Four centuries ago, Isaac Newton captured the theory of gravity in a formula that predicts planetary orbits and other phenomena with uncanny accuracy. Yet to this day, exactly how gravity works remains a mystery. Does mass warp the fabric of space, pulling objects together? Or do still-undiscovered quantum particles attract one another?
In a way, the theory of "learning styles" is a bit like gravity. For centuries, as far back as the ancient Greeks, people have observed that human beings have distinct personality types or ways of thinking and interacting with the world. Engineers, for instance, usually aren't great poets, and actuarial scientists aren't prone to becoming the best grief counselors—and vice versa.
For educators, "learning styles" appear to offer one compelling way to reframe learning disabilities as differences: Perhaps some students struggle because we're presenting them with information—or asking them to demonstrate learning—in ways that don't match their learning styles. If we could design learning experiences with these differences in mind, all students might be more successful. Such hopeful thinking has found its way into the federal Every Student Succeeds Act, which calls for states to apply the principles of Universal Design for Learning when planning student assessments and comprehensive literacy instruction.
But Is It Real Science?
But what evidence do we have that learning styles are actually a verifiable and measurable phenomenon? Many learning-styles models have been around for decades, yet to date, little hard scientific evidence has emerged to prove these styles' existence (Coffield, et al., 2004), leaving some researchers to liken the concept of learning styles to pseudoscience—akin to Sasquatch, the mythical beast people prowl the woods in search of.
Nonetheless, the learning-styles concept persists and continues to influence education. So where does the truth lie? Are learning styles like gravity—a real phenomenon we don't yet fully understand—or more like Bigfoot—entertaining to consider, but ultimately, a chimerical pursuit?
Defining Learning Differences
Before we go further, let's define what we're talking about. In the sense we're using here, learning differences have nothing to do with emotional disturbance, Autism Spectrum Disorder, or intellectual disability; all of these are real and well-documented phenomena. Nor do learning styles encompass specific learning disabilities—the umbrella term that generally comprises information-processing challenges like dyslexia, dysgraphia, and dyscalculia.
Rather, learning or cognitive styles attempt to capture people's preferred means of gathering, processing, and evaluating information. Often, these styles are derived from frameworks that, loosely speaking, try to capture dichotomies in how people perceive information (such as distinguishing concrete, verbal, rational analysis from holistic, intuitive, visual intuition) and how they process or express information (distinguishing application-oriented, impulsive doing from meaning-making, contemplative reflecting, for instance).
That's a gross simplification, however. More than 70 different learning-styles frameworks exist, including Ned Hermann's Brain Dominance Instrument, David Kolb's Learning Styles Inventory, Anthony Gregorc's Mind Styles Model and Style Delineator, and John Hayes and Christopher Allinson's Cognitive Style Index, to name but a few. At first blush, these frameworks seem to capture more or less the same concepts, but with competing terminology (Coffield, et al., 2004).
A Closer Look at the Research
For critics, that's the start of the problem; a proliferation of frameworks with no clear underlying constructs makes the whole field appear more speculative than scientific. Moreover, as Coffield and colleagues' (2004) broad overview of research on cognitive styles observed, many models not only lack rigorous external validation, but are also built on a flimsy foundation of self-reported data that raises a host of other validity concerns. For example, is someone really "detail-oriented," or do they just fancy themselves that way?
Claims by most researchers who've developed learning-styles models that learning styles are flexible—that people can reflect multiple preferences and switch among them—pose another problem. As Willingham, Hughes, and Dobolyi note in another review of research on learning styles, "If you're a visual learner on task X, you shouldn't be an auditory learner on task Y" (2015, p. 267); yet that appears to be the case when it comes to cognitive styles. This lack of consistency raises reliability questions about learning-styles research: If an apple can also be an orange, have we defined what an apple is? Willingham and colleagues conclude that "when it comes to learning styles … the most [psychologists] deserve is credit for effort and for persistence" (p. 269).
So are learning styles the equivalent of Sasquatch? Should we leave the woods and pursue something else?
Maybe not just yet. In recent years, a new generation of neuroscience studies has been using brain scanning and eye tracking to validate some of the core underpinnings of cognitive styles (Bendall, et al., 2016). These studies are finding, for example, that people who reported different cognitive preferences on learning-styles inventories—for example, a preference for visual versus verbal learning—also showed different brain activity patterns and eye movements when engaged in solving the same task.
The Real Takeaway
Still, we still have a long way to go before we can draw implications from these sorts of studies to aid classroom teachers. It might be easy to look at the entire field of learning styles as a hopeless mess of competing ideas and unresolved research questions, to wave our hands and say, "Move along folks. Show's over. There's nothing to see here."
Before we do that, however, let's reconsider gravity. We don't fully comprehend gravity, yet we can observe and anticipate its effects. Maybe the same could be said for learning styles. For starters, we can likely detect our own preferences—whether we lean toward big-picture versus detail-oriented thinking, learning alone or in groups, and so on. And perhaps we've seen light bulbs go on for students when we switch from verbal to visual instruction—or vice versa.
Maybe the takeaway from all this research is this: As educators, our inclination is probably to design learning environments that suit how we would best learn. But if we were to acknowledge that our students may learn somewhat differently than we do, and from one another (without getting hung up on particular labels, inventories, or models), would we change how we design their learning experiences?
Ultimately, that's the aim of UDL—to make learning environments accessible to all students by providing multiple ways for them to acquire information, process and demonstrate learning, and connect with learning. And "all students" means those with identified disabilities, those with conditions under the "learning disabilities" umbrella, and each kid with his or her own style of thinking or working. If we bring a loose notion of cognitive styles to these elements, we might consider, for example, how to represent information to students in both verbal and visual ways, how we can help them process and express learning in both experiential and reflective ways, and find meaning and application for learning.
Of course, that's what good teachers do: They get outside their own heads and consider what's happening in their students' minds so that, like Isaac Newton watching an apple fall from a tree, they'll have some eureka moments of their own.
