Surface and Deep Processing: Cognitive Behaviors of Aha! Moments (Part 1)

Surface and Deep Processing: Cognitive Behaviors of Aha! Moments

Marton and Säljö (1976) have produced a hierarchy for different levels of learning in order to determine, “processes and strategies for learning to be examined” (p. 1). Marton and Säljö were motivated to understand more than what a student may have learned, they were interested in understanding how much was learned. Marton and Säljö are the first to have produced surface-level and deep-level processing definitions and formulated research outcomes.

Hattie and Purdie (1998) sum up varying studies on both styles of processing and showed that surface approaches in learning, “involves minimum engagement with the task and typically focuses on memorization” (p. 4), with a key indicator being that learners reflect little or not at all on concepts, while a deep-level thinking approach “involves an intention to understand and impose meaning” (p. 4). A consistent aspect reported with deep-level thinking is the relationship students seek between concepts, creating more of, “an intrinsic interest in learning and understanding” (p. 4). Dinsmore and Alexander (2012) have reported on prevailing assumptions surrounding the notions of deep and surface processing. It is commonly believed that deep processing, “promotes better learning outcomes, while surface processing promotes weaker learning outcomes” (p. 500). The most easily traced outcomes include limited recall and reproduction of content information, new concepts described anecdotally, and memorization or non- prioritized listings of facts. This style of processing places greater emphasis on learning for the sake of information regurgitation. Deep processing strategies are defined largely by a learner’s comprehension of materials, “together with the process of relating and structuring ideas, looking for underlying principles, weighing relevant evidence, and critically evaluating knowledge” (Dolmans, et al., 2016, p. 1089).

Contrary to the judgments typically attached to the comparisons of surface-level vs. deep-level processing, a number of theorists have convincingly argued against a value-laden dichotomy, and that both forms of processing are equally important and necessary for learning, each offering unique advantages. An example of the favoring of deep-level over surface-level processing is found in Jay, Caldwell-Harris, and King (2008), who state that, “a shallow level of processing is one in which only superficial or physical aspects are encoded. A deeper level of processing takes more time and effort to activate the semantic meaning of stimulus” (p. 85). Jay et al. (2008) report also that surface learning often, “results in poor recall,” and that concepts that receive deeper levels of processing, “persist longer” (p. 85). However, it is important to recognize that by better understanding the relationship of surface and deep processing, teaching practice can be more positively informed and learning outcomes may be improved. This can be accomplished by studying how surface or deep processing assists or inhibits learning in a variety of contexts.

As Dinsmore and Alexander (2016) report, “the most advantageous level of processing for two individuals may not be or perhaps should not be the same but should depend on their stage of development [of concepts] and their performance goals” (p. 214). This challenges the assumptions that deep processing is always a stronger approach for outcomes and that the development of surface processing is invariably weaker. Alexander and Judy (1988, p. 391) have demonstrated that, “knowledge and strategic processing were inextricably intertwined [and that]... processing cannot be addressed in isolation” (Dinsmore & Alexander, 2016, p. 214). The elected processing approach, whether surface-level or deep-level, is utilized based upon a learner’s goals and nature of the learning activity (Biggs, Kember, & Leung, 2001; Entwistle & McCune 2004; Lonka & Lindblom-Ylånne 1996; Loyens, Gijbels, Coertjens, & Côté, 2013). However, evidence is inconsistent, and definitions and methods of studying the processes are sometimes ambiguous, and attempts to codify processes for reporting are, to date, incongruent (Block, 2009; Dinsmore, Alexander & Loughlin, 2008; Heijne-Penninga, Kuks, Hofman & Cohen-Schotanus, 2008; Murphy & Alexander, 2002).

Aha! moments, which can also be understood as moments of insight, could arguably indicate the point at which surface processing begins to transform into deep processing. A number of studies have identified insight as its own unique form of learning, and have argued that insight-learning has an effect on the higher executive functioning of the mind (Kounios & Beeman, 2015). It has been shown that when students are engaged in insight learning experiences and also succeed in solving problems in this way that there is a noticeable increase in long term memory (Cranford & Moss, 2012), and the production of a positive emotional state (Ekman, 2006; Fredrickson & Branigan, 2005). Further, it has also been shown that strong, positive learning habits are reinforced when students experience Aha! moments (Kounios & Beeman, 2014; Mednick, 1962).

Modern research on insight and how sudden changes in cognition influences learning dates back to the early twentieth century (Wallas, 1926). Dr. Karl Bühler (1907) coined the phrase “Aha- Erlebnis,” or translated: Aha-Experience. Bühler used this phrase to describe a moment in learning when “suddenly, the lights come on!” (p. 341). Bühler was extremely curious about the cognitive connections that were made, and how learning changed, as the result of Aha-Erlebnis. Bühler states,

it is clear that our AHA-experience leads us always to the deeper sense/meaning; you could say that this deeper understanding/comprehension may always be preceded by a “shallow” or superficial comprehension – just understanding the meaning of the words – and that constitutes the preliminary whole... but we would [now] have the whole thing... where we can prove it, at the deeper understanding, made comprehensible. (p. 16)

The spectrum of early research on insight ranges from observing changes in behavior and understanding psychological patterns that influence learning (Bühler, 1907; Duncker & Lee, 1945; Wallas, 1926), to the present and how insight is a unique form of learning. There are a number of theories on insight; at present, no one theory dominates interpretation (Kounios & Beeman, 2015; Sternberg 1996). In spite of differences between theories, they share two principles: (a) sudden, conscious change in a person’s representation of a stimulus, situation, event, or problem (Davidson, 1995; Kaplan & Simon, 1990), and (b) the change occurs unexpectedly (Jung-Beeman, et al., 2004; Kounios & Beeman, 2014; Metcalfe, 1986). Further, a strong correlation has been demonstrated between moments of insight and increased engagement in learning, positive boost in mood, and greater likelihood of more moments of insight (Kizilirmak, Da Silva, Imamoglu, & Richardson-Klavehn, 2016; Kounios & Beeman, 2014). Aha! moments have been shown to increase and enhance memory performance (Ash, Jee, & Wiley, 2012; Auble, Franks, & Soraci, 1979; Danek, Fraps, von Müller, Grothe, & Öllinger, 2013; Dominowski & Buyer, 2000; Kizilirmak, Da Silva, Imamoglu, & Richardson- Klavehn, 2016), reliably grounded on insight’s proven ability to, “comprise associative novelty, schema, congruency, and intrinsic reward” (Kizilirmak et al., 2016, p. 1).

Check back soon for Part 2!

The information given” were alone, “completely insufficient to answer the question.
— M. Beeman, Secondary School Science Teacher

Now You See Me, Now You Don't: The Hidden Truth In Our Faces!

Facial Expression and Emotion (and the hidden truth of our faces)

Paul Ekman (1993) examines cross-cultural research on facial expression, seeking to elucidate further understanding about four key questions: (1) “What information does an expression typically convey? (2) Can there be emotion without facial expression? (3) Can there be a facial expression of emotion without emotion? (4) How do individuals differ in their facial expressions of emotion?” (p. 384) Ekman reaffirms the cross-cultural agreement on six primary areas of universal categorization of facial expression: fear, anger, disgust, sadness, and enjoyment. Ekman also makes clear that further research is necessary to explain, “the question of what the face can signal, not what information it typically does signal” (p. 387). Important to this dissertation is Ekman’s assertion that, “facial expressions are more likely to occur when someone sees or hears a dynamic (moving) event and the beginning of the event is marked rather than very slow and gradual” (p. 388). Ekman claims that sometimes the only expression of emotion a person may exhibit might come from an area of the body other than the face, such as, “the voice, posture, or other bodily action” (p. 388). Ekman goes further by claiming that there is a possibility for an emotion to transpire without a facial or observable change in expression (p. 389). It may be that in situations where someone shows little or no observable change in expression that the emotional connection is weak, not present at all, or not entirely transferable to the person being observed. It is important to note that change may indeed be occurring, but these changes may be sub-visible, taking place at the micro-muscular level, indicating autonomic nervous system activity that is only detectable through sophisticated measurements with electromyography (EMG) sensors. Tomkins (1963) reports that facial activity is always part of an emotion, even when its appearance is inhibited. This could be based on cultural differences or any variety of other factors. The intensity of the emotional reception is somewhat correlated with the fidelity of the expression.

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Ekman (1985/2009, 1992, 1993) reports that individuals can experience emotion without observable changes in facial expression. Sometimes a person will respond to a stimulus with a head nod, a clenched fist, change in posture, or by walking toward or away from a situation. Even more intriguing is the change in expression that can be communicated through spoken words and audible vocalizations (i.e., moans, screams, or sighs), without necessarily expressing a visible change in the face. Ekman (1993) shows that it is equally true that a person can fabricate an expression of emotion without actually feeling an emotion (p. 390). Ekman states that, “although false expressions are intended to mislead another person into thinking an emotion is felt when it is not, referential expressions are not intended to deceive” (p. 390). It is most common to use referential expressions when referring to previous emotional experiences, specifically not experiences being felt currently. Examples of false emotional expressions aside from referential expressions are generally understood to be examples of deception. Efforts to deceive can be harmful or beneficial. A lie can conceal an important truth that harms a person in some manner. However, a lie can also allow a comedian to deliver a punchline at the appropriate time to maximize the intended comical effect, or give someone the courage to push past their fears when facing the insurmountable task of asking someone else to be their Valentine. The key is to fabricate expressions without specific emotional impetus.

Facial Action Coding System

Ekman and Friesen (1978/2002) published the Facial Action Coding System (FACS) manual, with a robust revision in 2002. This publication is a comprehensive guide for measuring facial expressions and behaviors. The manual includes the complete 527-page guide to various facial expressions, a 197-page investigator’s guide, a score checker protocol (included for the FACS test, published and sold separately), and a variety of example photos and videos are also included. The manual is a comprehensive system for describing all observable facial movements; it breaks down facial expressions into individual components of muscle movements that represent changes in behavior and emotional response to a given stimulus. Subsequent publications have featured subtle and microexpressions. Whether you can see them or not, there are a great many truths hidden in the expressions of our faces. Are you looking closely enough to find them?!

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The Cognitive Neuroscience of Insight: A Golden Era For Research

The Cognitive Neuroscience of Insight

Kounios and Beeman (2014) report on the variety of factors that influence and create insight moments. Their work represents the most comprehensive and provocative investigation on insight, focusing on changes in cognitive behaviors as a result of having experienced insight, whether through suddenly realizing a solution or suddenly becoming aware of one. Kounios and Beeman define insight occurring,

when a person suddenly reinterprets a stimulus, situation, or event to produce a nonobvious, nondominant interpretation. This can take the form of a solution to a problem (an “aha moment”), comprehension of a joke or metaphor, or recognition of an ambiguous percept. (p. 71)

Research shows that insight moments are distinct from other forms of learning, analytical thinking and processing in particular (Kounios & Beeman, 2014; Sternberg & Davidson, 1995). Kounios and Beeman (2015) report that, “except for a few limited and arguable counterexamples, only humans—most humans—have insights. It’s a basic human ability” (p.11).

Reliable production of insight moments has been accomplished through several scientific measures. Some early research made productive use of the Remote Associates Test (RAT), initially created to assess human creative potential (Mednick & Mednick, 1962/1967/1968), in order to induce moments of insight. A classic example from the original tests are the three words same/tennis/head, each associated in some fashion (i.e., synonymously, compound, or semantically) with the solution word: match. Same and match are associated as synonyms; match-head (or sometimes, matchhead) is a compound word; and tennis match is a semantic association. If and when a solution is accomplished or revealed, the test verifiably produces a change in thinking, often in the form of an insight. Bowden and Jung-Beeman (2003) modified the original RAT problems and developed them into a new subset of the original test, more commonly known as the Compound Remote Associates Test (CRAT). These CRAT problems are classified into two categories: (1) homogeneous, meaning the solution word is a prefix (or suffix) to each of the three challenge words in the triad; and (2) heterogeneous, meaning that the solution word is a prefix (or suffix) for at least one of the challenge words and a prefix (or suffix) to the other words in the triad. An example of an easy CRAT are the three words print/berry/bird, each associated with the solution word blue, whether as prefix or suffix to each of the words in the triad. Blue is the prefix to blueprint; blue is the prefix to blueberry; and blue is the prefix to the word bluebird. This is an example of a homogenous CRAT. Bowden and Jung-Beeman created this new hybrid because it fosters conditions that allow participants to solve challenges more quickly. Solutions require less abstract thinking and tests produce stronger reliability, and because participants can solve them more quickly, more of them can be observed to form a more cohesive and comprehensive understanding of insight and non-insight moments (Bowden & Jung-Beeman, 2003, p. 636).

Important preconditions exist with insight moments that have reported positive impact on the likelihood, frequency, and strength of Aha! moments. Mood has been studied and its effect on enhancing insight has been shown. Ashby, Isen, and Turken (1999) and Isen, Daubman, and Nowicki (1987) report on these effects and it appears that positive mood and affect, “enhances insight and other forms of creativity, both when the mood occurs naturally and when it is induced in the laboratory” (p. 83). Mood also impacts attention, positively increasing or negatively diminishing capacity based on naturally occurring or an induced emotional state. Fredrickson and Branigan (2005) show a distinct connection to positive mood and a broadening of novel and varied stimuli, creating a stronger opportunity for exploratory behavior. Subsequently, the variability of excitement and related phenomena of Aha! moments can fluctuate based on the context. Kounios and Beeman affirm that,

insights are often accompanied by surprise and a positive burst of conscious emotion, but we do not consider these to be defining features because individual insights in a sequence of insights, as occur in many experimental studies, don’t all elicit such conscious affective responses. (p. 74)

Related research draws upon Fredrickson and Branigan’s (2005) broaden-and-build theory:

The broaden hypothesis states that positive emotions broaden the scopes of attention, cognition, and action, widening the array of percepts, thoughts, and actions presently in mind. A corollary narrow hypothesis states that negative emotions shrink these same arrays. (p. 2)

Attention allows learners to narrow or broaden their focus on stimuli, which in the case of an Aha! moment can be most valuable. A person might choose to focus most of their energy on a singular problem, intending to solve it, at the expense of broader focus. The combination of mood and attention create an even stronger likelihood for insight to occur (Easterbrook, 1959; Rowe, Hirsh, & Anderson, 2007).

Kounios and Beeman (2014) conclude aspirationally, hoping that, “researchers may look back at the early twenty-first century as the beginning of a golden age of insight research!” (p. 88).

Structure of the Observed Learning Outcomes (SOLO): A Taxonomical Bridge

Structure of the Observed Learning Outcomes: A Taxonomical Bridge

Teaching practice is better informed with the knowledge of surface and deep processing, its role in learning, and the transfer-appropriate potential for student achievement in schools. However, these subsurface processes represent styles of thinking and learning – not necessarily physical behavior. In order for teachers to more fully utilize this information and synthesize strategies that support developing these processes, a correlated taxonomy will be useful. The Structure of the Observed Learning Outcome (SOLO) taxonomy is a widely recognized and accepted tool for showing changes in complexity of understanding. McMahon and Garrett (2016) report that,

SOLO is a useful contemporary tool that incorporates ... aspects of former taxonomies (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956; Merrill, 1971; Gagne, 1977/1984) in that it studies the cognitive complexities of a learner’s response to a given learning stimulus... [SOLO] emphasizing the observation of student learning cycles to describe the structural complexity of a particular response to a learning situation through five different levels: prestructural, unistructural, multistructural, relational, and extended abstract. (p. 422)

This approach more thoroughly examines changes in thinking by addressing changes in observed behavior. Just as Aha! moments represent sudden and unexpected cognitive illumination when a solution is found, along with their observable correlates, SOLO taxonomy represents a classification tool for the physiological behavior in learners as it changes over the complete cognitive continuum. This rubric for progression is a practical framework for teachers to evaluate achievement, “in a language that is generally applicable across the curriculum” (Biggs & Collis, 1989, p. 151). SOLO taxonomy is a form of measuring students’ understanding of subjects, from the introduction of a concept to a student’s expertise with it.

According to Biggs and Collis (1982/2014), who first introduced this taxonomy, SOLO is, “based on the observation that, over a large variety of tasks and particularly school based tasks, learners display a consistent sequence, or ‘learning cycle,’ in the way they go about learning them” (p. 152). In essence, as a learner moves from a superficial understanding of the components of a concept towards a deeper processing of the concept’s features, the taxonomy accurately shows these progressions in a manner that makes learning more easily observed by teachers. The final mode in the SOLO taxonomy suggests learners’ ability to extend comprehension into a final transfer-application understanding. The SOLO spectrum from prestructural to extended abstract is also analogous to the cognitive change represented when introducing a stimulus to a learner through to the development of an Aha! moment. Biggs and Collis (1989) discuss congruency among similar theories that support neo-Piagetian models (Case, Hayward, Lewis, & Hurst, 1988; Fischer, 1980; Fischer & Pipp, 1984; Halford, 1982), distinguishing, “between learning and development in a way similar to that suggested here [SOLO] with their terms ‘optimal level’ (the last mode reached) and ‘skill acquisition’” (p. 157).

Hunt, Walton, Martin, Haigh, and Irving (2015) studied the implications of school-wide adoption and application of the SOLO taxonomy to inform teaching and learning in a secondary environment. Hattie and Purdie (1994) were among the first to show that SOLO taxonomy is useful and effective for training teachers on how to structure questions, design activities, and to matriculate through modes of learning along the SOLO hierarchy in multiple curricular areas. Hattie and Purdie also showed that teachers indicated using SOLO taxonomy for accomplishing learning objectives, surface and deep processing, and found it much easier and more effective to use. Hattie, Clinton, Thompson, and Schmidt-Davis (1997) indicate in their research that,

expert teachers are more likely to lead students to deep rather than surface learning. These teachers will structure lessons to allow the opportunity for deep processing, set tasks that encourage the development of deep processing, and provide feedback and challenge for students to attain deep processing. (p. 54)

SOLO seems to promote stronger deep processing effects for teachers and with students, likely due to a reliable and understandable hierarchy for witnessing change in a learner’s thinking and cognition. In all of these studies, it is clear that surface and deep processing strategies are embedded into practices that are reflected through SOLO, and opportunities to inform and improve teaching practice are present.

What's the Difference Between Teacher Quality and Quality Teaching?

Contemporary researchers have published quantitative and qualitative research which examine learning in classrooms, particularly emphasizing learning outcomes and the effects of teacher quality and quality teaching in classrooms (Biggs, 2012; Gardner, 2011; Hattie 2016; Marzano, Frontier & Livingston, 2011; Nuthall, 2007). These two categories have specific influences and observable outcomes. Quality teaching and teacher quality both have tremendous impact on positive outcomes for students, particularly with regard to creating opportunities for moving learning objectives between surface processing and deep processing – at times into transfer-appropriate strategies for learning.

Recent arguments have been made that help to differentiate between quality of teachers and quality of teaching (or teaching efficacy) (Hanushek, 2011; Harris & Sass, 2011; Taylor, Roehrig, Hensler, Connor, & Schatschneider, 2010). Darling-Hammond and Jaquith (2012) posit that teacher quality and quality of teaching should be considered independently, but as equally important. Darling-Hammond and Jaquith argue that the talents, personal mannerisms, and paradigms each teacher draws from in order to inform their teaching should not be evaluated independently of factors that enable, “a wide range of students to learn” (p. i), asserting that teaching efficacy,

is also strongly influenced by the context of instruction: the curriculum and assessment system; the “fit” between teachers’ qualifications and what they are asked to teach; and teaching conditions, such as time, class size, facilities, and materials. If teaching is to be effective, policymakers must address the teaching and learning environment as well as the capacity of individual teachers. (p. i)

It is crucial to understand these distinctions while exploring the potential for introducing insight learning opportunities into learning environments. Teachers may be effective at implementing pedagogy, but lack the requisite training to maximize Aha! moments in learning. Similarly, an expert pedagogue may be inducing preconditions for Aha! moments but may lack the effectiveness to maximize their effect in learning, especially for moving from superficial information acquisition to deeper thinking strategies and transfer-appropriate opportunities.

Goe (2007) outlines a comprehensive framework for better understanding teacher quality in terms of its effect upon student success, following on from the concern with measurable and broad impacts upon the widest range of students. The graphic representation in Figure 2 presents teacher quality as a combination of inputs and processes, and student outcomes as measurable effects of teacher quality. These inputs and processes include teacher certification, beliefs, instructional delivery, interactions with students, teacher test scores and experience, and classroom management. Student achievement is both an input and output, often part of teacher evaluations and other forms of feedback influencing practice. Inputs, processes, and feedback from outcomes (generally in the forms of grades from student assessment) all inform the basis for teacher quality.

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Figure 1. Graphic representation of a framework for teacher quality (Goe, 2007, p. 9).

Teacher as Architect of Learning and Designer of Experiences

Teaching efficacy is among the most significant determinants of student outcomes in classrooms (Hattie, 2008). As wide as the variety of teaching styles are, so are the variations of curricula, curricular delivery systems, factors affecting schools, and the quality of teachers and the training they receive. Laurillard (2013) sums up nicely the importance of teachers and their role as architects of learning and designers of experiences that optimize surface to deep thinking:

What it takes to teach cannot be determined directly from what it takes to learn, which means that teachers must be willing to treat the process as essentially problematic, iterative, and always improvable; we must stop assuming that teaching can be theorized like a natural science and treat it as a design science. (p. 82)

Indeed, teaching can and should be a designed process that encourages vibrant and dynamic growth in student outcomes. Aha! moments in learning are an important and special part of that design. These moments in learning should be captured and cultivated – and produced regularly. Teachers can positively transform learning experiences for students using strategies that promote the increased frequency of Aha! moments in their classrooms, the benefits of which connect to all areas of learning growth and potential (Kounios & Beeman, 2014). The opportunity for students to find deeper meaning in their work, extend ideas, and become more actively interested in their personal development in all areas of learning, becomes a powerful lever in education and learning overall, and one that teachers and school leaders must embrace and nurture.

Experienced teachers are able to contextualize learning and meet the needs of their students within various curricula, regardless of personality differences, and remain focused on mastery of content and transfer across subjects (Hattie, 2003). Further, as teachers develop their practice over time, the potential for greater positive impact in classrooms increases. Hattie (2011) states:

Expert teachers and experienced teachers do not differ in the amount of knowledge that they have about curriculum matters or knowledge about teaching strategies – but expert teachers do differ in how they organize and use this content knowledge. Experts possess knowledge that is more integrated, in that they combine the introduction of new subject knowledge with students’ prior knowledge; they can relate current lesson content to other subjects in the curriculum; and they make lessons uniquely their own by changing, combining and adding to the lessons according to their students’ needs and their own teaching goals. (p. 261)

The focus must therefore be on providing opportunities to develop expertise within teachers to cultivate and capture insight and discovery throughout their curriculum and course lessons. If one of the primary objectives in increasing teacher efficacy is helping students move from surface to deep thinking (Hattie, 2003), and if it is hoped that this change in thinking will produce transfer across different areas of learning, Aha! moments in learning provide an excellent opportunity for this type of teacher training. Hence, an understanding of surface and deep learning, the differences between them, the place of both in the learning, and developing Aha! moments to enact the transition from surface to deep could be most valuable in teacher development programs. These teaching strategies can be aimed at manifesting greater numbers of Aha! moments and a more robust and engaging learning environment. Teachers can be trained on how to maximize the number and magnitude of these moments and further impact learning, achievement, and observable outcomes of students.

There is a growing body of neurological research that proves cognition is highly plastic and that complex mental activity improves cognition, brain function, and structure (Chapman et al., 2015). The tools that are becoming available to enhance and increase retention of learning are becoming easier to access and more widely used, and there is growing interest from teachers and professionals in implementing techniques that increase achievement in students. School administrators must discover and invest in teaching development programs where current research about learning is at the center of informing practice. Teachers need to spend more time harvesting from the available research literature, perhaps even adding to it, in order to garner the fullness of its potential to inform behaviors, and to enhance their professional work in schools. This may be best accomplished by placing a greater premium on the observable behaviors and patterns surrounding learning in classrooms. As Laurillard (2013) suggests, we should transfer energies away from teaching teachers how to teach and toward training them in methods to become leaders of learning. Teachers cannot practically observe what is happening in the mind when learning occurs (or easily, even with various measurement apparatus – e.g., fMRI), but if the observable correlates of the Aha! moment reflect the plasticity and growth happening when students’ do learn, this breakthrough in research will open tremendous opportunities for teachers and students alike.

Aha!

Archimedes’ discovery of water displacement as a method for measuring the volume of an object was among the first recorded instances of the Aha! moment (Kounios & Beeman, 2015). The account of Archimedes’ transcendent moment can be summed up briefly: King Heiro II challenged Archimedes to determine whether a votive crown that had been made for him was made of pure gold, as represented to him, or if the goldsmith had adulterated it with some other metal. Archimedes grappled for some time with the problem of how to authenticate the crown without damaging it until one day, as he was lowering himself into his bath, he observed the correlative rise of the water level and had a flash of inspiration. He is said to have shouted Eureka! (“I’ve found it!”). His observation of displacement led to a profound insight – his Aha! moment, which was the breakthrough that allowed him to solve this problem. His Aha! moment enabled his thinking to move from surface to deep, thereby producing a theory for the measurement of the volume of an object without damaging it. More important than what Archimedes was attempting to accomplish, was how his mind now managed the exact same set of observations that most humans have when wrestling with a problem. His thinking exhibited the capacity to take seemingly disconnected ideas (i.e., the water rising in the bath, the volume of gold, and finding a way to determine legitimacy without damaging the artifact) and combine specific factual knowledge in order to provoke an Aha!, a breakthrough that created a sudden and unanticipated solution. This indicates an ability to compare and manipulate concepts, which is further up the taxonomy on the SOLO scale, not to mention the Piagetian scale of conceptual facility (1950). From the point of view of an observer, the expressive exuberance of Archimedes’ eureka made it possible to actually see him exhibiting a new level of facility with the concepts available to him. If that observational mechanism can be brought into any learning environment, along with a rich understanding of how and when human beings achieve milestones along the path to greater conceptual facility, then our instructional practice will be that much more powerful and effective.

An insight is a quantum leap in thinking. There is a distinct before and after, and history is filled with similar stories of men and women, young and old, and their Aha! moments. Whether these moments are connected to monumental or to less consequential but still important moments of insight, they are part of the fabric of the human journey because they are a universal form of human learning. Galileo looked to the heavens and observed the orbit of the Earth (Kounios & Beeman, 2015), suddenly forming theories about orbital eccentricity; Sir Isaac Newton had an Aha! moment when he saw the apple fall from the tree (Gleick & Alexanderson, 2005), later going on to describe universal gravitation; Einstein worked through a thought experiment when a sudden breakthrough allowed him to conceive what became his theory of relativity (Einstein, 1922/2003); and Sir Paul McCartney woke up one morning, after a long series of shows, and in his Aha! moment he crafted (“Yesterday”), a song that has since gone on to become the most- recorded song in history (McCartney, 2009). In each of these examples, the sudden realization could not have been predicted. The significance of these moments generally causes the learner to refer back to the moment in a sort of before-and-after manner – a life moment.

The practice of seeking these moments of insight, their subsequent outcomes, and the transformation in learning that takes place as a result can be of great value in pedagogy. My research has collected, documented, and analyzed the observable instances of these Aha! moments, and used the term “correlates” to signify both a possible pattern to observation and a taxonomy of insight that occurs for individual students in complex ways. The goal is not only to identify these moments, but also to produce a template for techniques, methods, and practices that learning leaders may adopt or implement in their curricula in the hope of creating the fertile preconditions that facilitate production of these moments.