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

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. Franklin, Secondary School Science Teacher

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.