Rebecca A. Huggins

In How People Learn: Brain, Mind, Experience, and School, there is a lovely analogy to help the audience conceptualize prior knowledge and biases: in the children’s story by Leo Lioni called Fish is Fish, a fish imagines birds as fish with wings and cows as fish with udders because he can only understand the world through the lens of what he already knows. While a little exaggerative, this parallel does an attractive job in illustrating how we filter and transform knowledge: students do not enter our classrooms as empty vessels but as collections of mixed knowledge structures, experiences and deeply held misconceptions. If I were to ask an educator what they believed about student differences, many would share their “folk wisdom” about variability. They would likely tell me that all students are different. That lessons should be tailored to a student’s specific learning style, and that students learn best who have voice and choice. Some might say that “active” learning helps appeal to students over passive environments, while others might argue students must be interested or ready before learning can even take place. I have even recently heard the left-brain/right-brain neuromyth—where specific cerebral hemispheres supposedly dictate a person’s learning strengths. Like the fish in Lioni’s story, these pervasive myths about learner variability often prevent educators from seeing the actual cognitive work students are (or are not) doing.   

It is interesting, then, that educators (both young and old) come to teaching with their own collection of unique, idiosyncratic, and often flawed mental models of students and learning. The theories mentioned above are predominantly conflated conceptions of preference that have very little to do with student variability and learning. If I were to dabble in defining learner variability, I would say it acknowledges that learners arrive with different levels of prior knowledge, experiences and expertise, which shapes how they access and make sense of new information. This shouldn’t be taken as a cue to pile on choices for students, or to assume the next step is tailoring every lesson to each stated interest or experience level. It also doesn’t mean students need novelty or flashy engagement hooks to help them learn, “gamification” or even physically “active” learning environments—despite popular opinion.

The strongest and probably most difficult myth that teachers hold is the belief in behavioral engagement. This belief argues students need choices, relatability, and active learning environments to ensure that they are not psychologically absent from the content. There is a premise that active environments must be ones where students are doing through hands-on tasks, which are “inherently superior” to tasks like reading or listening. Often defined by a student’s outward participation and involvement in classroom tasks, teachers are often misled to believe that physical activity equates to mental activity. This overemphasis on engagement tactics, often neglects the instructional techniques that all learners benefit from including schema construction, direct guidance for novices, deliberate practice and retrieval, and task-related feedback. Yet instruction which prioritizes relatability and novelty over cognitive processing can, actually, backfire because it distracts students from deep structural principles of the lesson. 

Gamification and ed-tech tools have become increasingly popular to meet this end, because they replicate the mechanics of high-engagement activities like video games. Unlike in classrooms where feedback is often delayed, games provide immediate rewards and corrections, while maintaining a balance between challenge and skill. Avoiding the pervasively “boring” traditional teacher-directed models, folk wisdom argues that these active student-centered environments increase student motivation. Yet there is an interesting difference between participation and effort and the ability for these tools to impact cognitive performance. Unintentionally, these tools often prioritize the mechanics of behavior over the mechanics of thought—which is the exact opposite of their goal. Consequently, a student can spend hours in a state of high effort and participation—and achieve near-zero gains in cognitive performance. 

The conception of voice and choice—again, a conflation of variability with preference—refers to the deeply held conviction that providing students with extensive options regarding what they learn, how they learn, and how they demonstrate their learning is a primary driver for academic achievement. This is the cornerstone of frameworks like Universal Design for Learning (UDL), which promotes the idea that there is no “average” student. It’s structured around the belief that building diversity and flexibility into the learning environment from the start will increase student success and provide “cognitive access” to all learners. In their 9 guidelines, UDL founders argue for multiple means of engagement, representation, action and expression. While giving students choices can improve their attitudes, interest, and perceived autonomy up to a point, meta-analyses show that it has no consistent positive effect on objective learning outcomes. And while students often report “liking” UDL-aligned options, such as video games, more than traditional reading (shocking, I know), again, behavioral engagement does not translate into the cognitive work required to build mental schemas.  Even in the research where choice shows positive effects, this “choice” refers primarily to strategic self-regulation, or the ability to select the most effective mental tools from a vast repertoire to solve a complex problem, not the choice between activities.    

Ironically, making choices is a form of self-regulation that is mentally effortful. It is taxing on a student’s mental energy. To say nothing of the fact that students are poor judges of effective learning. Students are often fooled by subjective impressions of ease, believing they have mastered a topic because it was relatable or active, only to find that they have achieved surface-level familiarity. When given choice instructional paths, students most often choose practicality or ease over retention or conceptual depth. One of the most surprising findings in the research is that trivial, superficial choices that have low cognitive costs (like choosing the color of a pen) has a stronger positive effect on motivation than choices related to the content itself. Having too many open-ended choices in high-stakes activities—like the ever popular, though scientifically debatable Building Thinking Classrooms movement—can make it difficult for students to focus their attention on the deep structural principles that actually matter. In many active classrooms, students learn the mechanics of behavior to mask learning, while not really being engaged in selecting, organizing and integrating information into durable mental schemas. 

Another layer to the voice and choice conundrum—arguably instigated by Carol Tomlinson’s work on differentiation—is the belief that educators should differentiate student products based on learner variability, to demonstrate mastery. For example, if a student struggles with written expression, have them create a podcast rather than a written report. Seems fair enough, right?  Yet this type of work requires different cognitive entry points. For example, expert writing is a knowledge-transforming process, where the act of externalizing arguments physically changes the brain’s schemas. Whereas podcasts, which might reduce the “barrier” of written mechanism, leads to “knowledge-telling,” a less effortful approach where students simply record what they already know without the rigor of revising or organizing their thoughts. This creates a whole conundrum then with assessing learning: how does the teacher fairly grade a wide variety of student products? Not to mention the tension between maintaining objectivity in assessing deep understanding and diagnosing student thinking across different formats. Often, when grading different media, teachers are guilty of hyper focusing on the production process over the content. 

Probably the most pervasive and long-standing folk tale I hear is that of learning styles. While some concede that visual, auditory or kinesthetic learning has no empirical evidence base, it still takes on new life under different guises, for example in frameworks like UDL, which emphasize the need to “match” instruction to a student’s unique needs or preferences. While UDL is often framed as “proactive” in design versus “reactive,” it is almost as popular as the learning-styles myth of decades past and suffers from a similar lack of evidence .

Similarly, neuromyths of “left-brain” analytical thinkers versus “right-brain” creative thinkers is a false dichotomy. The brain is actually a highly integrated organ; even language, often associated with the left hemisphere, involves wide networks across both sides. As disappointing as it may seem to those who long for untapped limitless potential, humans do not only use 20% of their brains—these so-called “silent areas” are constantly mediating higher cognitive functions. Yet there does seem to be near-limitless potential in how much a student can learn in a specific domain. Contrary to Sir Francis Galton’s “talent myths” and fixed ceilings, modern research on deliberate practice shows that expertise is a physical and functional transformation of the brain, not a static trait; differences in performance are largely accounted for by the accumulation of domain-specific chunks of knowledge rather than innate capacity. 

Folk wisdom in education around learner variability is compelling and often framed as morally or equitably justified, but the reality is much simpler. The main way in which students differ in is their domain-specific prior knowledge, which forms the foundation for all future learning (Sweller, 2024; Hattan et al., 2024; National Research Council, 2000; Schneider & Simonsmeier, 2025). Rather than relying on complex engagement tactics like “voice and choice,” focus on addressing true variability. Start by locating students on the novice-to-expert continuum: give novices schema supports, such as worked examples, and gradually fade those supports for more expert learners so they can solve problems independently. Don’t assume needs from a profile; actively surface what students already know and, more importantly, what they misunderstand.  Expert teachers use pedagogical content knowledge to predict conceptual barriers and create cognitive conflict that makes student thinking visible and correctable. Prioritize acquiring and consolidating facts and procedures through deliberate practice and spaced repetition. Once surface knowledge is fluent, move students towards relating and extended ideas to build conceptual understanding.

Our goal should be to help every learner exceed their own expectations, not to chase vague notions of untapped potential. 

References

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National Research Council. 2000. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: The National Academies Press.

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