as opposed to behaviorism, constructivism acknowledges the role of prior knowledge in learning, believing that individuals interpret what they experience within the framework of what they already know (Kretchmar, 2019a). Social constructs, such as commonly held beliefs, and shared expectations around behavior and values provide a framework for knowledge, but people “do not just receive this knowledge as if they were empty vessels waiting to be filled. Individuals and groups interact with each other, contributing to the common trove of information and beliefs, reaching consensus with others on what they consider is the true nature of identity, knowledge, and reality” (Mercadal, 2018). Cognitivism and constructivism overlap in a number of ways. Both approaches build on the theories of Jean Piaget, who is sometimes referred to as a cognitive constructivist. However, while cognitivism is considered teacher-centered, constructivism centers the learner by recognizing their role in engaging with content and constructing meaning. Constructivist teachers act as guides or coaches, facilitating learning by developing supportive activities and environments, and building on what students already know (Kretchmar, 2019b).

Piaget discusses the concepts of assimilation, accommodation, and disequilibrium to describe how people create knowledge. In his early work as a biologist, Piaget noticed how organisms would adapt to their environment in order to survive. Through such adaptation, the organism achieved equilibrium. Extending these observations to cognitive science, he posited that human beings also seek equilibrium (Kretchmar, 2019a).

When they encounter new situations, or new information, human beings must find a way to deal with the new information. Similar to the processes described in the section on cognitivism, people will examine their existing knowledge, or schema, to see if the new information fits into what they already know. If it does, they are able to assimilate the information relatively easily. However, if the new information does not fit into what people already know, they experience disequilibrium or cognitive conflict, and must adapt by accommodating the new information. For example, once children learn what a dog is, they might call any four-legged creature they see a dog. This is assimilation, as the children are fitting new information into their existing knowledge. However, as children learn the differences between, say, a dog and cat, they can adjust their schema to accommodate this new knowledge (Heick, 2019).

Disequilibrium and accommodation can be uncomfortable. People might be confused or anxious when they encounter information that does not fit their existing schema, and they might struggle to accommodate that new information, but disequilibrium is crucial to learning (Kretchmar, 2019a). During assimilation, people might be adding new bits of information to their knowledge store, but they are not changing their understanding of the world. During accommodation, as people change their schema, construct new knowledge, and draw new connections among existing areas of knowledge, actual learning occurs, and accommodation requires disequilibrium.

Acknowledging the role of disequilibrium is important for both instructors and students. People naturally want to avoid discomfort, but that can also mean avoiding real learning. As instructors, we can facilitate accommodation by acknowledging that the process might be challenging, and by creating conditions that allow students to feel safe exploring new information. We can reassure learners that feelings of discomfort or anxiety are normal and provide them with low-stakes opportunities to engage with new information.

Social Constructivism

Social constructivism builds on the traditions of constructivism and cognitivism; whereas those theories focus on how individuals process information and construct meaning, social constructivists also consider how people’s interactions with others impact their understanding of the world. Social constructivists recognize that different people can have different reactions and develop different understandings from the same events and circumstances, and are interested in how factors such as identity, family, community, and culture help shape those understandings (Mercadal, 2018).While cognitivists and constructivists view other people as mostly incidental to an individual’s learning, social constructivists see community as central. Social constructivism can be defined as “the belief that the meanings attached to experience are socially assembled, depending on the culture in which the child is reared and on the child’s caretakers” (Schaffer, 2006). Like constructivism, social constructivism centers on the learners’ experiences and engagement, and sees the role of the instructor as a facilitator or guide. Two of the major theorists associated with social constructivism are Pierre Bourdieu and Lev Vygotsky.

Vygotsky built on the work of Piaget and believed knowledge is constructed, but felt that prior theories overemphasized the role of the individual in that construction of knowledge. Instead, he “was most interested in the role of other people in the development and learning processes of children,” including how children learn in cooperation with adults and older or more experienced peers who can guide them with more complex concepts (Kretchmar, 2019b). Vygotsky was also interested in how language and learning are related. He postulated that the ways in which people communicate their thoughts and understandings, even when talking themselves through a concept or problem, are a crucial element of learning (Kretchmar, 2019b). For Vygotsky, interaction and dialogue among students, teachers, and peers are key to how learners develop an understanding of the world and of the socially constructed meanings of their communities.

Bourdieu examined the way in which social structures influence people’s values, knowledge, and beliefs, and how these structures often become so ingrained as to be invisible. People within a society become so enculturated into the systems and beliefs of that society that they often accept them as “normal” and do not see them as imposed structures (Roth, 2018). As a result, individuals might not question or challenge those structures, even when they are unfair or oppressive. In addition to examining how community and culture help shape knowledge, Bourdieu was interested in how issues of class impact learning. He observed that over time, schools developed to reflect the cultures of wealthier families, which enabled their children to succeed because they inherently understood the culture of the classroom and the system of education. We continue to see such issues today, and as discussed more in Chapter 5 and Chapter 6, part of our critical practice is to ensure that our classrooms and instructional strategies are inclusive of and responsive to all students.

Activity 3.3 explores how we can use theory to guide our practice.

Activity 3.3: Using Learning Theory to Plan Lessons

While learning theories can be interesting on their own, our goal as instructors is to apply them to classroom practice. Imagine that you are a high school librarian working with a class that has just been assigned a research paper. Your goal for this session is for students to brainstorm keywords and synonyms for their topics, and to learn how to string those words together using the Boolean operators and , or , and not . You want to be sure the students understand the function of the Boolean operators and can remember how to use them for future searches.

Choose one of the learning theories outlined in this chapter and design a brief lesson to teach Boolean operators from the perspective of that theory. Concentrate less on what you would teach but rather on how you would teach it in keeping with the chosen theory:

• How would you introduce the topic?
• What sort of learning activities would you use?
• What would you be doing during the lesson? What would you expect students to do?
• How might any of your answers to these questions change if you were to use a different theory as your guide?

Developmental Stages

The learning theories outlined above discuss various cognitive processes involved in learning, as well as some of the motivators and conditions that facilitate learning. While these theories attempt to describe how people learn, it is important to note that individuals are not born ready to engage in all of these processes at once, nor do they necessarily all engage in the same processes at the same time. Rather, more complex processes develop over time as people experience the world and as their brain matures. In addition to studying how people learn, some theorists have also proposed theories or frameworks to describe developmental stages, or the various points in human development when different cognitive processes are enabled, and different kinds of learning can occur.

Piaget outlined four hierarchical stages of cognitive development: sensorimotor, preoperational, concrete operational, and formal operational (Clouse, 2019), illustrated in Table 3.1. In the sensorimotor stage, from birth to about two years, infants react to their environment with inherent reflexes such as sucking, swallowing, and crying. By about age two, they begin problem solving using trial and error. The preoperational stage, also sometimes called the intuitive intelligence stage, lasts from about ages two to seven. During this time, children develop language and mental imagery. They are able to use their imagination, but they view the world only from their own perspective and have trouble understanding other perspectives. Their understanding of the world during this stage is tied to their perceptions. Children are in the operational stage from about ages seven to 12, during which time they begin to think more logically about the world, can understand that objects are not always as they appear, and begin to understand other people’s perspectives. The final stage, formal operationalism, begins around age 12. At this point, individuals can think abstractly and engage in ideas that move beyond the concrete world around them, and they can use deductive reasoning and think through consequences (Clark, 2018; Clouse, 2019).

Table 3.1: Piaget’s Four Stages of Cognitive Development

Perry’s (1970) Scheme of Intellectual and Moral Development offers another useful framework for understanding the developmental stages of learning. Perry proposed four stages of learning. In the first stage, dualism, children generally believe that all problems can be solved, and that there are right and wrong answers to each question. At this stage, children generally look to instructors to provide them with correct answers. The second stage is multiplicity, where learners realize that there are conflicting views and controversies on topics. Learners in the multiplicity stage often have trouble assessing the authority and credibility of arguments. They tend to believe that all perspectives are equally valid and rely on their own experiences to form opinions and decide what information to trust. In the next stage, referred to as relativism, learners begin to understand that there are different lenses for understanding and evaluating information. They learn that different disciplines have their own methods of research and analysis, and they can begin to apply these perspectives as they evaluate sources and evidence. At this point, learners can understand that not all answers or perspectives are equal, but that some answers or arguments might be more valid than others. In the final stage, commitment, students integrate selected information into their knowledge base. You might notice connections between Perry and the cognitivists and constructivists described above in the way they each describe people making sense of information by comparing new information to existing knowledge. However, Perry organizes the processes into developmental stages that outline a progression of learning.

Understanding the stages laid out by Piaget and Perry, we can develop lessons that are appropriate to learners at each stage. For example, in presenting a lesson on climate change to preoperational students using Piaget’s framework, an instructor could gather pictures of different animal habitats, or take children on a nature walk to observe the surrounding environment. Instructors could ask these children to describe what they see and reflect on their personal experiences with weather, while older children could be asked to imagine how the changes are impacting other people and organisms, anticipate consequences of the impact of climate change, and perhaps use problem solving to propose steps to improve their environment. Considering Perry’s Scheme, instructors might guide students from multiplicity to relativism by explaining scientific methods for measuring climate, and challenging learners to evaluate and compare different sources of information to determine which presents the strongest evidence.

Piaget and Perry offer developmental models that outline stages broadly aligned with a person’s age. Both models assume a relatively linear chronological development, with children and young adults passing through different stages at roughly the same time. Vygotsky, on the other hand, describes a model that focuses more on the content being mastered rather than the age of the student. According to Vygotsky’s theory, known as Zone of Proximal Development (ZPD), as learners acquire new knowledge or develop new skills, they pass through three stages, often illustrated as concentric circles, as in Figure 3.2. The center circle, or first zone, represents tasks that the learner can do on their own. The second zone, or the Zone of Proximal Development, represents an area of knowledge or set of tasks that the learner can accomplish with assistance. The tasks and knowledge in this zone require students to stretch their abilities somewhat beyond their current skill level but are not so challenging as to be completely frustrating. The outermost circle, or third zone, represents tasks that the learner cannot yet do. Vygotsky posits that by working within the ZPD, learners can continue to grow their skills and abilities and increase their knowledge (Flair, 2019).

Figure 3.2: The Zone of Proximal Development

Whereas Piaget and Perry’s theories suggest that learners pass through the same stages at roughly the same time, Vygotsky maintains that the ZPD, or the zone of learning that will appropriately challenge the learner, is different for each student, depending on their background knowledge, experience, and ability (Flair, 2019). The same individual can experience different ZPDs in different subject areas; they might be advanced in math and able to take on material above their grade level but might find languages more challenging. Like with social constructivism, interaction with others is central to ZPD. According to Vygotsky, learning takes place when students interact with others who are more knowledgeable, including peers and instructors, who can provide guidance in the ZPD (Schaffer, 2006).

Math can provide a good example of working within the ZPD. Once students are comfortable with addition, they can probably learn subtraction with some help from a teacher or other peers but are probably not ready to learn long division. Our challenge as instructors is to identify the ZPD for each student so that we are neither boring learners with material that is too easy nor overwhelming them with material that is too hard. Chapter 7 discusses methods for assessing learners’ background knowledge to help determine the appropriate level of learning.

Most of the educational theories and frameworks outlined in this chapter were developed with a focus on children and young adults. While many of the principles can apply to an adult audience, they do not necessarily account for the specific issues, challenges, and motivations of adult learners. Yet, many information professionals will work mostly or even exclusively with adults. Academic librarians and archivists largely work with students who are at least 17 years old and, as the numbers of nontraditional students continue to increase, will find themselves increasingly working with older learners. Likewise, information professionals in corporations and medical and legal settings work almost exclusively with adults. Public librarians see a range of patrons, and many public libraries are increasing educational programming for their adult patrons. This section presents the educational concept of andragogy, which addresses teaching and learning for adults.

Knowles proposed andragogy as “the art and science of helping adults learn” (1988, p. 43). Andragogy is based on a set of assumptions about the ways in which adult learners’ experience, motivations, and needs differ from those of younger students, and suggests that traditional classroom approaches developed with younger students in mind will not necessarily be successful with adult learners. Perhaps one of the biggest differences between child and adult learners, according to Knowles (1988), is that adults are interested in the immediate applicability of what they are learning and are often motivated by their social roles as employees, parents, and so on. As Knowles notes, in traditional classrooms, children are usually taught discrete subjects like math, reading, and history, and their learning is focused on building up knowledge for the future. Young students might not use geometry in their everyday lives, but it forms a foundation for more complex math and for future job or life tasks like measuring materials for home repairs.

Adults, on the other hand, are already immersed in the social roles for which younger students are only preparing, and they want to see how their learning applies to those roles. Thus, Knowles suggests that adults will be interested in a competency-based, rather than a subject-based, approach to learning. Further, as autonomous individuals, adults are likely to be more self-directed in their learning. That is, they will want to, and should be encouraged to, take an active part in the design and planning of lessons, providing input on content and goals. Finally, Knowles also argues that adults’ wider experience and larger store of knowledge should be a resource for learning.

Knowles (1988, p. 45) organized his approach around four assumptions of adult learners:

• Their self-concept moves from one of being a dependent personality toward a self-directed human being.
• They accumulate a growing reservoir of experience that becomes an increasingly rich resource for learning.
• Their readiness to learn becomes oriented increasingly to the developmental tasks of their social roles.
• Their time perspective changes from one of postponed application of knowledge to immediacy of application, and, accordingly, their orientation toward learning shifts from one of subject-centeredness to one of performance-centeredness.

Later, he elaborated with two additional assumptions, summed up by Merriam et al. (2007):

• The most potent motivations are internal rather than external.
• Adults need to know why they need to learn something.

Certain understandings follow from Knowles’ assumptions that we can use to guide our practice with adult learners. To begin with, we should recognize and respect adults’ tendency to be self-motivated and self-directed learners. After all, in most states, school attendance is compulsory up to a certain age, and relatively strict curriculum standards are set by each state, meaning that children have little choice about attending school in some form or about what content they learn. At least in theory, adults have a choice about whether to attend college or engage in other kinds of learning opportunities such as workshops and professional development and continuing education courses. Presumably, adults are motivated to pursue these opportunities for a specific reason, whether out of personal curiosity, to advance in their careers, or to gain a new skill. These adult learners will likely have opinions and ideas about what they want to learn and perhaps even how they want to engage with the content, so Knowles suggests we provide adult learners with choices and opportunities for input to help shape the curriculum.

Adult learners also have a larger store of knowledge and experience than their younger counterparts. From a cognitivist or constructivist point of view, adults have a larger schema against which to compare new information and make new connections. As instructors, we should recognize this store of knowledge and find ways to integrate it into the classroom, by providing ample opportunity for reflection and using guiding questions to encourage learners to draw on that knowledge. We can approach adult learners as peers or co-learners, acting more as coaches or facilitators in the learning process than as the more directive teacher associated with a traditional school classroom. This focus on learner-centered approaches and a democratic environment overlaps with humanistic and constructivist approaches to teaching.

Points three, four, and six in Knowles’ list of assumptions underscore the importance of relevance and transparency for adult learners. Knowles suggests that adults have different priorities in learning, perhaps in part because they are learning by choice and are in a better position to direct their own learning. Adult learners also tend to have more demands on their time than younger students; they may have families and jobs that impact the time they have to devote to their studies. Thus, adult learners want to see the applicability of what they are learning and might be resistant to work or information that seems incidental. We should be transparent with our adult students, both about what they will learn and how that learning is important and relevant. Sharing learning goals is an important step toward transparency, as it can help set expectations so that students understand the purpose of the lesson and activities. To illustrate relevance, we can provide concrete examples of how the learning can be applied in practice. One could argue that all students, not just adults, deserve transparency and to see the relevance of lesson goals and learning. Knowles’ point is that adults are more likely to expect, and perhaps appreciate, such transparency.

While some controversy exists over whether andragogy really constitutes a theory per se or is more a set of guiding principles or best practices, the assumptions provide helpful guidance to instructors not just in how they organize content but also in how they frame the lesson and its purposes. Based on these assumptions, we can take certain steps to set an appropriate environment for adult education (Bartle, 2019):

• Set a cooperative learning climate.
• Create mechanisms for input.
• Arrange for a diagnosis of learner needs and interests.
• Enable the formulation of learning objectives based on the diagnosed needs and interests.
• Design sequential activities for achieving the objectives.
• Execute the design by selecting methods, materials, and resources.
• Evaluate the quality of the learning experience while rediagnosing needs for further learning.

As noted above, andragogy overlaps with other theories such as humanism and constructivism, and some of the principles of andragogy, like transparency, would benefit all learners. Still, this framework is useful in reminding instructors that adult learners likely have different priorities and motivations, and thus some differences in classroom approach might be warranted.

In addition to how people learn, we should also know something about why people learn. What motivates a student to put the time and effort into learning a skill or topic, and what can we do to cultivate that motivation? Svinicki (2004) offers an intriguing model that amalgamates some of the prevailing theories of motivation in learning. She suggests that motivation is a factor of the perceived value of the learning, along with students’ belief in their own self-efficacy, or their belief in their ability to achieve the goal. As Svinicki explains, “motivation involves a constant balancing of these two factors of value and expectations for success” (2004, p. 146). Most of the learning theories outlined above address motivation implicitly or explicitly. For instance, behaviorists talk in terms of reinforcement, or external motivators, as students strive to avoid negative consequences and achieve the rewards of good work. Humanists, on the other hand, focus on the internal motivation of self-actualization. As instructors, we can create environments to increase our learners’ motivation or their perception of the value of the goal and their self-efficacy:

• Emphasize the relevance of the material.  As outlined in the section on andragogy, learners are motivated when they see the benefits of learning and understand why the material is important. Instructors should explain how the effort individuals put into learning can help them achieve personal goals, such as getting a good grade on a paper or finding a job.
• Make the material appropriately challenging.  Reminiscent of the Zone of Proximal Development, material that is too easy will be boring for learners, while material that is too challenging will be overwhelming and frustrating.
• Give learners a sense of choice and control.  Choice allows learners to have a stake in the class, while control helps them determine the level of risk they will take and thus increase their confidence. We can foster choice and control by allowing learners options in the types of activities and assignments they engage in, or in the topics they research.
• Set learners up for success. Clear expectations for the class or the assignment help learners understand what a successful performance or project looks like. By providing meaningful feedback, we can guide learners toward success.
• Guide self-assessment.  When learners accurately assess their current level of knowledge and skill, they can make reasonable predictions of the likelihood of their success with the current material.

Activity 3.4 offers an opportunity to reflect on motivation in learning.

Activity 3.4: What Motivates You?

Think back on learning experiences such as courses or workshops where you felt more or less motivated as a learner. These experiences could be related to academics, hobbies, sports, or other interests.

• In the experiences in which you felt motivated, what steps did the instructor take that helped you feel motivated?
• In the experiences where you felt less motivated, what could the instructor have done differently?
• In each case, what role did self-efficacy, or your confidence in your own abilities, play?

Growth Mindset

Dweck’s (2016) mindset theory has gained much attention in the field of education over the last few decades and has some implications for student motivation. Although this theory is somewhat different in its conceptualizations than those described in the rest of this chapter, it is included here both because of its popularity and because it provides interesting insight into how instructors can coach learners to understand and build on their potential. Dweck’s theory is less about how people learn and more about how their attitude toward learning and their self-concept can impact their ability and willingness to learn. According to Dweck, people tend to approach learning with a fixed mindset or a growth mindset. Those with more of a fixed mindset tend to believe that ability is innate; either people are born with a certain talent and ability, or they are not. If individuals are not born with natural ability in a certain area, they would waste time working on that area because they will never truly be successful. People with more of a growth mindset, on the other hand, tend to believe that ability is the outcome of hard work and effort. These people see value in working at areas in which they are not immediately successful because they believe they can improve. Even when they are good at something, they are willing to continue to work at it because they believe they can continue to get better (Dweck, 2016).

These mindsets can have a profound impact on how a person approaches learning (Dweck, 2016). People with a fixed mindset will view low grades or poor test performance as a sign of their lack of natural ability and are likely to become discouraged. They might try to avoid that subject altogether or resign themselves to failure because they do not believe that practice or study will help them improve. Instead, they will tend to stick to subjects in which they already perform well. People with a growth mindset take an opposite view. They tend to view low grades or poor performance as a diagnostic tool that helps them see where they need to concentrate their efforts in order to get better. They are willing to put in extra effort because they believe that their hard work will lead to improved performance. They are also willing to take risks because they understand that failure is just part of the process of learning. We can see connections between Dweck’s theory and Piaget’s argument that the discomfort of disequilibrium is necessary to learning.

Understandably, people with a growth mindset are usually more successful learners because they believe in their own ability to learn and grow. Luckily, Dweck maintains that these mindsets themselves are not necessarily immutable. That is, a person with a fixed mindset can be coached to adopt a growth mindset. Learners can begin by recognizing when they are engaging in fixed mindset thinking, for instance when getting anxious about mistakes or telling themselves that they are “no good” at something. Once learners understand that this thinking is counterproductive, they can change their thinking to adopt a more encouraging voice.

Importantly, Dweck notes that encouraging a growth mindset in the classroom does not mean lowering standards for learning. She maintains that instructors should have high standards but also create a supportive and nurturing atmosphere. To begin with, instructors themselves must believe that learning and growth are possible, and not give up on students who are struggling. Instructors can model this belief for students by replacing fixed mindset feedback with growth mindset feedback. For example, Dweck suggests that if learners are struggling, instructors can respond by telling them they have not succeeded yet. The word “yet” implies that they will achieve the necessary learning; they just need to keep working at it. In that way, instructors can reframe mistakes and struggles as opportunities to learn rather than as failures. Instructors should encourage and appreciate effort as well as learning. In other words, rather than focusing only on a student’s achievement, instructors can praise the effort and hard work that led to that achievement. At the same time, Dweck (2015) notes that a growth mindset is not just about effort. In addition to putting in the work, learners must also be willing to try different strategies and be open to feedback on their performance. The goal is to help students view challenges as part of the learning process and to work with them rather than to fear or avoid them.

Learning theories are meant to help instructors understand the processes and circumstances that enable learning and, by extension, offer guidance in developing activities and environments that best support learning. But what to make of the fact that there are so many different theories and that some contradict each other? The truth is that the human brain and its cognitive processes are incredibly complex and not yet fully understood. Learning theorists do their best to describe how people learn based on careful observation and experimentation, but no learning theory is perfect. Indeed, each theory has its critics, and the various theories go in and out of favor over time. Even so, the theories provide us with an empirically based understanding of how learning occurs.

Further, these theories are not mutually exclusive. We do not have to strictly adhere to one theory but can combine elements across theories in ways that resonate with our teaching styles and reflect our best understanding of our students. For instance, a teacher might draw on elements of cognitivism to enhance students’ retention and recall but also develop group activities that promote social constructivism through peer-to-peer communication. Especially with younger children, instructors might draw on behaviorism by using rewards and positive reinforcement to motivate student engagement with the content, but also integrate humanism by empathizing with students and use constructive feedback to encourage a growth mindset. We can use our understanding of developmental stages to create lessons and activities that provide an appropriate level of challenge to help students grow in their understanding. Ultimately, we should view learning theories as guidelines, not rules, and draw on them in ways that reflect our own values and understandings.

Keeping this idea of learning across theories in mind, we can sum up the key takeaways from this chapter:

• Learning is the change in knowledge, behavior, or understanding that occurs when people make connections between new information and their existing knowledge. Various theories attempt to describe the factors that enable the learning process.
• Learning does not happen in the same way or at the same time for all students. Understanding developmental stages can help instructors align instruction with student readiness. Adult learners may have needs and constraints that differ from younger learners.
• The learning process is influenced by internal factors such as the student’s level of motivation and feelings of self-efficacy, and external factors such as the classroom environment and the adults and peers with whom the learner interacts.
• Creating a democratic, empathetic, and supportive learning environment
• Assisting students in becoming self-directed learners and enhancing their motivation by offering a sense of control and choice in their learning
• Acknowledging that learning can be challenging, and helping students develop the mindset and self-efficacy that will support their persistence
• Offering regular and meaningful feedback

Suggested Readings

Brown, P. C., Roediger, H. L. III, & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Belknap Press.

Brown, Roediger, and McDaniel present an engaging and accessible overview of current research in cognitive psychology. In addition to the science, the authors offer clear examples of how recommended recall and retrieval practices can be integrated into teaching.

Cooke, N. A. (2010). Becoming an andragogical librarian: Using library instruction as a tool to combat library anxiety and empower adult learners. New Review of Academic Librarianship, 16 (2), 208-227. https://doi.org/10.1080/13614533.2010.507388

This article offers a thorough overview of andragogy and the characteristics and motivators of adult learners and offers library-specific advice for teaching adult students.

Curtis, J. A. (2019). Teaching adult learners: A guide for public librarians . Libraries Unlimited.

Curtis provides a clear introduction to andragogy to contextualize instruction in public libraries. She also addresses issues of culture and generational differences in teaching adults. Covering many aspects of instruction, including developing learning objects and teaching online, this book is valuable as one of the few to focus exclusively on issues of teaching and learning in public libraries.

Dweck, C. S. (2016). Mindset: The new psychology of success (Updated ed.). Penguin Random House.

In this book, Dweck defines fixed and growth mindsets and how they can influence people’s feelings of motivation and self-efficacy in learning. She also offers guidance on how to facilitate the development of a growth mindset for better learning.

Freire, P. (2000). Pedagogy of the oppressed (30th Anniversary Edition). Bloomsbury.

In this foundational work, Freire presents the concept of the banking model of education. This book provides a social justice foundation for a humanistic approach to education.

Merriam, S. B., & Bierema, L. L. (2014).  Adult learning: Linking theory and practice . Jossey-Bass.

The authors provide a clear, concise, and engaging overview of both traditional and current theories of adult learning. The book includes activities and concrete examples for implementing the theories in the classroom.

Roy, L., & Novotny, E. (2000). How do we learn? Contributions of learning theory to reference services and library instruction. Reference Librarian, 33 (69/70), 129-139. https://doi.org/10.1300/J120v33n69_13

The authors provide an overview of some of the major learning theories, followed by specific ideas and advice for applying the theory to reference and library instruction.

Svinicki, M. D. (2004). Learning and motivation in the postsecondary classroom . Bolton, MA: Anker Publishing.

This book takes a student-centered approach to describing learning theory. Chapter 7 provides an excellent overview of motivation and self-efficacy, including implications for practice.

Bartle, S. M. (2019). Andragogy. In Salem press encyclopedia . EBSCO.

Brown, P. C., Roediger, H. L. III, & McDaniel, M.A. (2014). Make it stick: The science of successful learning . Belknap Press.

Clark, K. R. (2018). Learning theories: Cognitivism. Radiologic Technology, 90 (2), 176-179.

Clouse, B. (2019). Jean Piaget. In Salem press biographical encyclopedia . EBSCO.

Codington-Lacerte, C. (2018). Cognitivism. Salem press encyclopedia . EBSCO.

Dweck, C. S. (2015, September 22). Carol Dweck revisits the “growth mindset.” Education Week, 35 (5), 20-24. https://www.edweek.org/ew/articles/2015/09/23/carol-dweck-revisits-the-growth-mindset.html

Flair, I. (2019). Zone of proximal development (ZPD). Salem press encyclopedia . EBSCO

Gagné, R. M. (1985). The conditions of learning and theory of instruction . Wadsworth Publishing.

Heick, T. (2019, October 28). The assimilation vs accommodation of knowledge. teachthought . https://teachthought.com/learning/assimilation-vs-accommodation-of-knowledge/

Jensen, R. (2018). Behaviorism. Salem press encyclopedia of health . EBSCO.

Knowles, M. S. (1988). The modern practice of adult education: From pedagogy to andragogy. Revised and updated . Cambridge, The Adult Education Company.

Kretchmar, J. (2019a). Constructivism. Salem press encyclopedia . EBSCO.

Kretchmar, J. (2019b). Gagné’s conditions of learning. Salem press encyclopedia . EBSCO.

Kuhlthau, C. C. (1990). The information search process: From theory to practice. Journal of Education for Library and Information Science, 31 (1), 72-75. https://doi.org/10.2307/40323730

Lucas, C. J. (1996). Humanism. In J. J. Chambliss (Ed.),  Philosophy of education: An encyclopedia . Routledge.

Madsen, S. R., & Wilson, I. K. (2012). Humanistic theory of learning: Maslow. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning . Springer.

Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50 (4), 370-396.

McLeod, S. A. (2015). Cognitive approach in psychology . Simply Psychology . http://www.simplypsychology.org/cognitive.html

Mellon, C. A. (1986). Library anxiety: A grounded theory and its development. College & Research Libraries, 47 (2), 160-165. https://doi.org/10.5860/crl.76.3.276

Mercadal, T. (2018). Social constructivism. Salem press encyclopedia . EBSCO.

Merriam, S. B., Caffarella, R. S., & Baumgartner, L. M. (2007). Learning in adulthood: A comprehensive guide (3rd edition) . Wiley.

Perry, W. G., Jr. (1970). Forms of intellectual and ethical development in the college years; A scheme. Holt.

Popp, J. A. (1996). Learning, theories of. In J. J. Chambliss (Ed.),  Philosophy of education: An encyclopedia . Routledge.

Roth, A. L. (2018). Pierre Bourdieu. Salem press biographical encyclopedia . EBSCO.

Shaffer, R. H. (2006). Key concepts in developmental psychology . Sage UK.

Sharp, A. (2012). Humanistic approaches to learning. In N.M. Seel (Ed.), Encyclopedia of the Sciences of Learning . Springer.

Skinner, B. F. (1938).  The Behavior of organisms: An experimental analysis . Appleton-Century.

Svinicki, M. D. (2004). Learning and motivation in the postsecondary classroom . Anker Publishing.

Zucca-Scott, L. (2010). Know thyself: The importance of humanism in education. International Education, 40 (1), 32-38.

Instruction in Libraries and Information Centers Copyright © 2020 by Laura Saunders and Melissa A. Wong is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Insight Learning Theory: Definition, Stages, and Examples

Categories Learning

Insight learning theory is all about those “lightbulb moments” we experience when we suddenly understand something. Instead of slowly figuring things out through trial and error, insight theory says we can suddenly see the solution to a problem in our minds. 

This theory is super important because it helps us understand how our brains work when we learn and solve problems. It can help teachers find better ways to teach and improve our problem-solving skills and creativity. It’s not just useful in school—insight theory also greatly impacts science, technology, and business.

Table of Contents

What Is Insight Learning?

Insight learning is like having a lightbulb moment in your brain. It’s when you suddenly understand something without needing to go through a step-by-step process. Instead of slowly figuring things out by trial and error, insight learning happens in a flash. One moment, you’re stuck, and the next, you have the solution. 

This type of learning is all about those “aha” experiences that feel like magic. The key principles of insight learning involve recognizing patterns, making connections, and restructuring our thoughts. It’s as if our brains suddenly rearrange the pieces of a puzzle, revealing the big picture. So, next time you have a brilliant idea pop into your head out of nowhere, you might just be experiencing insight learning in action!

Three Components of Insight Learning Theory

Insight learning, a concept rooted in psychology, comprises three distinct properties that characterize its unique nature:

1. Sudden Realization

Unlike gradual problem-solving methods, insight learning involves sudden and profound understanding. Individuals may be stuck on a problem for a while, but then, seemingly out of nowhere, the solution becomes clear. This sudden “aha” moment marks the culmination of mental processes that have been working behind the scenes to reorganize information and generate a new perspective .

2. Restructuring of Problem-Solving Strategies

Insight learning often involves a restructuring of mental representations or problem-solving strategies . Instead of simply trying different approaches until stumbling upon the correct one, individuals experience a shift in how they perceive and approach the problem. This restructuring allows for a more efficient and direct path to the solution once insight occurs.

3. Aha Moments

A hallmark of insight learning is the experience of “aha” moments. These moments are characterized by a sudden sense of clarity and understanding, often accompanied by a feeling of satisfaction or excitement. It’s as if a mental lightbulb turns on, illuminating the solution to a previously perplexing problem. 

These moments of insight can be deeply rewarding and serve as powerful motivators for further learning and problem-solving endeavors.

Four Stages of Insight Learning Theory

Insight learning unfolds in a series of distinct stages, each contributing to the journey from problem recognition to the sudden realization of a solution. These stages are as follows:

1. Problem Recognition

The first stage of insight learning involves recognizing and defining the problem at hand. This may entail identifying obstacles, discrepancies, or gaps in understanding that need to be addressed. Problem recognition sets the stage for the subsequent stages of insight learning by framing the problem and guiding the individual’s cognitive processes toward finding a solution.

2. Incubation

After recognizing the problem, individuals often enter a period of incubation where the mind continues to work on the problem unconsciously. During this stage, the brain engages in background processing, making connections, and reorganizing information without the individual’s conscious awareness. 

While it may seem like a period of inactivity on the surface, incubation is a crucial phase where ideas gestate, and creative solutions take shape beneath the surface of conscious thought.

3. Illumination

The illumination stage marks the sudden emergence of insight or understanding. It is characterized by a moment of clarity and realization, where the solution to the problem becomes apparent in a flash of insight. 

This “aha” moment often feels spontaneous and surprising, as if the solution has been waiting just below the surface of conscious awareness to be revealed. Illumination is the culmination of the cognitive processes initiated during problem recognition and incubation, resulting in a breakthrough in understanding.

4. Verification

Following the illumination stage, individuals verify the validity and feasibility of their insights by testing the proposed solution. This may involve applying the solution in practice, checking it against existing knowledge or expertise, or seeking feedback from others. 

Verification serves to confirm the efficacy of the newfound understanding and ensure its practical applicability in solving the problem at hand. It also provides an opportunity to refine and iterate on the solution based on real-world feedback and experience.

Famous Examples of Insight Learning

Examples of insight learning can be observed in various contexts, ranging from everyday problem-solving to scientific discoveries and creative breakthroughs. Some well-known examples of how insight learning theory works include the following:

Archimedes’ Principle

According to legend, the ancient Greek mathematician Archimedes experienced a moment of insight while taking a bath. He noticed that the water level rose as he immersed his body, leading him to realize that the volume of water displaced was equal to the volume of the submerged object. This insight led to the formulation of Archimedes’ principle, a fundamental concept in fluid mechanics.

Köhler’s Chimpanzee Experiments

In Wolfgang Köhler’s experiments with chimpanzees on Tenerife in the 1920s, the primates demonstrated insight learning in solving novel problems. One famous example involved a chimpanzee named Sultan, who used sticks to reach bananas placed outside his cage. After unsuccessful attempts at using a single stick, Sultan suddenly combined two sticks to create a longer tool, demonstrating insight into the problem and the ability to use tools creatively.

Eureka Moments in Science

Many scientific discoveries are the result of insight learning. For instance, the famed naturalist Charles Darwin had many eureka moments where he gained sudden insights that led to the formation of his influential theories.

Everyday Examples of Insight Learning Theory

You can probably think of some good examples of the role that insight learning theory plays in your everyday life. A few common real-life examples include:

• Finding a lost item : You might spend a lot of time searching for a lost item, like your keys or phone, but suddenly remember exactly where you left them when you’re doing something completely unrelated. This sudden recollection is an example of insight learning.
• Untangling knots : When trying to untangle a particularly tricky knot, you might struggle with it for a while without making progress. Then, suddenly, you realize a new approach or see a pattern that helps you quickly unravel the knot.
• Cooking improvisation : If you’re cooking and run out of a particular ingredient, you might suddenly come up with a creative substitution or alteration to the recipe that works surprisingly well. This moment of improvisation demonstrates insight learning in action.
• Solving riddles or brain teasers : You might initially be stumped when trying to solve a riddle or a brain teaser. However, after some time pondering the problem, you suddenly grasp the solution in a moment of insight.
• Learning a new skill : Learning to ride a bike or play a musical instrument often involves moments of insight. You might struggle with a certain technique or concept but then suddenly “get it” and experience a significant improvement in your performance.
• Navigating a maze : While navigating through a maze, you might encounter dead ends and wrong turns. However, after some exploration, you suddenly realize the correct path to take and reach the exit efficiently.
• Remembering information : When studying for a test, you might find yourself unable to recall a particular piece of information. Then, when you least expect it, the answer suddenly comes to you in a moment of insight.

These everyday examples illustrate how insight learning is a common and natural part of problem-solving and learning in our daily lives.

Exploring the Uses of Insight Learning

Insight learning isn’t an interesting explanation for how we suddenly come up with a solution to a problem—it also has many practical applications. Here are just a few ways that people can use insight learning in real life:

Problem-Solving

Insight learning helps us solve all sorts of problems, from finding lost items to untangling knots. When we’re stuck, our brains might suddenly come up with a genius idea or a new approach that saves the day. It’s like having a mental superhero swoop in to rescue us when we least expect it!

Ever had a brilliant idea pop into your head out of nowhere? That’s insight learning at work! Whether you’re writing a story, composing music, or designing something new, insight can spark creativity and help you come up with fresh, innovative ideas.

Learning New Skills

Learning isn’t always about memorizing facts or following step-by-step instructions. Sometimes, it’s about having those “aha” moments that make everything click into place. Insight learning can help us grasp tricky concepts, master difficult skills, and become better learners overall.

Insight learning isn’t just for individuals—it’s also crucial for innovation and progress in society. Scientists, inventors, and entrepreneurs rely on insight to make groundbreaking discoveries and develop new technologies that improve our lives. Who knows? The next big invention could start with someone having a brilliant idea in the shower!

Overcoming Challenges

Life is full of challenges, but insight learning can help us tackle them with confidence. Whether it’s navigating a maze, solving a puzzle, or facing a tough decision, insight can provide the clarity and creativity we need to overcome obstacles and achieve our goals.

The next time you’re feeling stuck or uninspired, remember: the solution might be just one “aha” moment away!

Alternatives to Insight Learning Theory

While insight learning theory emphasizes sudden understanding and restructuring of problem-solving strategies, several alternative theories offer different perspectives on how learning and problem-solving occur. Here are some of the key alternative theories:

Behaviorism

Behaviorism is a theory that focuses on observable, overt behaviors and the external factors that influence them. According to behaviorists like B.F. Skinner, learning is a result of conditioning, where behaviors are reinforced or punished based on their consequences. 

In contrast to insight learning theory, behaviorism suggests that learning occurs gradually through repeated associations between stimuli and responses rather than sudden insights or realizations.

Cognitive Learning Theory

Cognitive learning theory, influenced by psychologists such as Jean Piaget and Lev Vygotsky , emphasizes the role of mental processes in learning. This theory suggests that individuals actively construct knowledge and understanding through processes like perception, memory, and problem-solving. 

Cognitive learning theory acknowledges the importance of insight and problem-solving strategies but places greater emphasis on cognitive structures and processes underlying learning.

Gestalt Psychology

Gestalt psychology, which influenced insight learning theory, proposes that learning and problem-solving involve the organization of perceptions into meaningful wholes or “gestalts.” 

Gestalt psychologists like Max Wertheimer emphasized the role of insight and restructuring in problem-solving, but their theories also consider other factors, such as perceptual organization, pattern recognition, and the influence of context.

Information Processing Theory

Information processing theory views the mind as a computer-like system that processes information through various stages, including input, processing, storage, and output. This theory emphasizes the role of attention, memory, and problem-solving strategies in learning and problem-solving. 

While insight learning theory focuses on sudden insights and restructuring, information processing theory considers how individuals encode, manipulate, and retrieve information to solve problems.

Related reading:

• What Is Kolb’s Learning Cycle?
• What Is Latent Learning?
• What Is Scaffolding in Psychology?
• What Is Observational Learning?

Kizilirmak, J. M., Fischer, L., Krause, J., Soch, J., Richter, A., & Schott, B. H. (2021). Learning by insight-like sudden comprehension as a potential strategy to improve memory encoding in older adults .  Frontiers in Aging Neuroscience ,  13 , 661346. https://doi.org/10.3389/fnagi.2021.661346

Lind, J., Enquist, M. (2012). Insight learning and shaping . In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning . Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_851

Osuna-Mascaró, A. J., & Auersperg, A. M. I. (2021). Current understanding of the “insight” phenomenon across disciplines . Frontiers in Psychology , 12, 791398. https://doi.org/10.3389/fpsyg.2021.791398

Salmon-Mordekovich, N., & Leikin, M. (2023). Insight problem solving is not that special, but business is not quite ‘as usual’: typical versus exceptional problem-solving strategies .  Psychological Research ,  87 (6), 1995–2009. https://doi.org/10.1007/s00426-022-01786-5

Why Every Educator Needs to Teach Problem-Solving Skills

Strong problem-solving skills will help students be more resilient and will increase their academic and career success .

Want to learn more about how to measure and teach students’ higher-order skills, including problem solving, critical thinking, and written communication?

Problem-solving skills are essential in school, careers, and life.

Problem-solving skills are important for every student to master. They help individuals navigate everyday life and find solutions to complex issues and challenges. These skills are especially valuable in the workplace, where employees are often required to solve problems and make decisions quickly and effectively.

Problem-solving skills are also needed for students’ personal growth and development because they help individuals overcome obstacles and achieve their goals. By developing strong problem-solving skills, students can improve their overall quality of life and become more successful in their personal and professional endeavors.

Problem-Solving Skills Help Students…

   develop resilience.

Problem-solving skills are an integral part of resilience and the ability to persevere through challenges and adversity. To effectively work through and solve a problem, students must be able to think critically and creatively. Critical and creative thinking help students approach a problem objectively, analyze its components, and determine different ways to go about finding a solution.  

This process in turn helps students build self-efficacy . When students are able to analyze and solve a problem, this increases their confidence, and they begin to realize the power they have to advocate for themselves and make meaningful change.

When students gain confidence in their ability to work through problems and attain their goals, they also begin to build a growth mindset . According to leading resilience researcher, Carol Dweck, “in a growth mindset, people believe that their most basic abilities can be developed through dedication and hard work—brains and talent are just the starting point. This view creates a love of learning and a resilience that is essential for great accomplishment.”

    Set and Achieve Goals

Students who possess strong problem-solving skills are better equipped to set and achieve their goals. By learning how to identify problems, think critically, and develop solutions, students can become more self-sufficient and confident in their ability to achieve their goals. Additionally, problem-solving skills are used in virtually all fields, disciplines, and career paths, which makes them important for everyone. Building strong problem-solving skills will help students enhance their academic and career performance and become more competitive as they begin to seek full-time employment after graduation or pursue additional education and training.

  Resolve Conflicts

In addition to increased social and emotional skills like self-efficacy and goal-setting, problem-solving skills teach students how to cooperate with others and work through disagreements and conflicts. Problem-solving promotes “thinking outside the box” and approaching a conflict by searching for different solutions. This is a very different (and more effective!) method than a more stagnant approach that focuses on placing blame or getting stuck on elements of a situation that can’t be changed.

While it’s natural to get frustrated or feel stuck when working through a conflict, students with strong problem-solving skills will be able to work through these obstacles, think more rationally, and address the situation with a more solution-oriented approach. These skills will be valuable for students in school, their careers, and throughout their lives.

    Achieve Success

We are all faced with problems every day. Problems arise in our personal lives, in school and in our jobs, and in our interactions with others. Employers especially are looking for candidates with strong problem-solving skills. In today’s job market, most jobs require the ability to analyze and effectively resolve complex issues. Students with strong problem-solving skills will stand out from other applicants and will have a more desirable skill set.

In a recent opinion piece published by The Hechinger Report , Virgel Hammonds, Chief Learning Officer at KnowledgeWorks, stated “Our world presents increasingly complex challenges. Education must adapt so that it nurtures problem solvers and critical thinkers.” Yet, the “traditional K–12 education system leaves little room for students to engage in real-world problem-solving scenarios.” This is the reason that a growing number of K–12 school districts and higher education institutions are transforming their instructional approach to personalized and competency-based learning, which encourage students to make decisions, problem solve and think critically as they take ownership of and direct their educational journey.

Problem-Solving Skills Can Be Measured and Taught

Research shows that problem-solving skills can be measured and taught. One effective method is through performance-based assessments which require students to demonstrate or apply their knowledge and higher-order skills to create a response or product or do a task.

What Are Performance-Based Assessments?

With the No Child Left Behind Act (2002), the use of standardized testing became the primary way to measure student learning in the U.S. The legislative requirements of this act shifted the emphasis to standardized testing, and this led to a  decline in nontraditional testing methods .

But   many educators, policy makers, and parents have concerns with standardized tests. Some of the top issues include that they don’t provide feedback on how students can perform better, they don’t value creativity, they are not representative of diverse populations, and they can be disadvantageous to lower-income students.

While standardized tests are still the norm, U.S. Secretary of Education Miguel Cardona is encouraging states and districts to move away from traditional multiple choice and short response tests and instead use performance-based assessment, competency-based assessments, and other more authentic methods of measuring students abilities and skills rather than rote learning. 

Performance-based assessments  measure whether students can apply the skills and knowledge learned from a unit of study. Typically, a performance task challenges students to use their higher-order skills to complete a project or process. Tasks can range from an essay to a complex proposal or design.

Preview a Performance-Based Assessment

Want a closer look at how performance-based assessments work?  Preview CAE’s K–12 and Higher Education assessments and see how CAE’s tools help students develop critical thinking, problem-solving, and written communication skills.

Performance-Based Assessments Help Students Build and Practice Problem-Solving Skills

In addition to effectively measuring students’ higher-order skills, including their problem-solving skills, performance-based assessments can help students practice and build these skills. Through the assessment process, students are given opportunities to practically apply their knowledge in real-world situations. By demonstrating their understanding of a topic, students are required to put what they’ve learned into practice through activities such as presentations, experiments, and simulations. 

This type of problem-solving assessment tool requires students to analyze information and choose how to approach the presented problems. This process enhances their critical thinking skills and creativity, as well as their problem-solving skills. Unlike traditional assessments based on memorization or reciting facts, performance-based assessments focus on the students’ decisions and solutions, and through these tasks students learn to bridge the gap between theory and practice.

Performance-based assessments like CAE’s College and Career Readiness Assessment (CRA+) and Collegiate Learning Assessment (CLA+) provide students with in-depth reports that show them which higher-order skills they are strongest in and which they should continue to develop. This feedback helps students and their teachers plan instruction and supports to deepen their learning and improve their mastery of critical skills.

Explore CAE’s Problem-Solving Assessments

CAE offers performance-based assessments that measure student proficiency in higher-order skills including problem solving, critical thinking, and written communication.

• College and Career Readiness Assessment (CCRA+) for secondary education and
• Collegiate Learning Assessment (CLA+) for higher education.

Our solution also includes instructional materials, practice models, and professional development.

We can help you create a program to build students’ problem-solving skills that includes:

• Measuring students’ problem-solving skills through a performance-based assessment    
• Using the problem-solving assessment data to inform instruction and tailor interventions
• Teaching students problem-solving skills and providing practice opportunities in real-life scenarios
• Supporting educators with quality professional development

Get started with our problem-solving assessment tools to measure and build students’ problem-solving skills today! These skills will be invaluable to students now and in the future.

Ready to Get Started?

Learn more about cae’s suite of products and let’s get started measuring and teaching students important higher-order skills like problem solving..

Insight Learning (Definition+ 4 Stages + Examples)

Have you ever been so focused on a problem that it took stepping away for you to figure it out? You can’t find the solution when you’re looking at all of the moving parts, but once you get distracted with something else - “A-ha!” you have it. 

When a problem cannot be solved by applying an obvious step-by-step solving sequence,   Insight learning occurs when the mind rearranges the elements of the problem and finds connections that were not obvious in the initial presentation of the problem. People experience this as a sudden A-ha moment.

Humans aren’t the only species that have these “A-ha” moments. Work with other species helped psychologists understand the definition and stages of Insight Learning. This video is going to break down those stages and how you can help to move these “a-ha” moments along. 

What Is Insight Learning? 

Insight learning is a process that leads to a sudden realization regarding a problem. Often, the learner has tried to understand the problem, but steps away before the change in perception occurs. Insight learning is often compared to trial-and-error learning, but it’s slightly different.

Rather than just trying different random solutions, insight learning requires more comprehension. Learners aim to understand the relationships between the pieces of the puzzle. They use patterns, organization, and past knowledge to solve the problem at hand. 

Is Insight Learning Only Observed In Humans? 

Humans aren’t the only species that learn with insight. Not all species use this process - just the ones that are closest to us intellectually. Insight learning was first discovered not by observing humans, but by observing chimps. 

In the early 1900s, Wolfgang Köhler observed chimpanzees as they solved problems. Köhler’s most famous subject was a chimp named Sultan. The psychologist gave Sultan two sticks of different sizes and placed a banana outside of Sultan’s cage. He watched as Sultan looked at the sticks and tried to reach for the banana with no success. Eventually, Sultan gave up and got distracted. But it was during this time that Köhler noticed Sultan having an “epiphany.” The chimp went back to the sticks, placed one inside of the other, and used this to bring the banana to him. 

Since Köhler’s original observations took place, psychologists looked deeper into the insight process and when you are more likely to experience that “a-ha” moment. There isn’t an exact science to insight learning, but certain theories suggest that some places are better for epiphanies than others. 

Four Stages of Insight Learning 

But how does insight learning happen? Multiple models have been developed, but the four-stage model is the most popular. The four stages of insight learning are preparation, incubation, insight, and verification. 

Preparation

The process begins as you try to solve the problem. You have the materials and information in front of you and begin to make connections. Although you see the relationships between the materials, things just haven’t “clicked” yet. This is the stage where you start to get frustrated. 

During the incubation period, you “give up” for a short period of time. Although you’ve abandoned the project, your brain is still making connections on an unconscious level. 

When the right connections have been made in your mind, the “a-ha” moment occurs. Eureka! You have an epiphany! 

Verification

Now, you just have to make sure that your epiphany is right. You test out your solution and hopefully, it works! This is a great moment in your learning journey. The connections you make solving this problem are likely to help you in the future. 

Examples of Insight Learning

Insight learning refers to the sudden realization or understanding of a solution to a problem without the need for trial-and-error attempts. It's like a "light bulb" moment when things suddenly make sense. Here are some examples of insight learning:

• The Matchstick Problem : Realizing you can light a match and use it to illuminate a dark room instead of fumbling around in the dark.
• Sudoku Puzzles : Suddenly seeing a pattern or number placement that you hadn't noticed before, allowing you to complete the puzzle.
• The Two Rope Problem : In an experiment, a person is given two ropes hanging from the ceiling and is asked to tie them together. The solution involves swinging one rope like a pendulum and grabbing it with the other.
• Opening Jars : After struggling to open a jar, you remember you can tap its lid lightly or use a rubber grip to make it easier.
• Tangram Puzzles : Suddenly realizing how to arrange the geometric pieces to complete the picture without any gaps.
• Escape Rooms : Having an "aha" moment about a clue that helps you solve a puzzle and move to the next challenge.
• The Nine Dot Problem : Connecting all nine dots using only four straight lines without lifting the pen.
• Cooking : Realizing you can soften butter quickly by grating it or placing it between two sheets of parchment paper and rolling it.
• Math Problems : Suddenly understanding a complex math concept or solution method after pondering it for a while.
• Guitar Tuning : Realizing you can use the fifth fret of one string to tune the next string.
• Traffic Routes : Discovering a faster or more efficient route to your destination without using a GPS.
• Packing Suitcases : Figuring out how to fit everything by rolling clothes or rearranging items in a specific order.
• The Crow and the Pitcher : A famous Aesop's fable where a thirsty crow drops pebbles into a pitcher to raise the water level and drink.
• Computer Shortcuts : Discovering a keyboard shortcut that makes a task you frequently do much quicker.
• Gardening : Realizing you can use eggshells or coffee grounds as a natural fertilizer.
• Physics Problems : After struggling with a concept, suddenly understanding the relationship between two variables in an equation.
• Art : Discovering a new technique or perspective that transforms your artwork.
• Sports : Realizing a different way to grip a tennis racket or baseball bat that improves your game.
• Language Learning : Suddenly understanding the grammar or pronunciation rule that was previously confusing.
• DIY Projects : Figuring out a way to repurpose old items in your home, like using an old ladder as a bookshelf.

Where Is the Best Place to Have an Epiphany? 

But what if you want to have an epiphany? You’re stuck on a problem and you can’t take it anymore. You want to abandon it, but you’re not sure what you should do for this epiphany to take place. Although an “a-ha” moment isn’t guaranteed, studies suggest that the following activities or places can help you solve a tough problem. 

The Three B’s of Creativity 

Creativity and divergent thinking are key to solving problems. And some places encourage creativity more than others. Researchers believe that you can kickstart divergent thinking with the three B’s: bed, bath, and the bus. 

Sleep 

“Bed” might be your best bet out of the three. Studies show that if you get a full night’s sleep, you will be twice as likely to solve a problem than if you stay up all night. This could be due to the REM sleep that you get throughout the night. During REM sleep , your brain is hard at work processing the day’s information and securing connections. Who knows - maybe you’ll dream up the answer to your problems tonight!

Meditation 

The word for “insight” in the Pali language is vipassana. If you have ever been interested in meditation , you might have seen this word before. You can do a vipassana meditation at home, or you can go to a 10-day retreat. These retreats are often silent and are set up to cultivate mind-body awareness. 

You certainly don’t have to sign up for a 10-day silent retreat to solve a problem that is bugging you. (Although, you may have a series of breakthroughs!) Try meditating for 20 minutes at a time. Studies show that this can increase the likelihood of solving a problem. 

Laugh! 

How do you feel when you have an epiphany? Good, right? The next time you’re trying to solve a problem, check in with your emotions. You are more likely to experience insight when you’re in a positive mood. Positivity opens your mind and gives your mind more freedom to explore. That exploration may just lead you to your solution. 

Be patient when you’re trying to solve problems. Take breaks when you need to and make sure that you are taking care of yourself. This approach will help you solve problems faster and more efficiently!

Insight Vs. Other Types Of Learning.

Learning by insight is  not  learning by trial and  error, nor by observation  and imitation. Learning by insight is a learning theory accepted by the Gestalt  school of psychology, which disagrees with the behaviorist  school, which claims that all learning occurs through conditioning from the  external environment.

Gestalt is a German word that approximately translates as ‘an organized whole  that has properties  and elements in addition to the sum of its parts .’ By viewing a problem as a ‘gestalt’ , the learner does not simply react to whatever she observes at the moment. She also imagines elements that could be present but are not and uses her imagination to combine parts of the problem that are presently not so combined in fact.  

Insight Vs. Trial And Error Learning

Imagine yourself in a maze-running competition. You and your rivals each have 10 goes. The first one to run the maze successfully wins $500. You may adopt a trial-and-error strategy, making random turning decisions and remembering whether those particular turns were successful or not for your next try. If you have a good memory and with a bit of luck, you will get to the exit and win the prize.

Completing the maze through trial and error requires no insight. If you had to run a different maze, you would have no advantage over running previous mazes with different designs. You have now learned to run this particular maze as predicted by behaviorist psychologists. External factors condition your maze running behavior. The cash prize motivates  you to run the maze in the first place. All maze dead ends act as punishments , which you remember not to repeat. All correct turns act as rewards , which you remember to repeat.

If you viewed the maze running competition as a gestalt, you might notice that it doesn't explicitly state in the competition rules that you must run along the designated paths to reach the exit.

Suppose you further noticed that the maze walls were made from cardboard. In that case, you may combine those 2 observations in your imagination and realize that you could just punch big holes in the walls or tear them down completely, to see around corners and directly run to the now visible exit.

Insight Vs. Learning Through Observation, Imitation, And Repetition

Observation, imitation, and repetition are at the heart of training. The violin teacher shows you how to hold your bow correctly; you practice your scales countless times before learning to play a sonata from Beethoven flawlessly. Mastering a sport or a musical instrument rarely comes from a flash of insight but a lot of repetition and error correction from your teacher.

Herbert Lawford, the Scottish tennis player, and 1887 Wimbledon champion, is credited for being the first player to play a topspin. Who could have taught it to him? Who could he have imitated? One can only speculate since no player at that time was being coached on how to hit topspin.

He could have only learned to play a topspin by having a novel insight. One possibility is that he played one by accident during training, by mistakenly hitting the ball at a flatter angle than normal. He could then have observed that his opponent was disorientated by the flatter and quicker bounce of the ball and realized the benefit of his ‘mistake’ .

Behaviorist theories of learning can probably explain how most successful and good tennis players are produced, but you need a Gestalt insight learning theory to explain Herbert Lawford.

Another interesting famous anecdote illustrating insight learning concerned Carl Friedrich Gauss when he was a 7-year-old pupil at school. His mathematics teacher seems to have adopted strict behaviorism in his teaching since the original story implies that he beat students with a switch.

One day the teacher set classwork requiring the students to add up all the numbers from 1 to 100. He expected his pupils to perform this calculation in how they were trained. He expected it to be a laborious and time-consuming task, giving him a long break. In just a few moments, young Gauss handed in the correct answer after having to make at most 2 calculations, which are easy to do in your head. How did he do it? Gauss saw the arithmetic sequence as a gestalt instead of adding all the numbers one at a time: 1+2+3+4…. +99+100 as he expected.

He realized that by breaking this sequence in half at 50, then snaking the last number (100) under the first number (1), and then adding the 2 halves of the arithmetic sequence like so:  

    1         +        2       +        3      +       4      +       5         +    ………….      +        48        +        49             +       50

100        +       99       +      98      +     97      +      96       +    …………...    +        53        +         52           +       51

101        +      101      +    101     +    101      +     101     +   …………….     +     101        +       101           +     101    

Arranged in this way, each number column adds up to 101, so all Gauss needed to do was calculate 50 x 101 = 5050.

Can Major Scientific Breakthroughs be made through observation and experiment alone?

Science is unapologetically an evidence-based inquiry. Observations, repeatable experiments, and hard, measurable data must support theories and explanations.

Since countless things can be observed and comparisons made, they cannot be done randomly for observations and experiments to advance knowledge. They must be guided by a good question and a  testable hypothesis. Before performing actual experiments and observations, scientists often first perform thought experiments . They think of ideal situations by imagining ways things could be or imaging away things that are.

Atoms were talked about long before electron microscopes could observe them. How could atoms be seriously discussed in ancient Greece long before the discoveries of modern chemistry? Pre-Socratic philosophers were puzzled by a purely philosophical problem, which they termed the problem of the one and many .  

People long observed that the world was made of many different things that didn't remain static but continuously changed into other various things. For example, a seed different from a tree changed into a tree over time. Small infants change into adults yet remain the same person. Boiling water became steam, and frozen water became ice.

Observing all of this in the world, philosophers didn’t simply take it for granted and aimed to profit from it practically through stimulus-response and trial and error learning. They were puzzled by how the world fit together as a whole.

To make sense of all this observable changing multiplicity, one needed to imagine an unobservable sameness behind it all. Yet, there is no obvious or immediate punishment or reward. Therefore, there seems to be no satisfying behaviorist reason behind philosophical speculations.

Thinkers such as Empedocles and Aristotle made associations between general properties in the world wetness, dryness, temperature, and phases of matter as follows:

• Earth :  dry, cold     
• Fire:  dry, hot
• Water:  wet, cold
• Air: hot or wet, depending on whether moisture or heat prevails in the atmosphere.

These 4 primitive elements transformed and combined give rise to the diversity we see in the world. However, this view was still too sensually based  to provide the world with sought-for coherence and unity. How could a multiplicity of truly basic stuff interact? Doesn't such an interaction presuppose something more fundamental in common?

The ratio of these 4 elements was thought to affect the properties of things. Stone contained more earth, while a rabbit had more water and fire, thus making it soft and giving it life. Although this theory correctly predicted that seemingly basic things like stones were complex compounds, it had some serious flaws.

For example, if you break a stone in half many times, the pieces never resemble fire, air, water, or earth.   

To account for how different things could be the same on one level and different on another level, Leucippus and his student Democritus reasoned that all things are the same in that they were made from some common primitive indivisible stuff but different due to the different ways or patterns in which this indivisible stuff or atoms could be arranged.

For atoms to be able to rearrange and recombine into different patterns led thinkers to the insight that if the atom idea was true, then logically, there had to be free spaces between the atoms for them to shift into. They had to imagine a vacuum, another phenomenon not directly observable since every nook and cranny in the world seems to be filled with some liquid, solid, or gas.  

This ancient notion of vacuum proved to be more than just a made-up story since it led to modern practical applications in the form of vacuum cleaners and food vacuum packing.

This insight that atoms and void exist makes no sense from a behaviorist learning standpoint. It cannot be explained in terms of stimulus-response or environmental conditioning and made no practical difference in the lives of ancient Greeks.  

For philosophers to feel compelled to hold onto notions, which at the time weren’t directly useful, it suggests that they must have felt some need to understand the universe as an intelligible ‘gestalt’ One may even argue that the word Cosmos, from the Greek word Kosmos, which roughly translates to ‘harmonious arrangement’ is at least a partial synonym.  

The Historical Development Of The Theory of Insight Learning

Wolfgang Kohler , the German gestalt psychologist, is credited for formulating the theory of insight learning, one of the first cognitive learning theories. He came up with the theory while first conducting experiments  in 1913 on 7 chimpanzees  on the island of Tenerife to observe how they learned to solve problems.

In one experiment, he dangled a banana from the top of a high cage. Boxes and poles were left in the cage with the chimpanzees. At first, the chimps used trial and error to get at the banana. They tried to jump up to the banana without success. After many failed attempts, Kohler noticed that they paused to think  for a while.  

After some time, they behaved more methodically by stacking the boxes on top of each other, making a raised platform from which they could swipe at the banana using the available poles. Kohler believed that chimps, like humans, were capable of experiencing flashes of insight, just like humans.

In another experiment, he placed a peanut down a long narrow tube attached to the cage's outer side. The chimpanzee tried scooping the peanut out with his hand and fingers, but to no avail, since the tube was too long and narrow. After sitting down to think, the chimp filled its mouth with water from a nearby water container in the cage and spat it into the tube.

The peanut floated up the tube within the chimp's reach. What is essential is that the chimp realized it could use water as a tool in a flash of insight, something it had never done before or never shown how  to do .  Kohler's conclusions contrasted with those of American psychologist Edward Thorndike , who, years back, conducted learning experiments on cats, dogs, and monkeys.

Through his experiments and research, Thorndike concluded that although there was a vast difference in learning speed and potential between monkey dogs and cats, he concluded that all animals, unlike humans, are not capable of genuine reasoned thought. According to him, Animals can only learn through stimulus-response conditioning, trial and error, and solve problems accidentally.

Kohler’s 4 Stage Model Of Insight Learning

From his observations of how chimpanzees solve complex problems, he concluded that the learning process went through the following 4 stages:

• Preparation:  Learners encounter the problem and begin to survey all relevant information and materials. They process stimuli and begin to make connections.
• Incubation: Learners get frustrated and may even seem to observers as giving up. However, their brains carry on processing information unconsciously.
• Insight: The learner finally achieves a breakthrough, otherwise called an epiphany or ‘Aha’ moment. This insight comes in a flash and is often a radical reorganization of the problem. It is a discontinuous leap in understanding rather than continuous with reasoning undertaken in the preparation phase.
• Verification: The learner now formally tests the new insight and sees if it works in multiple different situations. Mathematical insights are formally proved.

The 2 nd  and 3 rd  stages of insight learning are well described in anecdotes of famous scientific breakthroughs. In 1861, August Kekulé was contemplating the structure of the Benzene molecule. He knew it was a chain of 6 carbon atoms attached to 6 hydrogen atoms. Still, He got stuck   (incubation phase)  on working out how they could fit together to remain chemically stable.

He turned away from his desk and, facing the fireplace, fell asleep. He dreamt of a snake eating its tail and then spinning around. He woke up and realized (insight phase)  that these carbon-hydrogen chains can close onto themselves to form hexagonal rings. He then worked out the consequences of his new insight on Benzene rings. (Verification phase)

Suitably prepared minds can experience insights while observing ordinary day-to-day events. Many people must have seen apples fall from trees and thought nothing of it. When Newton saw an apple fall, he connected its fall to the action of the moon. If an unseen force pulls the apple from the tree top, couldn't the same force extends to the moon? This same force must be keeping the moon tethered in orbit around the earth, keeping it from whizzing off into space. Of course, this seems counterintuitive because if the moon is like the apple, should it not be crashing down to earth?

Newton's prepared mind understood the moon to be continuously falling to earth around the horizon's curve. Earth's gravitational pull on the moon balanced its horizontal velocity tangential to its orbit. If the apple were shot fast enough over the horizon from a cannon, it too, like the moon, would stay in orbit.

So, although before Newton, everyone was aware of gravity in a stimulus-response kind of way and even made practical use of it to weigh things, no one understood its universal implications.

Applying Insight Learning To The Classroom

The preparation-incubation-insight- verification cycle could be implemented by teachers in the classroom. Gestalt theory predicts that students learn best when they engage with the material; they are mentally prepared  for age, and maturity, having had experiences enabling them to relate to the material and having background knowledge that allows them to contextualize the material. When first presenting content they want to teach the students, teachers must make sure students are suitably prepared to receive the material, to successfully go through the preparation stage of learning.

Teachers should present the material holistically and contextually. For example, when teaching about the human heart, they should also teach where it is in the human body and its functional importance and relationship to other organs and parts of the body. Teachers could also connect other fields, such as comparing hearts to mechanical pumps.

Once the teacher has imparted sufficient background information to students, they should set a problem for their students to solve independently or in groups. The problem should require the students to apply what they have learned in a new way and make novel connections not explicitly made by the teacher during the lesson.

However, they must already know and be familiar with all the material they need to solve the problem. Students must be allowed to fumble their way to a solution  and make many mistakes , as this is vital for the incubation phase. The teacher should resist the temptation to spoon-feed them. Instead, teachers should use the Socratic method to coax the students into arriving at solutions and answers themselves.

Allowing the students to go through a sufficiently challenging incubation phase engages all their higher cognitive functions, such as logical and abstract reasoning, visualization, and imagination. It also habituates them to a bit of frustration to build the mental toughness to stay focused.

It also forces their brains to work hard in processing combining information to sufficiently own the insights they achieve, making it more likely that they will retain  the knowledge they gained and be able to apply it across different contexts.

Once students have written down their insights and solutions, the teacher should guide them through the verification phase. The teacher and students need to check and test the validity of the answers. Solutions should be checked for errors and inconsistencies and checked against the norms and standards of the field.

However, one should remember that mass education is aimed at students of average capacity and that not all students are always equally capable of learning through insight. Also, students need to be prepared to gain the ability and potential to have fruitful insights.  

Learning purely from stimulus-response conditioning is insufficient for progress and major breakthroughs to be made in the sciences. For breakthroughs to be made, humans need to be increasingly capable of higher and higher levels of abstract thinking.

However, we are not all equally capable of having epiphanies on the cutting edge of scientific research. Most education aims to elevate average reasoning, knowledge, and skill acquisition. For insight, learning must build on rather than replace behaviorist teaching practices.

Related posts:

• The Psychology of Long Distance Relationships
• Beck’s Depression Inventory (BDI Test)
• Operant Conditioning (Examples + Research)
• Variable Interval Reinforcement Schedule (Examples)
• Concrete Operational Stage (3rd Cognitive Development)

Reference this article:

About The Author

Operant Conditioning

Classical Conditioning

Observational Learning

Latent Learning

Experiential Learning

The Little Albert Study

Bobo Doll Experiment

Spacing Effect

Von Restorff Effect

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Problem Solving

Cognitive processing aimed at figuring out how to achieve a goal is called problem solving. In problem solving, the problem solver seeks to devise a method for transforming a problem from its current state into a desired state when a solution is not immediately obvious to the problem solver. Thus, the hallmark of problem solving is the invention of a new method for addressing a problem. This definition has three parts: (1) problem solving is cognitive –that is, it occurs internally in the mind (or cognitive system) and must be inferred indirectly from behavior; (2) problem solving is a process –it involves the manipulation of knowledge representations (or carrying out mental computations); and (3) problem solving is directed –it is guided by the goals of the problem solver.

The definition of problem solving covers a broad range of human cognitive activities, including educationally relevant cognition–figuring out how to manage one's time, writing an essay on a selected topic, summarizing the main point of a textbook section, solving an arithmetic word problem, or determining whether a scientific theory is valid by conducting experiments.

A problem occurs when a problem solver has a goal but initially does not know how to achieve the goal. This definition has three parts: (1) the current state –the problem begins in a given state; (2) the goal state –the problem solver wants the problem to be in a different state, and problem solving is required to transform the problem from the current (or given) state into the goal state, and (3) obstacles –the problem solver does not know the correct solution and an effective solution method is not obvious to the problem solver.

According to this definition a problem is personal, so that a situation that is a problem for one person might not be a problem for another person. For example, "3 + 5 = ___" might be a problem for a six-year-old child who reasons, "Let's see. I can take one from the 5 and give it to the 3. That makes 4 plus 4, and I know that 4 plus 4 is 8." However, this equation is not a problem for an adult who knows the correct answer.

Types of Problems

Routine and nonroutine problems. It is customary to distinguish between routine and nonroutine problems. In a routine problem, the problem solver knows a solution method and only needs to carry it out. For example, for most adults the problem "589 × 45 = ___" is a routine problem if they know the procedure for multicolumn multiplication. Routine problems are sometimes called exercises, and technically do not fit the definition of problem stated above. When the goal of an educational activity is to promote all the aspects of problem solving (including devising a solution plan), then nonroutine problems (or exercises) are appropriate.

In a nonroutine problem, the problem solver does not initially know a method for solving the problem. For example, the following problem (reported by Robert Sternberg and Janet Davidson) is nonroutine for most people: "Water lilies double in area every twenty-four hours. At the beginning of the summer, there is one water lily on the lake. It takes sixty days for the lake to be completely covered with water lilies. On what day is the lake half covered?" In this problem, the problem solver must invent a solution method based on working backwards from the last day. Based on this method, the problem solver can ask what the lake would look like on the day before the last day, and conclude that the lake is half covered on the fifty-ninth day.

Well-defined and ill-defined problems. It is also customary to distinguish between well-defined and ill-defined problems. In a well-defined problem, the given state of the problem, the goal state of the problem, and the allowable operators (or moves) are each clearly specified. For example, the following water-jar problem (adapted from Abrahama Luchins) is an example of a well defined problem: "I will give you three empty water jars; you can fill any jar with water and pour water from one jar into another (until the second jar is full or the first one is empty); you can fill and pour as many times as you like. Given water jars of size 21, 127, and 3 units and an unlimited supply of water, how can you obtain exactly 100 units of water?" This is a well-defined problem because the given state is clearly specified (you have empty jars of size 21, 127, and 3), the goal state is clearly specified (you want to get 100 units of water in one of the jars), and the allowable operators are clearly specified (you can fill and pour according to specific procedures). Well-defined problems may be either routine or nonroutine; if you do not have previous experience with water jar problems, then finding the solution (i.e., fill the 127, pour out 21 once, and pour out 3 twice) is a nonroutine problem.

In an ill-defined problem, the given state, goal state, and/or operations are not clearly specified. For example, in the problem, "Write a persuasive essay in favor of year-round schools," the goal state is not clear because the criteria for what constitutes a "persuasive essay" are vague and the allowable operators, such as how to access sources of information, are not clear. Only the given state is clear–a blank piece of paper. Ill-defined problems can be routine or nonroutine; if one has extensive experience in writing then writing a short essay like this one is a routine problem.

Processes in Problem Solving

The process of problem solving can be broken down into two major phases: problem representation, in which the problem solver builds a coherent mental representation of the problem, and problem solution, in which the problem solver devises and carries out a solution plan. Problem representation can be broken down further into problem translation, in which the problem solver translates each sentence (or picture) into an internal mental representation, and problem integration, in which the problem solver integrates the information into a coherent mental representation of the problem (i.e., a mental model of the situation described in the problem). Problem solution can be broken down further into solution planning, in which the problem solver devises a plan for how to solve the problem, and solution execution, in which the problem solver carries out the plan by engaging in solution behaviors. Although the four processes of problem solving are listed sequentially, they may occur in many different orderings and with many iterations in the course of solving a problem.

For example, consider the butter problem described by Mary Hegarty, Richard Mayer, and Christopher Monk: "At Lucky, butter costs 65 cents per stick. This is two cents less per stick than butter at Vons. If you need to buy 4 sticks of butter, how much will you pay at Vons?" In the problem translation phase, the problem solver may mentally represent the first sentence as "Lucky = 0.65," the second sentence as "Lucky = Vons - 0.02," and the third sentence as "4 × Vons = ___." In problem integration, the problem solver may construct a mental number line with Lucky at 0.65 and Vons to the right of Lucky (at 0.67); or the problem solver may mentally integrate the equations as "4 × (Lucky + 0.02) = ____." A key insight in problem integration is to recognize the proper relation between the cost of butter at Lucky and the cost of butter at Vons, namely that butter costs more at Vons (even though the keyword in the problem is "less"). In solution planning, the problem solver may break the problem into parts, such as: "First add 0.02 to 0.65, then multiply the result by 4." In solution executing, the problem solver carries out the plan: 0.02 + 0.65 =0.67, 0.67 × 4 = 2.68. In addition, the problem solver must monitor the problem-solving process and make adjustments as needed.

Teaching for Problem Solving

A challenge for educators is to teach in ways that foster meaningful learning rather than rote learning. Rote instructional methods promote retention (the ability to solve problems that are identical or highly similar to those presented in instruction), but not problem solving transfer (the ability to apply what was learned to novel problems). For example, in 1929, Alfred Whitehead used the term inert knowledge to refer to learning that cannot be used to solve novel problems. In contrast, meaningful instructional methods promote both retention and transfer.

In a classic example of the distinction between rote and meaningful learning, the psychologist Max Wertheimer (1959) described two ways of teaching students to compute the area of a parallelogram. In the rote method, students learn to measure the base, measure the height, and then multiply base times height. Students taught by the A = b × h method are able to find the area of parallelograms shaped like the ones given in instruction (a retention problem) but not unusual parallelograms or other shapes (a transfer problem). Wertheimer used the term reproductive thinking to refer to problem solving in which one blindly carries out a previously learned procedure. In contrast, in the meaningful method, students learn by cutting the triangle from one end of a cardboard parallelogram and attaching it to the other end to form a rectangle. Once students have the insight that a parallelogram is just a rectangle in disguise, they can compute the area because they already know the procedure for finding the area of a rectangle. Students taught by the insight method perform well on both retention and transfer problems. Wertheimer used the term productive thinking to refer to problem solving in which one invents a new approach to solving a novel problem.

Educationally Relevant Advances in Problem Solving

Recent advances in educational psychology point to the role of domain-specific knowledge in problem solving–such as knowledge of specific strategies or problem types that apply to a particular field. Three important advances have been: (1) the teaching of problem-solving processes, (2) the nature of expert problem solving, and (3) new conceptions of individual differences in problem-solving ability.

Teaching of problem-solving processes. An important advance in educational psychology is cognitive strategy instruction, which includes the teaching of problem-solving processes. For example, in Project Intelligence, elementary school children successfully learned the cognitive processes needed for solving problems similar to those found on intelligence tests. In Instrumental Enrichment, students who had been classified as mentally retarded learned cognitive processes that allowed them to show substantial improvements on intelligence tests.

Expert problem solving. Another important advance in educational psychology concerns differences between what experts and novices know in given fields, such as medicine, physics, and computer programming. For example, expert physicists tend to store their knowledge in large integrated chunks, whereas novices tend to store their knowledge as isolated fragments; expert physicists tend to focus on the underlying structural characteristics of physics word problems, whereas novices focus on the surface features; and expert physicists tend to work forward from the givens to the goal, whereas novices work backwards from the goal to the givens. Research on expertise has implications for professional education because it pinpoints the kinds of domain-specific knowledge that experts need to learn.

Individual differences in problem-solving ability. This third advance concerns new conceptions of intellectual ability based on differences in the way people process information. For example, people may differ in cognitive style–such as their preferences for visual versus verbal representations, or for impulsive versus reflective approaches to problem solving. Alternatively, people may differ in the speed and efficiency with which they carry out specific cognitive processes, such as making a mental comparison or retrieving a piece of information from memory. Instead of characterizing intellectual ability as a single, monolithic ability, recent conceptions of intellectual ability focus on the role of multiple differences in information processing.

See also: C REATIVITY ; L EARNING , subentry on A NALOGICAL R EASONING ; M ATHEMATICS L EARNING, subentry on C OMPLEX P ROBLEM S OLVING .

BIBLIOGRAPHY

C HI , M ICHELENE T. H.; G LASER , R OBERT ; and F ARR , M ARSHALL J., eds. 1988. The Nature of Expertise. Hillsdale, NJ: Erlbaum.

D UNKER , K ARL . 1945. On Problem Solving. Washington, DC: American Psychological Association.

F EUERSTEIN , R EUVEN . 1980. Instrumental Enrichment. Baltimore: University Park Press.

H EGARTY , M ARY ; M AYER , R ICHARD E.; and M ONK , C HRISTOPHER A. 1995. "Comprehension of Arithmetic Word Problems: Evidence from Students' Eye Fixations." Journal of Educational Psychology 84:76–84.

H UNT , E ARL ; L UNNEBORG , C LIFF ; and L EWIS , J. 1975. "What Does It Mean to Be High Verbal?" Cognitive Psychology 7:194–227.

L ARKIN , J ILL H.; M C D ERMOTT , J OHN ; S IMON , D OROTHEA P.; and S IMON , H ERBERT A. 1980. "Expert and Novice Performance in Solving Physics Problems." Science 208:1335–1342.

L UCHINS , A BRAHAMA S. 1942. Mechanization in Problem Solving: The Effect of Einstellung. Evanston, IL: American Psychological Association.

M AYER , R ICHARD E. 1992. Thinking, Problem Solving, Cognition, 2nd edition. New York: Freeman.

M AYER , R ICHARD E. 1999. The Promise of Educational Psychology. Upper Saddle River, NJ: Prentice-Hall.

N ICKERSON , R AYMOND S. 1995. "Project Intelligence." In Encyclopedia of Human Intelligence, ed. Robert J. Sternberg. New York: Macmillan.

P RESSLEY , M ICHAEL J., and W OLOSHYN , V ERA . 1995. Cognitive Strategy Instruction that Really Improves Children's Academic Performance. Cambridge, MA: Brookline Books.

S TERNBERG , R OBERT J., and D AVIDSON , J ANET E. 1982. "The Mind of the Puzzler." Psychology Today 16:37–44.

S TERNBERG , R OBERT J., and Z HANG , L I -F ANG , eds. 2001. Perspectives on Thinking, Learning, and Cognitive Styles. Mahwah, NJ: Erlbaum.

W ERTHEIMER , M AX . 1959. Productive Thinking. New York: Harper and Row.

W HITEHEAD , A LFRED N ORTH . 1929. The Aims of Education. New York: Macmillan.

R ICHARD E. M AYER

Additional topics

• Learning - Reasoning
• Learning - Perceptual Processes

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Guiding Students to Harness Mistakes for Learning

With practice and an eventual shift in mindset, students can understand that mistakes are fundamental to how we learn.

As students begin to build the skills they desire, such as solving early puzzles or making circles instead of scribbles, they often experience the frustration of not doing it “right.” Even when we assure them that there is no right or wrong when starting out, or that with practice they'll get better and better, many still suffer distress.

Students often have misunderstandings about mistakes. They may think that speed in comprehension represents knowledge or that mistakes are a sign of lesser intelligence.

For many students in school, their greatest fear is to make a mistake in front of their classmates and suffer a self-imposed humiliation. Let them know that all their classmates have the same fears. Help them understand that setbacks provide opportunities for them to revise their brains’ inaccurate memory circuits, which, if uncorrected, could impede future understanding. Working through periods of confusion strengthens the correct durable networks their brains ultimately construct. Allowing students to make mistakes and correct them with a positive attitude builds their understanding and solidifies accurate learning connections.

.css-1ynlp5m{position:relative;width:100%;height:56px;margin-bottom:30px;content:'';} .css-2tyqqs *{display:inline-block;font-family:museoSlab-500,'Arial Narrow','Arial','Helvetica','sans-serif';font-size:24px;font-weight:500;line-height:34px;-webkit-letter-spacing:0.8px;-moz-letter-spacing:0.8px;-ms-letter-spacing:0.8px;letter-spacing:0.8px;}.css-2tyqqs *{display:inline-block;font-family:museoSlab-500,'Arial Narrow','Arial','Helvetica','sans-serif';font-size:24px;font-weight:500;line-height:34px;-webkit-letter-spacing:0.8px;-moz-letter-spacing:0.8px;-ms-letter-spacing:0.8px;letter-spacing:0.8px;} An error recognized in homework, tests, or class participation may be disappointing, but with timely feedback and opportunities to build accurate memory, their brains rewire neural pathways with the faulty information and will avoid the same mistake next time. .css-1ycc0ui{display:inline-block !important;font-family:'canada-type-gibson','Arial','Verdana','sans-serif';font-size:14px;line-height:27px;-webkit-letter-spacing:0.8px;-moz-letter-spacing:0.8px;-ms-letter-spacing:0.8px;letter-spacing:0.8px;text-transform:uppercase;padding-top:24px;margin-bottom:0 !important;}.css-1ycc0ui::before{content:'—';margin-right:9px;color:black;font-size:inherit;} Judy Willis

Help students persevere through mistakes

Learning is a process of going from the unknown to the known and involves detours through uncertainty and mistakes. By encouraging students to think beyond single approaches and giving them opportunities to make decisions and mistakes, you help them build perseverance and mistake tolerance.

Once students have accomplished goals, reminding them of how they overcame challenges boosts their perseverance after mistakes. Help them recall the experiences when with effort and practice, they made fewer mistakes and enjoyed the pleasure of success. For example, “Remember when you were learning to play soccer and you kept trying even though you felt like giving up?” “Think back to when you struggled to play basic chords on the guitar, and now you have mastered so many!” “Do you remember how your first attempts to write were challenging and now it’s easy for you?” 

You can also promote opportunities for students to take the risk of making mistakes when you provide examples of people they admire, who have described their own struggles with mistakes. As Michael Jordan has said , “I’ve missed more than 9,000 shots in my career. I’ve lost almost 300 games. I’ve failed over and over and over again in my life. And that is why I succeed.” 

You expand perseverance and understanding with questions that have more than one correct answer. Try extending your wait time—don’t give the answers to their questions before all students have enough time to really consider the question and predict possible answers. Ask students questions where they need to explain their reasons and consider alternative or additional solutions .

Utilize Class Discussions

The power of peers is harnessed when you promote class discussions about mistakes. Start by describing some whopper mistakes that you’ve made and how your life went on even after these big mistakes. Invite students to share mistakes they made in the past and how they felt and reacted. Ask them what they would do differently now confronting similar issues.

Students can discuss examples such as these:

• Sending a text message or media posting without considering all possible outcomes
• Judging people too quickly from appearances or initial interactions
• Making preventable mistakes by starting an assignment before reading all the instructions
• Rushing through reading and finding they don’t remember what they read
• Choosing the first multiple-choice answer that seems right without looking at the other options that really included the most correct response 

Knowing that their peers have had similar experiences can help students feel less shame about mistakes—everyone makes them, and it’s OK.

Learning from mistakes leads to discovery

When “learning” is errorless and effortless, the acquisition of new knowledge is limited. To be true learners, students need opportunities to construct their understanding, in addition to making and revising mistakes along the way. 

Explain to students how learning from mistakes—understanding where they made the mistake—is powerful cement for their brains to construct the correct understanding and solutions. For example, an error recognized in homework, tests, or class participation may be disappointing, but with timely feedback and opportunities to build accurate memory, their brains rewire neural pathways with the faulty information and will avoid the same mistake next time.

This is because the brain has a system that promotes accurate and strong memories in response to mistakes, enhanced by timely feedback . Called the nucleus accumbens or reward center , this storage house of dopamine responds when making predictions, choices, or answers to questions. Through the nucleus accumbens, dopamine is released from its storage area, resulting in the cementing of accurate predictions and the opportunity to revise incorrect ones. 

This reward center is always sending a baseline flow of dopamine to the prefrontal cortex—the region where stored memories are assembled to solve a problem, answer a new question, or make a decision. When the nucleus accumbens gets timely feedback that a correct prediction (answer, choice, decision) was made, there is extra dopamine flow to that memory consolidation network in the prefrontal cortex. The resulting satisfying pleasure reinforces the network of stored memories that guided that correct prediction. When errors occur, the flow of dopamine drops, and the brain seeks to prevent that drop in the future. Thus, with timely corrective feedback, the drop in dopamine triggers the construction of more accurate memory circuits. 

Students Will See Mistakes as Opportunities

Learning from their mistakes now will help your students evolve into future learners perceiving problems as opportunities and help them to have perseverance to exceed the status quo. As they build mistake tolerance and tenacity through setbacks, they’ll view mistakes as opportunities that increase understanding and skills rather than as indicators of failure.

By building their power of perseverance through their inevitable setbacks, errors, and mistakes, you’ll help your students develop the blueprints needed to confidently manage and flourish through future challenges, solve new problems, and become creative innovators.

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5 Teaching Mathematics Through Problem Solving

Janet Stramel

In his book “How to Solve It,” George Pólya (1945) said, “One of the most important tasks of the teacher is to help his students. This task is not quite easy; it demands time, practice, devotion, and sound principles. The student should acquire as much experience of independent work as possible. But if he is left alone with his problem without any help, he may make no progress at all. If the teacher helps too much, nothing is left to the student. The teacher should help, but not too much and not too little, so that the student shall have a reasonable share of the work.” (page 1)

What is a problem  in mathematics? A problem is “any task or activity for which the students have no prescribed or memorized rules or methods, nor is there a perception by students that there is a specific ‘correct’ solution method” (Hiebert, et. al., 1997). Problem solving in mathematics is one of the most important topics to teach; learning to problem solve helps students develop a sense of solving real-life problems and apply mathematics to real world situations. It is also used for a deeper understanding of mathematical concepts. Learning “math facts” is not enough; students must also learn how to use these facts to develop their thinking skills.

According to NCTM (2010), the term “problem solving” refers to mathematical tasks that have the potential to provide intellectual challenges for enhancing students’ mathematical understanding and development. When you first hear “problem solving,” what do you think about? Story problems or word problems? Story problems may be limited to and not “problematic” enough. For example, you may ask students to find the area of a rectangle, given the length and width. This type of problem is an exercise in computation and can be completed mindlessly without understanding the concept of area. Worthwhile problems  includes problems that are truly problematic and have the potential to provide contexts for students’ mathematical development.

There are three ways to solve problems: teaching for problem solving, teaching about problem solving, and teaching through problem solving.

Teaching for problem solving begins with learning a skill. For example, students are learning how to multiply a two-digit number by a one-digit number, and the story problems you select are multiplication problems. Be sure when you are teaching for problem solving, you select or develop tasks that can promote the development of mathematical understanding.

Teaching about problem solving begins with suggested strategies to solve a problem. For example, “draw a picture,” “make a table,” etc. You may see posters in teachers’ classrooms of the “Problem Solving Method” such as: 1) Read the problem, 2) Devise a plan, 3) Solve the problem, and 4) Check your work. There is little or no evidence that students’ problem-solving abilities are improved when teaching about problem solving. Students will see a word problem as a separate endeavor and focus on the steps to follow rather than the mathematics. In addition, students will tend to use trial and error instead of focusing on sense making.

Teaching through problem solving  focuses students’ attention on ideas and sense making and develops mathematical practices. Teaching through problem solving also develops a student’s confidence and builds on their strengths. It allows for collaboration among students and engages students in their own learning.

Consider the following worthwhile-problem criteria developed by Lappan and Phillips (1998):

• The problem has important, useful mathematics embedded in it.
• The problem requires high-level thinking and problem solving.
• The problem contributes to the conceptual development of students.
• The problem creates an opportunity for the teacher to assess what his or her students are learning and where they are experiencing difficulty.
• The problem can be approached by students in multiple ways using different solution strategies.
• The problem has various solutions or allows different decisions or positions to be taken and defended.
• The problem encourages student engagement and discourse.
• The problem connects to other important mathematical ideas.
• The problem promotes the skillful use of mathematics.
• The problem provides an opportunity to practice important skills.

Of course, not every problem will include all of the above. Sometimes, you will choose a problem because your students need an opportunity to practice a certain skill.

Key features of a good mathematics problem includes:

• It must begin where the students are mathematically.
• The feature of the problem must be the mathematics that students are to learn.
• It must require justifications and explanations for both answers and methods of solving.

Problem solving is not a  neat and orderly process. Think about needlework. On the front side, it is neat and perfect and pretty.

But look at the b ack.

It is messy and full of knots and loops. Problem solving in mathematics is also like this and we need to help our students be “messy” with problem solving; they need to go through those knots and loops and learn how to solve problems with the teacher’s guidance.

When you teach through problem solving , your students are focused on ideas and sense-making and they develop confidence in mathematics!

Mathematics Tasks and Activities that Promote Teaching through Problem Solving

Choosing the Right Task

Selecting activities and/or tasks is the most significant decision teachers make that will affect students’ learning. Consider the following questions:

• Teachers must do the activity first. What is problematic about the activity? What will you need to do BEFORE the activity and AFTER the activity? Additionally, think how your students would do the activity.
• What mathematical ideas will the activity develop? Are there connections to other related mathematics topics, or other content areas?
• Can the activity accomplish your learning objective/goals?

Low Floor High Ceiling Tasks

By definition, a “ low floor/high ceiling task ” is a mathematical activity where everyone in the group can begin and then work on at their own level of engagement. Low Floor High Ceiling Tasks are activities that everyone can begin and work on based on their own level, and have many possibilities for students to do more challenging mathematics. One gauge of knowing whether an activity is a Low Floor High Ceiling Task is when the work on the problems becomes more important than the answer itself, and leads to rich mathematical discourse [Hover: ways of representing, thinking, talking, agreeing, and disagreeing; the way ideas are exchanged and what the ideas entail; and as being shaped by the tasks in which students engage as well as by the nature of the learning environment].

The strengths of using Low Floor High Ceiling Tasks:

• Allows students to show what they can do, not what they can’t.
• Provides differentiation to all students.
• Promotes a positive classroom environment.
• Advances a growth mindset in students
• Aligns with the Standards for Mathematical Practice

Examples of some Low Floor High Ceiling Tasks can be found at the following sites:

• YouCubed – under grades choose Low Floor High Ceiling
• NRICH Creating a Low Threshold High Ceiling Classroom
• Inside Mathematics Problems of the Month

Math in 3-Acts

Math in 3-Acts was developed by Dan Meyer to spark an interest in and engage students in thought-provoking mathematical inquiry. Math in 3-Acts is a whole-group mathematics task consisting of three distinct parts:

Act One is about noticing and wondering. The teacher shares with students an image, video, or other situation that is engaging and perplexing. Students then generate questions about the situation.

In Act Two , the teacher offers some information for the students to use as they find the solutions to the problem.

Act Three is the “reveal.” Students share their thinking as well as their solutions.

“Math in 3 Acts” is a fun way to engage your students, there is a low entry point that gives students confidence, there are multiple paths to a solution, and it encourages students to work in groups to solve the problem. Some examples of Math in 3-Acts can be found at the following websites:

• Dan Meyer’s Three-Act Math Tasks
• Graham Fletcher3-Act Tasks ]
• Math in 3-Acts: Real World Math Problems to Make Math Contextual, Visual and Concrete

Number Talks

Number talks are brief, 5-15 minute discussions that focus on student solutions for a mental math computation problem. Students share their different mental math processes aloud while the teacher records their thinking visually on a chart or board. In addition, students learn from each other’s strategies as they question, critique, or build on the strategies that are shared.. To use a “number talk,” you would include the following steps:

• The teacher presents a problem for students to solve mentally.
• Provide adequate “ wait time .”
• The teacher calls on a students and asks, “What were you thinking?” and “Explain your thinking.”
• For each student who volunteers to share their strategy, write their thinking on the board. Make sure to accurately record their thinking; do not correct their responses.
• Invite students to question each other about their strategies, compare and contrast the strategies, and ask for clarification about strategies that are confusing.

“Number Talks” can be used as an introduction, a warm up to a lesson, or an extension. Some examples of Number Talks can be found at the following websites:

• Inside Mathematics Number Talks
• Number Talks Build Numerical Reasoning

Saying “This is Easy”

“This is easy.” Three little words that can have a big impact on students. What may be “easy” for one person, may be more “difficult” for someone else. And saying “this is easy” defeats the purpose of a growth mindset classroom, where students are comfortable making mistakes.

When the teacher says, “this is easy,” students may think,

• “Everyone else understands and I don’t. I can’t do this!”
• Students may just give up and surrender the mathematics to their classmates.
• Students may shut down.

Instead, you and your students could say the following:

• “I think I can do this.”
• “I have an idea I want to try.”
• “I’ve seen this kind of problem before.”

Tracy Zager wrote a short article, “This is easy”: The Little Phrase That Causes Big Problems” that can give you more information. Read Tracy Zager’s article here.

Using “Worksheets”

Do you want your students to memorize concepts, or do you want them to understand and apply the mathematics for different situations?

What is a “worksheet” in mathematics? It is a paper and pencil assignment when no other materials are used. A worksheet does not allow your students to use hands-on materials/manipulatives [Hover: physical objects that are used as teaching tools to engage students in the hands-on learning of mathematics]; and worksheets are many times “naked number” with no context. And a worksheet should not be used to enhance a hands-on activity.

Students need time to explore and manipulate materials in order to learn the mathematics concept. Worksheets are just a test of rote memory. Students need to develop those higher-order thinking skills, and worksheets will not allow them to do that.

One productive belief from the NCTM publication, Principles to Action (2014), states, “Students at all grade levels can benefit from the use of physical and virtual manipulative materials to provide visual models of a range of mathematical ideas.”

You may need an “activity sheet,” a “graphic organizer,” etc. as you plan your mathematics activities/lessons, but be sure to include hands-on manipulatives. Using manipulatives can

• Provide your students a bridge between the concrete and abstract
• Serve as models that support students’ thinking
• Provide another representation
• Support student engagement
• Give students ownership of their own learning.

Adapted from “ The Top 5 Reasons for Using Manipulatives in the Classroom ”.

any task or activity for which the students have no prescribed or memorized rules or methods, nor is there a perception by students that there is a specific ‘correct’ solution method

should be intriguing and contain a level of challenge that invites speculation and hard work, and directs students to investigate important mathematical ideas and ways of thinking toward the learning

involves teaching a skill so that a student can later solve a story problem

when we teach students how to problem solve

teaching mathematics content through real contexts, problems, situations, and models

a mathematical activity where everyone in the group can begin and then work on at their own level of engagement

20 seconds to 2 minutes for students to make sense of questions

Mathematics Methods for Early Childhood Copyright © 2021 by Janet Stramel is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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How Your Child Learns to Problem-Solve

Your preschooler is figuring out what things are, why things are, and how things work..

In the course of your child's day, dozens of questions like these arise: "What's inside this box?" "How can I get into it?" "How far can I throw this ball?" "What will happen if I spill all of the crayons out of the box?" "I wonder if my teddy bear floats?" "How can I get these pieces of paper to stick to that piece of paper?" "Why does my block tower keep falling over?"

By asking these questions, your child is identifying and figuring out ways to solve them, and trying out her ideas. Every time she experiments with and investigates things in her world, such as how far water will squirt from a sprayer and what's inside a seedpod, for example, she is building her ability to solve problems. This is also true when she selects materials for building or when she learns to resolve an argument with a friend or sibling over a toy.

If we look at this process more closely, we discover that problem solving involves both creative and critical thinking. Both are necessary to figure out the solutions to problems of all kinds.

Creative Thinking

Creative thinking is the heart of problem solving. It is the ability to see a different way to do something, generate new ideas, and use materials in new ways. Central to creative thinking is the willingness to take risks, to experiment, and even to make a mistake. Part of creative thinking is "fluent" thinking, which is the ability to generate or brainstorm ideas. So ask your child "wide-open" questions! For instance, ask him to:

• imagine all the different ways to get to school (walking, flying, driving, swimming!).
• name everything he can think of that's red.
• name everything he can think of that's round.
• imagine all the things he could make out of clay or paper bags or even an empty box.

These are good examples of thinking problems that have many right answers. Research has shown that the ability to think fluently has a high correlation to school success later on. Another part of creative thinking is "flexible" thinking, which is the ability to see many possibilities or to view objects or situations in different ways. The next time your child pretends a pot is a hat or a spoon is a microphone or speculates on all the reasons that a child in a picture might feel sad, he is practicing his flexible thinking.

Critical Thinking

Critical, or logical, thinking is the ability to break an idea into its parts and analyze them. The math skills of sorting and classifying, comparing similarities and differences, are all parts of critical thinking. Whenever your child looks at, say, two glasses of juice and tries to figure out which one holds more, he is practicing this kind of thinking. To encourage it, ask your child:

• how many different ways he can sort his blocks.
• how many different ways he can make a building out of the blocks.
• how the building would be different if he used blocks of only one size.
• how a bottle of juice and his lunch box are alike and how they are different.
• how family members' shoes are alike and how they are different.

Asking questions about things that don't seem to make sense is another way children think critically. Questions such as "Why do I have a shadow on the playground but not inside?" or "Why can't I see the wind?" are examples of critical thinking. You don't need to have one right answer, but do encourage your child to express his ideas. There's one other thing to remember about problem solving: It's fun! So make room for spontaneity and prepare yourself to be surprised and delighted as you discover your child's unique way of thinking.

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Collaborative learning.

Collaborative learning is a broad strategy that can range from students working in pairs to working in groups of various sizes. The concept is based in sociocultural learning theory and constructivism and focuses on how people learn within social interactions by respecting knowledge held within the group (Ertmer & Newby, 2018; Panitz, 1999; Yang, 2023). Students can use the perspectives of other students and the shared experience of learning together to improve critical thinking skills (Kaddoura, 2013), experience deeper learning (Sembert et al., 2021), and connecting by negotiating boundaries of knowledge with peers (Yang, 2023).

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Collaborative problem solving, think-pair-share, considerations.

The purpose of collaborative learning is to allow students to:

• build knowledge within social groups through activities, 
• test out that understanding with the whole class as groups share what they have learned with each other, 
• then confirm the accuracy of their knowledge against the broader knowledge of the field by getting feedback from the instructor. (Bruffee, 1995)

Collaborative learning is not just for task division or coming to agreement, but enables students to “develop, compare, and understand multiple perspectives on an issue” (Karagiorgi & Symeou, 2011, p. 21). The classroom culture should enable groups to develop theories and refine these theories together.

Individual performance can put a lot of undo pressure on students, which is not helpful to maximize learning potential. By focusing on achieving a common goal, students are able to participate in socialization (McKeachie & Svinicki, 2006, p. 78). Students are more likely to communicate a lack of understanding to a peer than to an instructor (McKeachie & Svinicki, 2006).

In collaborative learning “group rewards (instead of individual rewards) and individual accountability (achieved by task specialization and division of labor) are critical to improving students’ achievement.” (Slavin, 1983 as referenced in Yang, 2023, p. 723)

There is distinction made in the literature between the processes of collaborative learning and cooperative learning (Bruffee, 1995; Panitz, 1999; Yang, 2023) that discusses the purpose of the interaction and distinguishes the process of learning from the product created because of working with members of a group. However, the terms have more similarities than differences. For brevity, the two terms are used interchangeably here.

Types of Collaborative Learning Activities

As previously mentioned, Collaborative Learning is a broad strategy that has a broad range of implementation strategies. Below explain some of these strategies.

Good collaborative learning tasks encourage individuals within groups to bring compelling ideas to the group to help other members of the group think about the task differently. For example, the task might be to come up with three alternative plans, pick the best, and describe the reasoning behind why the selection is preferable within the defined context. (Bruffee, 1995)

With this method the instructor provides a loosly structured problem to the student groups and the students decide how they are going to proceed in solving the problem. The following criteria must be present:

• a novel problem to be solved (i.e., as opposed to completing a routine task)
• objective accountability(i.e., the quality of the solution is visible to team members),
• differentiation of roles (i.e., team members complete different tasks), and 
• interdependency (i.e., a single person cannot solve the problem alone) (Graesser et al., 2018, p. 60)

These requirements can quite easily be met for various disciplines and skill levels.

Another well documented strategy Think-Pair-Share was developed by Dr. Frank Lyman in 1981. The strategy is to have students 1) reflect on a question or idea presented in class, 2) discuss their ideas with someone else in the class, then 3) share their own —more refined— thoughts or their peer’s thoughts with the rest of the class. In Sembert et al. (2021) Dr. Lyman provides insight into how he came up with the idea when observing a student teacher. The student teacher was having problems with the class participation with the model where only one person could talk at a time. Lyman connected the need for students to have a pause to collect their thoughts before sharing with a need for more students to be able to participate. So, he grouped the students together to share with each other before sharing their thoughts with the whole class. The Think-Pair-Share method was born.

Teaching Format Modifications

At Utah State University, courses can be taught in one of five different delivery formats, each having their own unique challenges and benefits. Below expounds on how to modify Collaborative Learning Techniques for some of those teaching formats that might not already be explicitly obvious.

Collaborative learning activities are possible in Connect and Online courses, but they require some technological mediation. For the Think-Pair-Share method, that might look something like the following:

Assign students to work with a buddy for the semester. Pairing each student with someone who is different from them can make it possible for the pair to have varying perspectives for discussion. When students work with the same partner for the duration of the course it gives them a chance to get to know each other. Allow them to pick with their partner what format of communication will work best for them (i.e. phone call, text messaging, instant messaging app, etc.). In each class period, provide at least one opportunity for students to stop and think, then connect with their buddy, then share their group perspectives with the class.

Sembert et al. (2021) used the Think-Pair-Share approach in a virtual course with live instruction via online video conferencing. Students were assigned a buddy based on their answers to a pre-course “All About Me” survey to maximize diversity, where possible. Buddies reported sharing insights with each other, asking for clarification, or getting professional support. Two of the students shared their experience in the class by noting feelings of socialization, camaraderie, and safety within the virtual environment. One of the students expressed a desire to have all his instructors use the Think-Pair-Share or buddy system.

Working in collaborative groups introduces the possibilities that students might not manage time efficiently and get off task, some students in the group may choose not participate fully or may not be able to do so for various reasons (a.k.a. “social loafing”), and lack of social skills might result in conflict or disruption to group productivity (Graesser et al., 2018, p. 62). Some structural or task ground rules and instructor coaching can help to alleviate these issues.

Collaborative inhibition is when the group that has collaborated doesn’t do as well on a recall task as a group who hasn’t worked together. Graesser et al. (2018) referenced a couple of studies (Andersson, Hitch, & Meudell, 2006; Weldon & Bellinger, 1997) which have identified this effect.

Ideas for additional collaborative learning activities can be found on the USU Teach website:

• Think-Pair-Share (Kaddoura, 2013; Sembert et al., 2021)
• Three-Step Interview (Yang, 2023)
• Case Study (Cornell University Center for Teaching Innovation, 2024)
• Team-Based Learning (Cornell University Center for Teaching Innovation, 2024)
• Jigsaw (strategy first developed by Elliot Aronson (McKeachie & Svinicki, 2006)
• Fishbowl Debate (Cornell University Center for Teaching Innovation, 2024)

Bruffee, K. A. (1995). Sharing Our Toys: Cooperative Learning Versus Collaborative Learning. Change: The Magazine of Higher Learning, 27(1), 12–18. https://doi.org/10.1080/00091383.1995.9937722

Cornell University Center for Teaching Innovation. (2024). Examples of Collaborative Learning or Group Work Activities. https://teaching.cornell.edu/resource/examples-collaborative-learning-or-group-work-activities

Ertmer, P. A., & Newby, T. (2018). Behaviorism, Cognitivism, Constructivism: Comparing Critical Features From an Instructional Design Perspective. In R. E. West (Ed.), Foundations of Learning and Instructional Design Technology (1st ed.). Available at https://edtechbooks.org/lidtfoundations

Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the Science of Collaborative Problem Solving. Psychological Science in the Public Interest, 19(2), 59–92. https://doi.org/10.1177/1529100618808244

Kaddoura, M. (2013). Think pair share: A teaching learning strategy to enhance students’ critical thinking. Educational Research Quarterly, 36(4), 3–24.

Karagiorgi, Y., & Symeou, L. (2011). Translating Constructivism into Instructional Design: Potential and Limitations.

McKeachie, W. J., & Svinicki, M. (2006). McKeachie’s Teaching Tips: Strategies, Research, and Theory for College and University Teachers (Twelfth Edition). Houghton Mifflin Company.

Panitz, T. (1999, December). Collaborative versus Cooperative Learning. https://eric.ed.gov/?id=ED448443

Sembert, P. J., Vermette, P. J., Lyman, F., Bardsley, M. E., & Snell, C. (2021). Think-Pair-Share as a Springboard for Study Buddies in a Virtual Environment. Excelsior: Leadership in Teaching and Learning, 14(1). https://doi.org/10.14305/jn.19440413.2021.14.1.04

Yang, X. (2023). A Historical Review of Collaborative Learning and Cooperative Learning. TechTrends, 67(4), 718–728. https://doi.org/10.1007/s11528-022-00823-9