Categorizing Learners: Bad Uses for Good Tools

by Kyle Skinner

Over the last couple of years, while becoming more invested in developing myself as a teacher, I’ve been asked to take the VARK inventory three or four times. VARK was presented to me as a learning style inventory, or a sort of personality quiz that tells you what kind of “learner” you are—visual, auditory, read/write, and/or kinesthetic. If you haven’t taken the VARK inventory before, you can take it for free here. Whether you’ve taken it or not, you’ve probably taken something like it before. Quizzes or inventories of learning styles like VARK, Kolb’s Learning Styles Inventory, or the Honey and Mumford Learning Styles Questionnaire are easy to find and seemingly ubiquitous. While they follow slightly different models from one another, they are all built to sort students into categories of learners. One study included a list of more than seventy available learning style models, and another even cites statements on an old version of our very own Yale Graduate Teaching Center website to demonstrate the extent to which the idea of learning styles has become popular in the world of pedagogy.

When asked to take these inventories in the context of teaching workshops, facilitators often justify their use of the quiz with vague allusions to the “matching hypothesis,” or the notion that learning outcomes can be improved by matching your presentation of course material to the style of learning that individual students prefer.

I have always been suspicious of VARK and learning inventories like it, but the last time I was asked to take it I got stuck on one particular question:

You are helping someone who wants to go to your airport, the center of town or railway station. You would:

-write down the directions.
-draw, or show her a map, or give her a map.
-go with her.
-tell her the directions.

One of the things I like about the VARK inventory is that you can select multiple answers to any question. But this question in particular (and a few other similar questions) makes me want to explain myself. Depending on whether the recipient of my directions is a beloved friend or a stranger, I might have different feelings about walking her to the airport. I might decide to write down directions instead of drawing a map if the directions would be simple and my piece of paper was small. Most importantly, I might give directions in different ways based on the navigational prowess of the recipient (my sister has such good directional memory that I could give her verbal directions once, but I would rather just put my mother in an Uber to save us both the inevitable frustration). The way I would answer this question would be dependent on context, not on my own personal learning preferences.

Even if the questionnaire did accurately diagnose my learning preference, I find myself skeptical that such diagnoses for my students would be particularly helpful to me as a teacher. When asked what their favorite days of class were over the course of a semester, I would be disappointed but not surprised were some of my more honest students to rank the lessons featuring active learning and hard work below the one day I screened a film. And I wouldn’t blame them—everyone needs an easy day every once in a while, but if curriculum were driven simply by what our students prefer to do, I think we’d see very little learning while YouTube would see a moderate increase in ad revenue.

My intuition seemed to pan out as I looked for evidence-based considerations of the usefulness of the matching hypothesis. A meta-analysis of relevant research revealed no reason to believe that the matching hypothesis is real. I far prefer to learn by reading silently to myself—but that’s because I’m impatient as a listener (as many close friends and former girlfriends would agree) and find that diagrams make me work too hard. But I could probably learn a lot more by becoming a better listener and analyzing diagrams. In fact, when I “match” my learning style, my intuition is that I get less out of it because I end up putting less effort into the learning.

I was feeling pretty misled about the VARK inventory and the like until I found this punnily named article, co-authored by a higher-ed consultant and Neil Fleming, the designer of the VARK questionnaire. I was surprised and delighted to find out that the questionnaire was never designed to be diagnostic, but rather was intended to be the starting point of useful conversations about meta-cognition that might help students themselves become better learners by thinking more about circumstances that aid or stifle learning:

“I sometimes believe that students and teachers invest more belief in VARK than it warrants. It is a beginning of a dialogue, not a measure of personality. It should be used strictly for learning, not for recreation or leisure. Some also confuse preferences with ability or strengths. You can like something, but be good at it or not good at it or any point between. VARK tells you about how you like to communicate. It tells you nothing about the quality of that communication.”

Using VARK as a diagnostic tool to determine what “kind of learners” our students are is a missed opportunity. Used to determine how we can match our teaching to our students’ learning styles, VARK encourages a one-sided teacher-centric classroom. For example, after finding out which learning modalities my students prefer, I can change my curriculum to match their preferences. Teachers should, of course, tailor their curricula to make sure they are meeting learners half-way, but providing information only in a student’s preferred learning modality means the student won’t get practice in learning in other styles. Instead of using VARK to dictate how we should teach or to inform our students that they should focus on particular methods of learning, the inventory could be used as the first step in a series of conversations with students about metacognition, ultimately helping them to develop their own notions of how to learn most effectively in the classroom or while studying on their own time.

There’s nothing wrong, I think, with the VARK inventory. Or any of the learning styles inventories you might find—but even the best tools are only useful when used well.


  1. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review. London: Learning and Skills Research Centre.
  2. Fleming, Neil, and David Baume. “Learning Styles Again: VARKing up the Right Tree!” Educational Developments 7.4 (2006): 4-7. Web. 12 Jan. 2016. <>.
  3. Fleming, Neil. “Introduction to VARK.” VARK. VARK Learn Limited, n.d. Web. 12 Jan. 2016. <>.
  4. Fleming, Neil. “The VARK Questionnaire.” VARK. VARK Learn Limited, n.d. Web. 12 Jan. 2016. <>.
  5. Pashler, Harold, Mark Mcdaniel, Doug Rohrer, and Robert Bjork. “Learning Styles: Concepts and Evidence.” Psychological Science in the Public Interest 9.3 (2009): 105-19. Web. 19 Jan. 2016. <>.

Mobile-Ready Education: Making Education More Accessible

By Stefan Avey

In our increasingly digital world, mobile devices are ubiquitous. On college campuses, cell phone and tablet use by students and teachers alike seems to be increasing with respect to less-mobile laptops and PCs. As a 4th year Ph.D. student in bioinformatics, I spend most of my time in lab working on a desktop computer. When I am not in lab, however, I am likely to learn something by listening to a podcast or watching a video on a mobile device (often while driving, washing dishes, or folding laundry). Given the increasing use of mobile devices, can encouraging students to use mobile devices make learning more accessible?


Mobile-ready education requires adaptation of course material to formats a mobile device can handle, which leads to practical concerns.1 If you expect your students to use a website, is it mobile-friendly? Try Google’s URL tester to find out. If you use videos to teach, are they small enough to be downloaded on a mobile device or accessible as streaming videos? If not, have you considered that some students may not have easy access to a computer and are therefore at a disadvantage?


Beyond the practical considerations, how should we think of mobile-ready education? One useful framework is Universal Design for Learning (UDL) (ACCESS Project 2011). UDL has historically been viewed as a design principle to make learning more accessible to students with disabilities (Tobin 2015). For example, closed captioning was created to aid people with hearing impairments, but it helps many people including, perhaps, college students who do homework after their children go to sleep at night. The idea is simple: putting content in multiple formats, and letting your students choose how to learn, benefits all students. Why? The UDL framework emphasizes giving students an extra option for encountering content, demonstrating learning, and staying engaged. (ACCESS Project 2011) UDL will especially benefit students whose success depends on utilizing multiple learning modalities or capitalizing on opportune moments to study (e.g. on their way to work).2 Indeed, the demographics of the average college student is shifting towards students who are older than 30, have children, or work while in college. A recent report from Georgetown University found that around 40% of undergraduate students work at least 30 hours per week. (Carnevale 2015).3


At this point you may be thinking, “well this is all great in theory but I don’t have the time to make professional videos and mobile apps.” One way to adopt UDL principles with minimal extra work is to use, or ask your students to use, existing technologies. For example, in a science class at Yale, an undergraduate recently gave a final presentation about the chemical structure and degradation of pigments used in oil paints. This student used a free app to demonstrate how a pink pigment that Van Gogh commonly used looked when fresh (Van Gogh’s intended color) and how it currently looks. This award-winning app, Touch Van Gogh, allowed the entire class to dive deeper into Van Gogh’s work without stepping foot in a museum.


There are many excellent resources available online that can be adopted for your course.4 In addition to representing information, mobile devices can be used to engage students and allow them to demonstrate learning. Some examples include encouraging your foreign language students to converse in the language by texting on mobile devices outside of class or giving course credit to your botany students when they post pictures of field work on twitter with a class hash tag.


While UDL aims to increase accessibility, the risk with any online learning is that the content may be permanently or temporarily unavailable. I recently taught an introductory course on Data Science that relied heavily on example websites for in-class demos. A few minutes before class, as I was setting up my laptop, I discovered (to my horror) that the site was down even though I checked that it was working a few hours prior. I scrambled and found a similar tool online but didn’t have time to change my slides to reflect the particularities of the new tool, making that section confusing for my students. I learned that when using online content, having a backup plan helps students be successful. If you give your students an assignment and the website doesn’t work, what should they do instead? What if it only stops working the day before an assignment is due?


A 2007 study found that 94% of distance learners but only 60% of faculty said they were ready for mobile learning. Since 2007, mobile technology has continued to evolve and our students are mostly “digital natives,” while faculty are not. (Corbeil 2007) Mobile-ready education has the potential to help bridge this gap by providing content to students in a more familiar, accessible way. Major challenges include building infrastructure to support faculty developing mobile-ready content and convincing faculty that this is worth the investment. To some extent, the infrastructure can be facilitated by a university-wide learning management system. The Canvas system, currently piloted by Yale, boasts mobile apps for iOS and android that allow easy access to content on the go. In addition, many universities, like Yale, have instructional technologists who support faculty in providing mobile-ready learning. Depending on the strategy, the time commitment can range from a few minutes per lesson to months of developing an app with a support team.


I would love to hear your thoughts on who benefits from a mobile education and how education can be made more accessible by making it mobile-ready?




1. For a more detailed analysis of techniques and frameworks for mobile content adaption, see Chapter 10 of Emerging Perspectives on the Mobile Content Evolution.

2. For more information on UDL including resources and technical modules, see Colorado State University’s excellent UDL webpage.

3. The Georgetown website describing this report gives a good model for mobile-ready education as it is mobile-friendly and represents content in a variety of formats (video, PowerPoint, text, audio, podcast, etc.).

4. To learn about some of these resources, visit the CTL technology resource page or attend CTL advanced teaching workshops on technology (check the CTL website for schedules).



Carnevale, A.P., Smith, N., Melton, M., and Price, E.W. (2015). Learning While Earning: The New Normal.

Quinn, Clark (2000). “mLearning: Mobile, Wireless, In-Your-Pocket Learning,” LineZine. (retrieved November 27, 2015).

Corbeil, J.R., and Valdes-Corbeil, M.E. (2007). Are You Ready for Mobile Learning? Educ. Q. 51–58. (retrieved November 27, 2015).

Thomas J. Tobin (2015). Everyone’s Future: Getting Faculty to Adopt Universal Design for Learning. (retrieved November 17, 2015).

ACCESS Project (2011). Universal Design for Learning: A Concise Introduction. 1–4.