Aha! in Action: Ms. Holmes (#2)

Ms. Holmes has 25 years of professional teaching experience, working as an adjunct professor in a university, a private and public elementary, middle, and high school teacher in southern Maine, rural northern Maine, New Hampshire, Boston, Massachusetts, and now in Houston, Texas. Ms. Holmes was emphatic in her narrative about a particular story during her first year of teaching in Houston, Texas, which occurred 9 years ago. Ms. Holmes had a beginning photography class, “full of senior boys who were taking their last arts credit in order to graduate.” Holmes recalls her transformative Aha! with her students:

The “moment” came when a student, who was considered to be problematic and barely passing his academic classes, looked at the first roll of film he had just processed. The film was perfectly exposed, rolled and processed, he the only kid in class who had not made one mistake. He was so amazed that he had earned the title “Best in Class,” something he hadn't experienced in [high school], it changed everything. He took film home every night to take photos just for fun, not for an assignment, and would come to tutorials (after school support) once a week to work in the darkroom. None of his other teachers believed me when I told them he was my favorite student and the hardest working kid in all of my classes. It changed the way I looked at each kid!

Ms. Holmes was clearly impacted by this moment, and the positive effect has transformed her current practice. She writes,

I now take each kid at face value and ignore any negative feedback from other teachers (even though the teachers mean well and are giving me “insider information” so that I'm [supposedly] prepared). I take every chance I can to celebrate the small successes along the way for each student, and to help them realize that practice makes you better when they are disappointed in a failure.

Ms. Holmes’ innocent faith in her student provided the necessary preconditions for the project to develop, for without this grace, the student clearly would have followed similar habits formed with other teachers. Further, her continued insistence with colleagues provided a metaphorical wall and created a secure environment for exploration and development of the student’s work. More than informing their different independent approaches, in this case the student and the teacher became codependent authors of their mutual successes. One needed the other, and neither would have experienced an Aha! moment without the belief that arose from the other. Way to go, Ms. Holmes. Your inspired story is another amazing Aha! in Action!

...it changed the way I looked at each kid!
— Ms. Holmes

Engagement: A Cognitive Catalyst! (Part I)

Engagement: A Cognitive Catalyst

Engagement is the most important noncognitive factor correlated with academic success, and has been defined broadly in the literature as willingness to participate in the processes introduced by the teacher. As such, “engagement” is seen by a number of theorists as a goal in planning, teaching, and evaluation protocols (Bundick, Quaglia, Corso, & Haywood, 2014; Gallup, 2013). Experiential and active learning increase engagement and are both positively associated with learning. In addition to stronger academic performance, student engagement increases critical thinking and learners develop better attitudes toward learning (Wang & Degol, 2014). Strong, Silver, and Robinson (1995) have found that increased engagement encourages learners to be more creative, causes them to be more curious, promotes positive relationships with others, and fosters enthusiasm learning – all ideal preconditions for insight and Aha! experiences (Dolmans, Loyens, Marcq, & Gijbels, 2016). Likewise, students have low tolerance for activities that are overly repetitive and forced upon them with no active choice or that require little active thought (Jay, Caldwell-Harris, & King, 2008). Kolb’s (1984) Experiential Learning theory claims that teaching focused on engaging students serves as an ideal vehicle for learning mastery, claiming that, “learning is best conceived as a process, not in terms of outcomes” (p. 26). Kolb’s theory combines nicely with active learner research established by Dewey (1924), as well as Vygotsky’s (1978) research on social constructs built from learners while fully engaged, and outcomes displayed in Piaget’s (1950) theory of cognitive development (Piaget, 1950; Piaget & Cook, 1952). Engagement is a powerful catalyst in learning, opening cognitive pathways for students to better engage in the conceptual processes necessary to move from surface to deep thinking and display strongest outcomes (Jonassen, 1992).

Historical Instances of Measurement and Intervention in U.S. Schools (Part I)

Historical Instances of Measurement and Intervention in U.S. Schools

Federal interventions and measurements of schools’ success, teacher efficacy, and student achievement are a relatively recent development. Moreover, much needed attempts to address structural ills have brought up complex issues of pedagogy and student outcomes. We are still presented with widely variable local practice and degrees of support, which have made outcomes difficult to measure and success for future programs hard to predict (Easley 2005; Hirschland & Steinmo, 2003). In this post I will trace some of the more significant interventions and how they have shifted the debate in the past few decades.

According to Wong and Nicotera (2004), Brown v. Board of Education (1954), “set a precedent for the use of social science research in defining and examining... education” (p. 122). This landmark case and the Supreme Court’s ruling to desegregate schools created an environment in education that allowed for more equitable conversations about educational opportunities, the observation of national norms for schools, and how to connect measurements with practice (Wong & Nicotera, 2004). One of the most significant of these interventions is a co-authored document published in 1966 by Coleman et al., provisioned by the Civil Rights Act of 1964, under the title of Equality of Educational Opportunity. Under the direction of James Coleman, this was the first major study to evaluate metrics affecting student achievement nationwide, but it also introduced the complex problem of which structural elements in the student experience are most important to address. According to Hanushek, Coleman et al.’s report redirected focus from the organization of individual schools to student outcomes, including employment opportunities (2016, pp. 19-20). This entailed a shift in the definition of education, placing it within a broader social context. Coleman et al. (1966) argue that, “schools bring little influence to bear on a child’s achievement that is independent of his background and general social context” (p. 325). American schools spent the next several decades focusing greater attention on a myriad of environmental and structural needs of students, with particular focus on improving outcomes of minority students by integrating schools. Many of these changes and differences in school effects made positive impact on students (Hanushek & Kain, 1972; Sewell, 1976), especially those in urban environments where socioeconomic differences are more evident (Hanushek, 2016).

Changes in federal and state policy made desegregating schools possible, and these changes improved learning conditions for students. However, these new, federally enforced measures did not go far enough to improve student achievement, nor did these policies recognize the positive effects of teachers. The Coleman Report brought about improvements in schools, but the new policies missed a great opportunity by failing to robustly support what teachers need in order to attain learning objectives, particularly the observation of how teacher effects increase students’ processing (Hanushek, 2016). Many scholars and commentators presented a counterargument to the Coleman Report, arguing that schools have a greater relative weight in positively influencing student outcomes than their communities and other external factors (DuFour & DuFour, 2010). Robert Marzano (2003) concludes not only that schools have a significant impact on student achievement, but also that, “schools that are highly effective produce results that almost entirely overcome the effects of student backgrounds” (p. 7). Schools, and the teachers within them, are the critical component for meaningful, positive reform in education.

U.S. Secretary of Education T. H. Bell formed the National Commission on Excellence in Education in 1981, instructing the commission to examine the quality of education in the United States and to produce a report within 18 months. The report, published as A Nation at Risk (1983), was, like the Coleman Report, dramatic in its impact. Again, the emphasis was on student outcomes, and the overall assessment it presented was of an educational system in crisis. An interesting point that the authors make in A Nation at Risk that still resonates with many Americans now is the claim that the lack of progress in schools causes, “a dimming of personal expectations and the fear of losing a shared vision for America” (p. 324). The report aimed its efforts towards maximizing, “the best effort and performance from all students” (p. 324), making claims that schools are underserving our citizens and that without significant reform Americans will continue to become less competitive in our global economy and less able to succeed in the job markets domestically due to their lack of preparation. Critics of this influential book identified the most pressing issue, that is, the relative roles of federal and local authorities in shaping the educational experience. Guthrie and Springer (2004) state that, “a centuries-long American tradition of state plenary authority and local operating discretion is now giving way to a pressing national uniformity of federally imposed accountability requirements” (p. 7-8).

Check back soon for Part II of this series!

Aha! in Action: Mr. McLaughlin (#1)

Mr. McLaughlin has 25 years of professional teaching experience in public and private high schools, beginning in a rural northern city in Texas, and now in Houston, Texas. Mr. McLaughlin describes Aha! moments as, “an exclamation point” that happens when teaching, coaching, and directing reach their fullest potential. Mr. McLaughlin recalled having many throughout his career, but offered a remarkable story of a particular student that changed the course of her life (and Mr. McLaughlin’s), based on an intense Aha! experience:

Virginia, who came [with] a reputation of being an average student, with marginal athletic ability, and quite reserved. As the years progressed, she became known as a plodder in the classroom, a good teammate in softball, and her personality began to blossom. In the spring of her senior year, however, one event seemed to have an everlasting impact on who she was and the timing was perfect. In the conference championship game, we were behind by one run in the top of the seventh inning with two outs and runners on first and second. Virginia, who always batted ninth in the batting order, looked overmatched facing a pitcher who would eventually play for the University of Arkansas. Another strike and the count was now 3 and 2. From the third base coaching box, I started moving toward Virginia and started to motion for her to meet me halfway up the baseline, but before I got my hand up to my waist, she put her hand up, palm out and mouthed the words, “I got this, coach.” She confidently repositioned herself into the batter's box . . . windup and the pitch, and the ball left her bat with a crack, a line drive perfectly over the second base bag. The first run scored and the throw to home plate dribbled away from the catcher, and before the pitcher could retrieve the ball, the second run scored. We held on in the bottom of the inning and won the championship!


Mr. McLaughlin noted that this seminal moment in his career formed the basis of a belief (his own Aha!) that, “sometimes it is the most unlikely member of a team who makes the most important contribution.” This noticeable change in Virginia’s behavior is a testimony to the extreme effect of her Aha! experience. McLaughlin describes Virginia at first as someone who was reserved, marginal athletic ability, and most notably, “a plodder in the classroom.” Over the course of their career, every teacher has this student. In fact, teachers might often dismiss a student who is both average in ability and does not seem to express a great disposition for future achievement, but that is exactly where McLaughlin’s relationship with this student, understanding how to push and pull with her abilities, became the necessary ingredient for igniting her potential, and for Mr. McLaughlin to revise his assessments. In this way, Virginia’s Aha! moment became a turning point for the teacher as well.

The Aha! moment allowed Mr. McLaughlin to understand that Virginia’s thinking had changed. But more than this, the shared Aha! experiences of Mr. McLaughlin and Virginia combine to create a life-changing moment that set a new foundation for them both to flourish now at new, previously unanticipated levels. In fact, McLaughlin changed his entire belief about what is possible with students from this experience, subsequently benefiting thousands of students over his nearly 25 years of teaching. In this situation, the winning moment can be seen as a manifestation of the Aha!, but it is also in understanding the subtle nuance between the coach and the athlete where one can fully see how the learning transcended the game. “I got this,” was perhaps an even greater breakthrough because it signified a shift in relationship, not just forms of the thinking and understanding within an individual. Virginia now connected with Mr. McLaughlin in a way previously unattainable and in a way that could not have been deduced from previous experience. This Aha! is one of enlightened human interconnectivity. Congratulations, coach!

What’s your Aha!?!

What’s your Aha!?!

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.


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?!

FACS 2.jpg

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.