From Categories to Clarity: How Science Debunks the Learning Styles Approach

By John Whitfield. 

Let’s go back to the year 2000, when we held our breath waiting to see if the millennium bug would crash the world, the Nokia 3310 was the must-have mobile phone, and the fledgling ABP were taking their formative steps in the world of Business Psychology. At that time, e-Learning was an exciting new initiative, PowerPoint sessions were seen as cool, and we took quizzes to see if we were visual, auditory, or kinaesthetic learners. Yes, learning styles were a valuable addition to any training intervention.  

The theory of learning styles – made popular by models such as VARK (Fleming & Mills, 1992), Kolb’s Experiential Learning Cycle (1984), and Honey & Mumford’s Learning Styles (1986), amongst others – dominated education and corporate learning. Training professionals chatted sagely about the importance of knowing your learning style. The logic felt irresistible:

"If we teach people the way they learn, surely, they’ll learn better." 

Simple, right? However, fast-forward 25 years, and research tells a different story.  

"No Adequate Evidence"

As early as the 1980s, Richard Snow (1989) suggested that there was weak evidence for learning styles' effects. The first comprehensive academic challenge to the theory came with Coffield et al., (2004) in their paper, ‘Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review’.

The paper examined 71 models and found no strong evidence that matching teaching to individual learning styles improves learning outcomes. They highlighted poor reliability, weak validity, and inconsistent definitions across models, concluding that the concept lacked a sound empirical foundation. The study became a turning point in educational research, shifting the debate from enthusiastic adoption to widespread scepticism.  

In 2008, the learning styles theory was coined as the ‘Matching Hypothesis’ in a paper (Pashler et al., 2008) that stated, “According to the matching hypothesis, instructional methods should be matched to the learner’s preferred mode in order to maximise learning outcomes”.  

Some might say that Pashler et al. were advocates of the matching hypothesis; after all, they invented the term. Those who have read the paper would know that they were far from advocates. They, in fact, claimed that “No adequate evidence shows that assessing learning styles and matching instruction accordingly leads to improved outcomes.”  

Also in 2008, a University of Auckland Professor, John Hattie published Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. In this book, he concluded “the effect size of aligning teaching methods to supposed learning styles was virtually zero”. He argued that what truly matters for learning are factors such as feedback, clarity, cognitive challenge, and prior knowledge, not tailoring instruction to visual, auditory, or kinaesthetic preferences.  

John Hattie would be a leading voice in challenging the learning styles theory and has very recently published a new meta-analysis on the topic, concerned that there seemed to be resurging interest in learning styles as an effective developmental theory.  

In the paper; ‘Learning Styles, Preferences, or Strategies? An Explanation for the Resurgence of Styles Across Many Meta-analyses’ (Hattie & O’Leary, 2025), which draws on data from 17 previous meta-analyses (n = 105,000), the authors suggest that the ‘matching hypothesis’ predicts a learning boost when teaching fits someone’s style, but studies show an effect size of d = 0.04, meaning no meaningful difference. However, correlational studies do show a small link between learning style and achievement (r = 0.24), but this explains less than 6% of performance differences. It doesn’t prove that matching teaching to style helps; it simply shows that some questionnaires capture effective study strategies, not true learning “types.” Correlation isn’t causation. What matters is how learners study, not the ‘style’ they prefer.  

In short, matching instruction to style doesn’t work. 

Why Is It Still So Popular?  

Learning styles are appealing because they align with the brain’s natural preference for simplification and categorisation (Seger, 2013). People tend to make sense of complex phenomena by organising them into neat, intuitive groups, and human learning is certainly a complex phenomenon.  

The idea that everyone has a distinct ‘learning style’ provides a cognitively comforting framework. This categorical thinking also creates an illusion of personalisation: it suggests that learning can be optimised simply by identifying and matching a person’s ‘type’, which feels both scientific and empowering. However, this appeal stems less from evidence and more from cognitive fluency, our tendency to prefer explanations that are simple, familiar, and easy to process (Reber et al., 2004).  

As Hattie and O’Leary (2025) note, despite weak empirical support, learning styles persist because they resonate with educators’ desire for actionable tools and with learners’ desire for self-identity and control over how they learn. In essence, learning styles endure not because they work, but because they fit how the mind likes to think: in tidy, reassuring categories (See also the ‘Barnum Effect’, Forer, 1949; Meehl, 1956). 

What Should Be Advocated Instead? 

Although human learning is a complicated and energy-intensive process (Muzzio et al., 2009), there are things that research tells us can maximise the potential of learning happening. Kaiger and Ford proposed, in their 2020 paper, a framework linking instructional events, learning events/outcomes and instructional outcomes to show how training interventions can lead to meaningful workplace impact. 

For instance, retrieval practice and spaced learning are seen as two of the most effective learning strategies available to educators (Vlach and Sandhofer, 2012; Carpenter et al., 2022). These strategies are highly effective because they optimise how memory is strengthened, consolidated, and accessed in the brain, using systems rooted in both psychology and neuroscience (Smolen et al., 2016). The greatest effects are found when these strategies are combined: spaced retrieval practice leads to optimal long-term retention, resistance to forgetting, and greater transfer of complex knowledge (Yang et al., 2021; Roediger & Karpicke, 2006). 

Honouring the Complexity  

The journey of workplace learning has evolved from intuition to evidence. The challenge now is not to categorise, but to cultivate, rooting development in what science reliably shows works.  

If we move beyond styles and embrace principles like spaced learning and retrieval practice, we honour the complexity of how people truly learn rather than the simplicity of how we wish they did. As psychologists, we must stand firm and challenge these theories based on intuition and brain biases. 

About the Author 

John Whitfield is an evidence-based learning and development professional specialising in behavioural science, leadership capability, and organisational performance. With a master’s in psychology, he brings a research-informed lens to how people learn, adapt, and excel at work. John’s work focuses on making science usable, turning complex concepts into clear, actionable guidance. 

References 

Carpenter, S.K., Pan, S.C. & Butler, A.C. (2022) The science of effective learning with spacing and retrieval practice. Nat Rev Psychol 1, 496–511  

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. 

Fleming, N.D. and Mills, C. (1992) ‘Not Another Inventory, Rather a Catalyst for Reflection’, To Improve the Academy, 11(1), pp. 137–155. 

Forer, B. R. (1949). "The Fallacy of Personal Validation: A Classroom Demonstration of Gullibility." Journal of Abnormal and Social Psychology, 44, 118-123 

Hattie, J. (2008). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. London & New York: Routledge. 

Hattie, J., O’Leary, T. Learning Styles, Preferences, or Strategies? An Explanation for the Resurgence of Styles Across Many Meta-analyses. Educ Psychol Rev 37, 31 (2025). 

Honey, P. and Mumford, A. (1986) The Manual of Learning Styles. Maidenhead: Peter Honey Publications. 

Kolb, D.A. (1984) Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall. 

Meehl, P. E. (1956). Wanted — A good cookbook. American Psychologist, 11(5), 263–272. 

Muzzio, I.A., Kentros, C. and Kandel, E.R. (2009) ‘What is remembered? Role of attention on the encoding and retrieval of episodic memories’, Journal of Physiology, 587(12), pp. 2837–2854. 

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. 

Reber, R., Schwarz, N. and Winkielman, P. (2004) ‘Processing fluency and aesthetic pleasure: Is beauty in the perceiver's processing experience?’, Personality and Social Psychology Review, 8(4), pp. 364–382. 

Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. 

Seger, C.A. (2013) ‘The involvement of corticostriatal systems in categorization learning’, Journal of Neuroscience, 33(6), pp. 2809–2818. 

Smolen, P., Zhang, Y. and Byrne, J.H. (2016). The right time to learn: mechanisms and optimization of spaced learning. Nature Reviews Neuroscience,17(2), pp.77–88. 

Snow, R. E. (1989). Aptitude–treatment interaction as a framework for research on individual differences in learning. In P. L. Ackerman, R. J. Sternberg, & R. Glaser (Eds.), Learning and individual differences: Advances in theory and research (pp. 13–59). New York: W. H. Freeman. 

Vlach, H.A. and Sandhofer, C.M. (2012). Distributing Learning Over Time: The Spacing Effect in Children’s Acquisition and Generalization of Science Concepts. Child Development, 83(4), pp.1137–1144. 

Yang, C., Potts, R., & Shanks, D. R. (2021). Evidence accumulation underpins retrieval practice effects on memory retention. Nature Communications, 12, Article 3563.