the learning styles task force

Model of Learning Styles: Why and How to use the Models

Learning, the only thing that can completely transform our lives and make us better one. Learning will be more comfortable when we prefer Learning styles.

Every day we learn many new skills, new information, new concepts, and many more in our way.

Though you are undoubtedly reading this article, you are a good learner and learn in your way and want to know – is there any great possible way to learn more effectively, efficiently, and accurately?    

To explore this great way of learning, educationalist and psychologist discover many different models of Learning styles through which one can learn better.

The Learning Style models are much more helpful but have a drawback because everyone has a different learning style on different topics.

So applying these models of learning style one everyone in every situation will not be appropriate.

Despite all the drawbacks, understanding these models of learning style can boost up your learning capabilities, and you can explore your full potential.

So in this article, Let’s explore the different modules of learning styles discovered by the experts of the learning field. 

WHAT’S IN IT

David Kolb’s model of learning style

Firstly, The learning style by David A. Kolb is completely based on experimental learning.

Kolb’s model relates to four modes, such as Concrete experience and Abstract conceptualization for grasping experience, whereas Reflective observation and Active experimentation for transforming experience. 

This model forms a learning cycle such as Experience, Observation, conceptualization and experimentation, then it will then reverse back to the experience and then follow the next steps of the cycle.

Kolb also mentions that to make your learning more productive, you have to incorporate all these four modes. He advises preferring one of the following four styles of learning that is Accommodator, Converger, Diverger, and Assimilator.

• Accommodator, which is Concrete Experience with Active Experiment, is strong in practical doing.
• Converger, which is Abstract Conceptualization with Active Experiment, is strong in the practical application of theories.
• Diverger, which Concrete Experience with Reflective Observation is strong in discussion and imaginative ability.
• Finally, the Assimilator , which is Abstract Conceptualization with Reflective Observation, is reliable in making theories and inductive reasoning. 

Peter and Alan’s model of learning style 

At first, Peter Honey and Alan Mumford use the experimental learning model discovered by David Kolb .

But later, based on their managerial experiences, they renamed the four stages of the learning cycle of Kolb as Activist, Reflector, Theorist, Pragmatist.

Peter Honey and Alan Mumford’s learning style questionnaire (LSQ) is a self-development tool, and it differs from Kolb’s model.

In 1999, A survey by the campaign for learning found that LSQ by Peter Honey and Alan Mumford is the most useful learning style in the UK.

VAK Modalities

Visualizing, Auditory and Kinesthetic (VAK) Modalities are proposed by educational psychologist Walter Burke Burble and colleagues.

Visualizing modalities contains picture, shape, sculpture, and painting also. The auditory modalities contain listening, rhythms, tone, charts, and last but not least Kinesthetic contains gestures, body movements, object manipulation, positioning.

According to Barbe and Colleagues, a model of learning style can occur individually or in combination. Although visual or mixed modality is the most frequent according to their research.

In those years, the VAK model of learning style was widely used by the people. But later, like the other model of learning, it also has a drawback.

Psychologist Scott Lillienfeld has mentioned that the excess use of the VAK learning style model is just nothing more than pseudoscience.

Anthony Gregorc’s model of learning style

This model of learning by Anthony Gregorc consists of two perceptual abilities as well as two ordering abilities.

Concrete and Abstract are the two perceptual abilities, whereas random and sequential are the two ordering abilities.

The Concrete perceptual ability registers the information provided through five senses, while the Abstract perceptual ability understands the qualities and ideas of the concept.

Likewise, perceptual ability, the Sequential ordering ability, organizes the information linearly and logically, whereas random ordering ability organizes in a chunk, not in a specific order.

This model suggests four combinations of the abilities based on the dominance – Concrete random, Concrete sequential, Abstract random, Abstract sequential.

This learning style by Anthony also says that someone with one of the four combinations learns in a completely different way than someone with another combination and also says the strengths, questions asked during learning, doubts will completely differ from each other.

VARK Model of Learning Style

Firstly, Neil fleming’s VARK model is the extended model of Barbe and colleagues’ VAK model. 

Here four modalities are suggested by Fleming in his model of learning style. These are Visual learning, Auditory learning, Physical learning, and Social learning. 

Neil Fleming suggests that every learner learns in his style of learning. According to him, Visual learner prefers to learn through seeing objects like diagrams, charts, etc.

Likewise, Auditory learner prefers learning through listening whereas Kinesthetic learner prefers to learn through experience like doing experiments.

Fleming suggests students find their learning style and focus on these styles, which help them improve their learning.

Neil Fleming also suggests that one can prefer a single modality of learning or multiple learning modalities as per their choice, which benefits them most. 

Cognitive model

Firstly, In 1974, Anthony Grasha and Sherry Reichmann proposed a cognitive model known as Grasha-Reichmann’s learning style.

It was mainly focused on the student’s attitude and their approach towards learning. 

Also, In this model of learning, Anthony Grasha and Reichmann mainly distinguish between adaptive styles and maladaptive styles. These learning styles’ names are avoidant, participative, competitive, collaborative, dependent, and independent.

It was mainly designed to provide insight knowledge to the college teachers on how to deal with instructional plans for their students.

NASSP Learning Style

A task force named as National Association of Secondary School Principals (NASSP) was formed in the 1980s to take a study over learning styles. 

The force found the majorly of three categories of style – Cognitive, Affective, Physiological, with 31 variables, including the Barbe and Colleagues’ VAK model.

Cognitive styles of learning are used for perception, retention, and organization. Attractive styles show the learners’ motivational dimensions.

Physiological style is based upon learners’ condition of health, his well-being, and the surrounding from where he is learning.

The NASSP team suggests teachers observe every individual student’s behaviour through which the teacher can recognize the best learning style for that student.

Criticism on models of learning styles 

Many psychologists, neuroscientists, learning scholars, and researchers have questioned the scientific basis and criticized those models.

Lastly, The serious concern about this argument is that – In the classroom, the use of a specific learning style leads students to self-limit themselves, which is more harmful than beneficial.

Some researchers also suggest that learning style can be better under a specific condition in the long-term, which is next to more challenging, and teaching students with their learning style will not valid.

 Some psychologists also suggest that students learn more than before if the preferred learning style matches the student’s learning style.

Uses Of Learning Style

On the above, you find a lot of criticism about the models of learning style. Now you also have a doubt that is using a learning style good or not?

All the learning styles may be good or bad. But it entirely depends upon you which modules of learning style are better for you, which can develop your way of learning.

In this article, we have entirely discussed the models of learning styles. One of the key points is that we can be successful learner if we can find which learning styles suit us.

To find an effective learning style, we should have to analyze all the modules to find the most relevant learning style that can improve our learning and help us approach new learning in a better and easier way, makes us better at studies, and many more.

Also you can read our blog on  Powers of Observation: 8 Analyzed Ways to Develop it

Learning styles means a scientifically proven way to enhance the quality of learning. Not all Learning styles follow blindly, we have to search for the best Learning style, which suits us.

Ans:-  The models of learning style are:- David Kolb’s learning model  Peter and Alan’s learning model  VAK Modalities Anthony Gregorc’s model of learning style VARK Model Cognitive model NASSP Learning Style

Yes, The use of the Learning style will be beneficial if you find the particular Learning style that suits your Learning way.

It’s upon you to find the best. All the learning styles are suitable. But the best Learning style is that you find more effective on you when you follow.

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Article • 14 min read

Learning Styles

The models, myths and misconceptions – and what they mean for your learning.

By the Mind Tools Content Team

It's tempting to try to pin down one "perfect" way of learning. But it can also be dangerous.

Everyone's approach to learning is based on a complex mix of strengths and preferences. And we absorb and apply new concepts, skills and information in different ways at different times.

So, however helpful it would be to find out how each of us does it "best," there are many reasons why even asking the question is far from straightforward.

After all, how we learn depends a great deal on what we're learning. And our preferred learning techniques might not, in fact, be the most useful. Despite this, many scientists, psychologists and education experts have tried to identify distinct, innate "learning styles."

But serious doubts have arisen about some of the most popular models – especially the ways in which they have been applied. There are even concerns that the "labels" they produce might actually limit people's learning.

In this article, we look at how the key learning styles theories were developed, and explore their intentions and limitations. We also show why it's still valuable to understand your personal approach to learning – even if there's no single, "magic bullet" solution for any of us.

What Are Learning Styles?

The notion that everyone has their own learning style became popular in the 1970s. It's an attractive thought: if each of us could identify one, "ideal" approach to learning, we'd be able to focus on it – and be consistently successful.

What's more, by understanding other people's needs, we'd know how best to support them to learn. It could revolutionize education, training and L&D, and help all of us to reach our full potential as learners.

Before we explain why many experts now have little faith in learning styles, let's explore how some of the original ideas came about.

Learn more about the theories behind learning styles – and their drawbacks and limitations.

Different Learning Styles: 6 Influential Models and Theories

1. david kolb and experiential learning.

David Kolb's model of "experiential learning" stated that we learn continually, and, in the process, build particular strengths. Those strengths were said to give rise to personal preferences, which Kolb described in terms of four learning styles: Accommodating , Converging , Diverging , and Assimilating .

As Kolb saw it, Accommodators were "hands-on" types, keen to learn from real experience.

Convergers were supposed to deal better with abstract ideas, but still liked to end up with concrete results. They understood theories, but wanted to test them out in practice.

Divergers tended to use personal experiences and practical ideas to formulate theories that they could apply more widely.

And Assimilators , according to Kolb, were most comfortable working with abstract concepts. They extended their understanding by developing new theories of their own.

Kolb said that it was beneficial to know which type of learner you were, in order to "play to your strengths." He also believed that educators and trainers could tailor their teaching methods to different people's learning styles.

2. Honey and Mumford's Learning Styles

Peter Honey and Alan Mumford developed Kolb's model by focusing on how learning is used in practice, particularly at work. They identified four new learning styles: Activist , Pragmatist , Reflector , and Theorist – using terms that we might naturally pick to describe ourselves and our colleagues.

To find out more about Kolb's model, and about Honey and Mumford's Learning Styles, see our article on the 4MAT approach to learning.

3. Anthony Gregorc's Mind Styles

Anthony Gregorc and Kathleen Butler went into more detail about how we think, and how this might affect the way we learn.

This theory put us all on a spectrum between concrete and abstract thinking, and between sequential and random ordering of our thoughts.

• Concrete perceptions happen through the senses, while abstract perceptions deal with ideas.
• Sequential thinking arranges information in a logical, linear way, while a random approach is multidirectional and unpredictable.

In Gregorc's model, our strengths and weaknesses in each of these areas determined our individual learning style.

4. 4 Learning Styles (VARK)

Educational psychologist Walter Burke Barbe and his colleagues proposed three "modalities" of learning: Visual , Auditory , and Kinesthetic (movement and touch). These were often referred to simply as VAK.

A variation on the acronym, developed by New Zealand-based teacher Neil D. Fleming, is VARK® , or visual, auditory, reading/writing, and kinesthetic. You can find out more about both VAK and VARK in our article, VAK Learning Styles .

Visual Learning Style

A visually-dominant learner absorbs and retains information better when it is presented in, for example, pictures, diagrams and charts.

Auditory Learning Style

An auditory-dominant learner prefers listening to what is being presented. They respond best to voices, for example, in a lecture or group discussion. Hearing their own voice repeating something back to a tutor or trainer is also helpful.

Reading/Writing Learning Style

People with a dominant reading-and-writing learning style take in new information best when they read it as words and text. They're often good at summarizing information in written notes.

Kinesthetic Learning Style

A kinesthetic-dominant learner prefers a physical experience. They like a "hands-on" approach and respond well to being able to touch or feel an object or learning prop.

Barbe was clear that everyone had strengths, weaknesses and preferences in each of the VAK modalities. The most effective learning, he said, utilized all three in combination. He said that the mix we achieved depended on many factors, and would likely change over time.

The VAK model was popular and widely applied. But, like some of the earlier models, it became associated with a fixed outlook on learning. Many people took it to mean that learners could be classified by a single modality – as a "visual learner," for example – with little room for maneuver. And there was confusion over whether the VAK definition referred to someone's innate abilities, their personal preferences, or both.

5. The Learning Styles Task Force

In the 1980s, American educationalists were still trying to find out as much as they could about learning styles, to help classroom teachers to achieve the best possible results.

The National Association of Secondary School Principals (NASSP) formed a research "task force," and proposed additional factors that might affect someone's ability to learn. These included the way study was organized, levels of motivation, and even the time of day when learning took place.

They divided learning styles into three categories: Cognitive , Affective and Physiological .

• Cognitive: how we think, how we organize and retain information, and how we learn from our experiences.
• Affective: our attitudes and motivations, and how they impact our approach to learning.
• Physiological: a variety of factors based on our health, well-being, and the environment in which we learn.

6. The Index of Learning Styles™

Various related questionnaires and tests quickly came into use, aimed at helping people to identify their personal learning style. One of the most popular was based on The Index of Learning Styles™ , developed by Dr Richard Felder and Barbara Soloman in the late 1980s.

The questionnaire considered four dimensions: Sensory/Intuitive , Visual/Verbal , Active/Reflective , and Sequential/Global . The theory was that we're all somewhere on a "continuum" for each of them. Neither extreme was said to be "good" or "bad." Instead, we'd do best by drawing on both ends of the spectrum.

Questionnaires like this promised to define anyone's learning style, so that they could address any "imbalances," and learn in the ways that would benefit them most.

Criticisms of Learning Styles

These and other theories about learning styles have become extremely popular and widespread. However, a growing body of research has challenged many of their claims.

Let's look at the four key criticisms that have been leveled against them:

1. The Science Isn't Strong Enough

We may express our preferences about how we learn, but they're not necessarily an accurate reflection of how our brains work. According to neuroscientist Susan Greenfield , the idea that we can be defined as purely visual, auditory or kinesthetic learners is "nonsense." That's because, she says, "humans have evolved to build a picture of the world through our senses working in unison, exploiting the immense interconnectivity that exists in the brain."

A study by Massa and Mayer also found little difference in learning outcomes when they matched their test subjects' preferences (visual or verbal) to the learning materials they were given.

2. Learning Styles Change

Attempts to "diagnose" someone's learning style once and for all will likely fail. As Eileen Carnell and Caroline Lodge explain in their book " Effective Learning ," an individual's learning method will be different in different situations, and likely change over time.

3. Strengths and Preferences Are Not the Same

An influential piece of research published in the Journal of Educational Psychology revealed big differences between people's assessed strengths, and how they actually tackled learning tasks in practice. For example, someone who scores better in tests after hearing the information might still choose to learn by reading – simply because they enjoy that style of learning more.

4. Teaching to Particular Learning Styles Doesn't Work

For psychologist Scott Lilienfeld, the idea that "students learn best when teaching styles are matched to their learning styles" is one of the " 50 Great Myths of Popular Psychology ." This, he says, "encourages teachers to teach to students' intellectual strengths rather than their weaknesses," limiting their learning as a result.

Using Learning Styles to Improve Learning

Despite the criticisms we've outlined, some of the ideas that underpin learning styles theories still have value – especially the emphasis on metacognition: "thinking about thinking."

One influential collection of research cast doubt on specific learning styles models, but was still positive about metacognition. And metacognition has been shown to improve educational outcomes – leading the Education Endowment Foundation to recommend it as a key teaching and learning tool.

Analyzing our thinking can help us to plan learning strategies that work for us. It can support us to become more organized in our studies, to use prior knowledge as the foundation for new learning, and to choose effective methods for different learning tasks.

Plus, by examining our strengths and weaknesses, we can make the most of any aspects of learning that "come naturally" and that we enjoy, while also working on the areas that might be holding us back.

If you're eager to improve your personal approach to learning, here are three key steps to take:

1. See the Big Picture

Do everything you can to gain a rounded picture of your learning. Look at all the different reasons why you tend to tackle learning the way you do.

And, when you're in the process of learning, ask yourself why you're doing it a particular way. Is it because it's the most effective for you, or simply because it's what you've always done?

Be wary of definitive judgments. Instead, consider different scenarios, and try to differentiate between how you like to learn, and how you learn best – in a variety of learning situations.

2. Identify Your Strengths

Highlight the types of learning that work best for you, and the conditions for learning that support them. For instance, you might be more of an active learner, who operates best in groups.

Keep doing the things that give the best results, to keep your learning fast and effective – and look for ways to improve them even more.

But also leave room to practice and strengthen any learning behaviors that you find more difficult.

3. Work on Your Weaknesses

You can often improve areas of your learning that are letting you down simply by using them more.

If you feel that you're not confident learning visually, for example, get into the habit of reading the charts and diagrams in an article before grappling with the ideas in the text.

Or, if you're an independent learner by nature, make a point of involving others in your problem-solving from time to time.

Also, actively look for opportunities to try out new ways to learn. You might be surprised about what works – and about the new elements of learning that you enjoy.

How to Help Other People to Learn

Becoming more aware of your own strengths and preferences helps you to appreciate and cater for the diverse ways in which others learn, too.

For example, when you're giving a presentation, chairing a meeting, or leading a training session, avoid leaning too heavily on the approach that you would enjoy yourself.

Remember that some learners will benefit from visual aids, while others will rely on listening to what you say, or on watching your body language. Back up abstract theories with real-life examples. Spend time discussing small details as well as outlining large-scale ideas.

You can't always cater for everyone, but you can better engage your audience by allowing for different approaches to learning. If nothing else, your varied approach will keep people energized and alert!

Frequently Asked Questions

What is the kinesthetic learning style.

A learner with a preference for the kinesthetic learning style prefers a physical experience. They like a "hands-on" approach and respond well to being able to touch or feel an object or learning prop.

Can you have two learning styles?

Yes. Or more than two. Very few people, if any, are completely reliant on one learning style. They may favor, say, visual learning, but still be able to learn by reading and writing.

• "Learning Styles" theories attempted to define people by how they learn – based on individual strengths, personal preferences, and other factors such as motivation and favored learning environment.
• Many different Learning Styles models were developed, but even the most popular ones have now been called into question. The main criticisms are that they are unscientific, inflexible, and ineffective in practice.
• However, it's still worth using metacognition – "thinking about thinking" – to work out what does help you to learn. That way, you can play to your strengths, develop any weaker areas, and create the best conditions for learning.
• This level of awareness can also help you to communicate with greater impact, and to support other people to learn.

Butler, K. A. (1988). ' It's All In Your Mind ,' Columbia, CT: Learner's Dimension.

Carnell, E. and Lodge, C. (2002). ' Supporting Effective Learning ,' London: Paul Chapman Publishing.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning Styles and Pedagogy in Post-16 Learning: a Systematic and Critical Review. LSRC Reference, Learning & Skills Research Center, London. Available here .

Education Endowment Foundation (2018). Metacognition and Self-Regulation [online]. Available here . [Accessed November 13, 2019.]

Felder & Soloman. Index of Learning Styles Questionnaire [online]. Available here . [Accessed November 1, 2019.]

Henry, J. (2007). Professor Pans "Learning Style" Teaching Method [online]. Available here . [Accessed November 1, 2019.]

Honey, P., & Mumford, A. (1982). ' The Manual of Learning Styles .' Maidenhead: Peter Honey.

Keefe, J. W. (1985). 'Assessment of Learning Style Variables: the NASSP Task Force Model,' Theory into Practice , 24(2), 138-144. Available here .

Kolb, David A. (2015). ‘ Experiential Learning ' (2nd ed.), Upper Saddle River, NJ: Pearson Education.

Krätzig, G. P. and Arbuthnott, K. D. (2006). 'Perceptual learning style and learning proficiency: a test of the hypothesis,' Journal of Educational Psychology , 98(1), 238-246. Available here .

Lilienfeld, S. O., Lynn, S. J., Ruscio, J., & Beyerstein, B. L. (2010). ' 50 Great Myths of Popular Psychology ,' Chichester, UK: Wiley-Blackwell.

Massa, L. J., & Mayer, R. E. (2006). 'Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style?' Learning and Individual Differences , 16(4), 321-335. Available here .

Pashler, H. et al. (2008). ‘Learning Styles: Concepts and Evidence,’ Psychological Science in the Public Interest , 9(3), 105-19. Available here .

VARK is a registered trademark of Vark Learn Ltd., see www.vark-learn.com .

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• Published: 17 October 2023

Beware the myth: learning styles affect parents’, children’s, and teachers’ thinking about children’s academic potential

• Xin Sun 1 ,
• Owen Norton 2 &
• Shaylene E. Nancekivell 2 , 3  

npj Science of Learning volume  8 , Article number:  46 ( 2023 ) Cite this article

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Three experiments examine how providing learning style information (a student learns hands-on or visually) might influence thinking about that student’s academic potential. Samples were American and predominately white and middle-class. In Experiment 1, parents ( N  = 94) and children ( N  = 73, 6–12 years) judged students who learn visually as more intelligent than hands-on learners. Experiment 2 replicated this pattern with parents and teachers ( N  = 172). In Experiment 3 (pre-registered), parents and teachers ( N  = 200) predicted that visual learners are more skilled than hands-on learners at “core” school subjects (math/language/social sciences, except science), whereas, hands-on learners were skilled at non-core subjects (gym/music/art). Together, these studies show that learning style descriptions, resultant of a myth, impact thinking about children’s intellectual aptitudes.

Introduction

The learning style myth is widely endorsed by educators and the general public across countries, including those in the United States, Turkey, Portugal, China, Switzerland, the United Kingdom, and Latin America 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . The most common, but erroneous, models of learning styles focus on children as possessing a dominant way of learning tied to a singular learning modality, such as a visual, auditory, or kinesthetic/tactile learning style (i.e., the VAK model 1 , 12 ). Despite its popularity, there is no substantial evidence to support the idea that people have distinct dominant styles of learning that are tied to a singular modality 13 , 14 , 15 , 16 . For example, there is no evidence that VAK learning styles predict cognitive functioning or learning. VAK learning styles are unrelated to the evidence-based ways that researchers have successfully characterized individual differences in thinking 17 , 18 , 19 , 20 .

The present study investigates an overlooked, but likely serious consequence of the learning style myth: how learning style information may influence parents’, teachers’ and children’s thinking about young students’ potential. Scientists have long argued that the main detriment of the learning style myth is that it leads educators to waste resources on ineffective methods that could be spent on evidence-based ones 13 , 14 , 15 , 16 . However, the pervasiveness of the myth makes it likely that its consequences extend beyond solely wasted resources. The current study addresses this issue by examining whether teachers, parents, and children think those described as having certain learning styles are smarter than others (i.e., learning better with one’s hands as a hands-on vs. learning better with one’s eyes as a visual learner), and similarly, how learning style information affects teachers’ and parents’ thinking about young children’s ability to excel in different school subjects.

Prior work suggests that embedded in the (VAK) learning style myth may indeed be erroneous beliefs about young students’ educational abilities. There is also a large body of work treating learning styles as real phenomena, which has sought to uncover “field-specific” beliefs about learning styles and their link to academic success (e.g., STEM learning; medicine 21 , 22 ). Inherent in this work is a similar suggestion that certain learning styles may be more common and/or more likely to lead to success than others in certain academic fields. For example, one study erroneously linked visual learning style with academic success in applied science courses 23 while another study concluded that medical students have a different learning style than the general population 21 . Moreover, these kinds of links have been spread across university settings with staff from teaching centers lecturing on how auditory learners “ flourish in foreign language learning ”, and “ chemists and engineers are often kinesthetic learners ” 24 . Together, prior work suggests that it is likely that supposed learning styles are viewed as informative about children’s intelligence, and their success in specific academic domains (art vs. science).

Several recent empirical works probing learning styles beliefs provide theoretical support for the proposal that people link learning styles to student potential. They specifically suggest that many people view learning styles through an essentialist lens 6 , 25 . Psychological essentialism is a belief that a category has “true nature” that is based in biology, and which determines the behavior of its category members 26 , 27 . In the case of learning styles, this work showed that people believe that learning in a kinesthetic, auditory, or visual manner are markers of stable categories (e.g., visual or kinesthetic/hands-on learning styles) that are based in the biology of the brain, and predict life and educational outcomes 6 . A hallmark of such essentialist reasoning is that it facilitates specific inductive generalizations about behavior 28 . For example, in the case of gender, essentialist reasoning leads children to believe that a girl, by virtue of being a girl, will play with dolls, and wear dresses 29 . In the case of learning styles, prior work has only examined whether people generally agree that their perceived learning styles predict learning outcomes, and not how they predict specific behaviors or academic performance. In other words, prior work has not examined the kinds of predictions or inductive inferences about academic performance that learning styles might lead to. The present study seeks to answer this question by examining the specific inferences that learning styles lead parents, teachers, and children to make about young students’ educational outcomes and potential (i.e., how ‘smart’ a child is, or good at math).

The possibility that people may believe that perceived learning styles are predictive of student potential is concerning. From childhood, people’s theories of learning are complex and have consequences on their thinking about themselves and other people 30 , 31 , 32 . By first grade, children hold implicit theories of intelligence 33 , and many view intelligence as a stable fixed trait 32 . Young children also hold stereotypes about intelligence. For example, 6-year-olds have gendered notions of brilliance wherein they believe that boys are more likely than girls to be “really really smart” 30 , and they similarly think that boys are better at math than girls 34 , 35 . At this age, racial stereotypes also influence children’s thinking about achievement: They associate white men, but not Black men with brilliance 36 . In adulthood, such stereotypes remain. They additionally influence thinking about who is likely to be smart, and participate in STEM fields 37 , 38 .

Further, such beliefs have ramifications for children: They affect their educational preferences and engagement in science 30 , 34 , 35 , 38 . They influence children’s perceptions of classmates, thereby impacting peer acceptance and classroom engagement 39 . In adulthood, stereotypes related to brilliance are related to the distribution of women in scientific fields, and to experiences of imposter syndrome 37 , 40 . Parents’ beliefs about their child’s math abilities are related to their child’s math achievement 35 . Educators’ beliefs about whether intelligence is fixed at birth are related to their students’ achievement, especially among stigmatized groups 41 . Taken together, these bodies of work demonstrate that people’s beliefs about learning and intelligence emerge early and have measurable real-life consequences.

The Present Studies

Here, we conduct three experiments which are the first to test whether (and how) learning style information influences thinking about children’s academic potential. Throughout these experiments, we rely on descriptions (e.g., “a student who learns best by using their hands”) instead of only labels (e.g., “hands-on learner”) to account for potential differences in label familiarity among children, parents, and teachers (e.g., tactile, hands-on, and kinesthetic are all similar labels). Moreover, the reliance on descriptors is ecologically valid as such descriptors are always provided in learning style assessments and reports 42 , 43 . We modeled our descriptors closely after such real-life discussions (see Supplementary Table 1 for descriptions from online blogs and learning styles assessment tools).

In Experiment 1, children and parents were introduced to two young students who were described as a visual or hands-on learner. They were then asked to rate their intelligence and sportiness (athleticism). We include children as one of the samples to explore whether they may embrace the idea of learning styles and how they may reason about learning styles. In Experiment 2, parents and teachers were asked to select which learner was smarter in a forced-choice scenario. In both these experiments, we compare thinking about intelligence to athleticism/sportiness to ensure findings are not the result of a global halo effect wherein one kind of learner is indiscriminately rated higher. Experiment 2 predicted that the effect we found in Experiment 1 would be amplified in this forced-choice ranking scenario. Children were not tested in Experiment 1 or subsequent studies due to their limited availability during COVID-19 pandemic. To dive deeper into the perceived weaknesses and strengths of each learner, in Experiment 3 (pre-registered), parents and teachers predicted the report card grades of hands-on and visual learners across common elementary school subjects. As many real-world discussions (i.e., online educational blogs) often mistakenly associate hands-on learners with sports, arts, and visual learners with core school curriculum (e.g., reading 44 , 45 ), we hypothesized that the visual learner would be rated as more intelligent and competent at core school subjects (i.e., math, language arts, science, and social studies) whereas the hands-on learner would be rated as more sporty and competent at non-core school subjects (i.e., arts, gym, and music). The predicted link between aptitude (intelligence) and core subjects was driven by both real word discussions (see review above) and by prior work examining which fields are thought to require brilliance 30 . The data, analytic code, and materials for Experiments 1 to 3 are publicly accessible: Link . Experiment 3 is preregistered at: Link .

Experiment 1

Analytic plan.

Parents’ and children’s answers were scored using a four-point scale as follows: 1 as not smart or sporty, 2 as sort of smart or sporty, 3 as smart or sporty, and 4 as really smart or sporty. Next, linear mixed-effects regression models were built using R package lme4. Cohen’s d was calculated for effect sizes. 95% Confidence intervals were calculated using Wald method. In all models, random effects were modeled for each participant as (1|id). Post hoc simple effects tests were conducted with R package emmeans and p -values were Bonferroni corrected. For critical effects, post hoc sensitivity analyses were conducted using the R package simr. Children and adults participated in slightly different variants of the study, and data were analyzed separately.

Children’s responses

We first fit an omnibus mixed linear effects model including main effects of Age (centered, continuous in months), Learning Style (visual, hands-on), and Question Type (smart, sporty) and all interaction terms to test which factors predicted participant ratings. There was a significant Learning Style main effect, b LearningStyle  = 0.22, 95% CI = [0.09, 0.34], p  < 0.001, d  = 0.47. The interaction between Question Type and Learning Style was also significant, b Learning Style*Question  = −0.20, 95% CI = [−0.32,−0.08], p  = 0.001, d  = −0.44. A post hoc sensitivity analysis determined that the sample was adequately powered to detect this main interaction, 90.30% power with a 95% CI = [88.30%, 92.06%]. All the remaining terms were non-significant.

We next decompose the significant two-way Question Type by Learning Style interaction using follow-up simple effects contrasts (Bonferroni corrected for two tests). Results showed that children viewed visual learners ( M(SD) Visual  = 2.88 (0.99)) as significantly smarter than hands-on learners ( M(SD) Hands-on  = 2.26 (1.09), Fig. 1 left panel), t  = 3.48, p adjusted  = 0.001, d  = 0.58. In contrast, children rated visual and hands-on learners ( M(SD) Visual  = 2.04 (1.17)) as similarly sporty ( M(SD) Hands-on  = 2.23 (1.21), Fig. 1 left panel), t  = 1.08, p  = 0.540, d  = 0.18. Scatterplots, provided in our supplementary materials, illustrate the absence of an age effect. These graphs plot each Question Type and Learning Style and are presented in Supplementary Fig. 1 .

Left panel: ratings from the child sample. Right panel: ratings from the parent sample. Ratings were converted to 1-4 to indicate how smart/sporty the participants think about the student described as a visual/hands-on learner. 1 - not very smart/sporty, 2 - sort of smart/sporty, 3 - smart/sporty, 4 - really smart/sporty.

Parents’ responses

A similar set of analyses were then conducted with the parent sample but without age. Results showed that all main effects and interactions were significant. For the main effect of Question Type, b Question  = 0.16, 95% CI = [0.10, 0.23], p  < 0.001, d  = 0.56. For the main effect of Learning Style, b Learning Style  = 0.10, 95% CI = [0.03, 0.17], p  = 0.007, d  = 0.32. For the interaction between Learning Style and Question Type, b Learning Style*Question  =–0.33, 95% CI = [–0.40, –0.26], p  < 0.001, d  = –1.11. A post hoc sensitivity analysis determined that the sample was adequately powered to detect this main interaction, 100% power with a 95% CI (99.63%, 100%).

To parse out the interaction effect, we again conducted follow-up simple effects contrasts (Bonferroni corrected for two tests). Results showed that parents rated visual learners ( M(SD) Visual  = 3.02 (0.49)) as significantly smarter than hands-on learners ( M(SD) Hands-on  = 2.55 (0.74), Fig. 1 right panel), t  = 4.66, p adjusted  < 0.001, d  = 0.68. In contrast, parents viewed hands-on learners ( M(SD) Hands-on  = 2.88 (0.79)) as significantly sportier than visual learners ( M(SD) Visual  = 2.03 (0.99), Fig. 1 right panel), t  = 8.47, p adjusted  < 0.001, d  = 1.24.

Experiment 2

We fit a mixed binary logistic regression to examine whether question (smarter and sportier) and group membership (teacher and parent) predicted participants’ selections (the visual or hands-on learner). Identical to Experiment 1, random effects were again modeled for each participant. The same packages were used as in Experiment 1. The main difference was that due to the binary nature of the DV, odds ratios are reported instead of Cohen’s d.

Figure 2 displays the percentage of participants’ answers who selected each student type (visual or hands-on learner) divided by question (smarter and sportier) and participant group (teacher and parent). The main effect of the question type was significant, b  = 1.61, 95% CI = [1.29, 1.92], z  = 9.95, p  < 0.001, odds ratio = 4.99. There was no main effect of group on judgments (parent or teacher), b  = 0.25, 95% CI = [–0.07, 0.56], z  = 1.53, p  = 0.126, odds ratio = 1.28. But there was a significant interaction between the question and group, b  = –0.43, 95% CI = [–0.74, –0.11], z  = –2.63, p  = 0.008, odds ratio = 0.65. As shown in Fig. 2 , participants were more likely to characterize the visual learner as smarter than the hands-on learner, but the hands-on learner as sportier than the visual learner. In terms of interaction, teachers showed a larger effect than parents (again see Fig. 2 ).

a percentage to the question “Which student is smarter”. b percentage to the question “Which student is sportier” Each bar indicates the percentage that the sample selected the visual or hands-on learner as “smarter” or “sportier”, adding up to 100%.

For the exploratory open-ended question, we then drew word clouds for each learner style (Fig. 3 ) to help us to visualize the frequency with which different subjects were listed. As shown in Fig. 3 parents and teachers viewed each learner as having different educational strengths. Next, we break down those strengths using frequency data.

Color and word size indicate the frequency of the word (word stems used).

Visual learner

Teachers viewed visual learners as likely to excel at math/mathematics (22.65%), history (14.89%), English (9.39%), art (8.74%), and reading (5.83%). Similarly, the top five subjects listed by parents were math/mathematics (19.56%), English (9.96%), art (7.75%), history (7.75%), and reading (5.54%).

Hands-on learner

Teachers viewed hands-on learners as likely to excel in science (19.14%), art (16.83%), gym (11.55%), math (6.60%), and music (5.28%). Similarly, the top five subjects listed by parents were science (13.52%), art (11.03%), gym (6.76%), chemistry (5.34%), and physics (4.63%). We note that collapsing across subcategories of science (e.g., chemistry, physics) does not change the nature of the top three subjects.

Experiment 3

The same packages and analytic method were used as in Experiment 1. However, we did not use Bonferroni correction because of pre-registration.

Pre-registered Hypothesis 1

Visual learners will be rated as having higher grades than hands-on learners for “traditional” core school subjects (e.g., math, science, social studies, language arts). In contrast, we predict that hands-on learners will be rated higher on non-core subjects than visual learners (e.g., art, music, gym).

We ran a mixed linear effects model including main effects of Subject Type (core, non-core), Learning Style (visual, hands-on), and Sample (teacher, parent) and all their interaction terms to predict grade ratings. There were significant main effects of Learning Style, b Learning Style  = 0.07, 95% CI = [0.004, 0.128], p  = 0.036, d  = 0.08, and Subject Type, b Subject Type  = –0.69, 95% CI = [–0.75, –0.63], p  < 0.001, d  = –0.86, but not Sample, b Sample  = –0.05, 95% CI = [–0.17, 0.08], p  = 0.456, d  = –0.11. There was a significant interaction between Learning Style and Subject Type, b Learning Style*Subject Type  = –0.60, 95% CI = [–0.66, –0.54], p  < 0.001, d  = –0.75. A post hoc sensitivity analysis determined that the sample was adequately powered to detect this interaction, 100% power with a 95% CI (99.63%, 100%).

We next decomposed the significant two-way Learning Style by Subject Type interaction using simple effects contrasts. As predicted, for core subjects, participants rated the visual learner as having higher grades than the hands-on learner, t  = 12.89, p  < 0.001, d  = 0.65. In contrast, for non-core subjects, participants rated the hands-on learner as having higher grades than the visual learner, t  = 13.94, p  < 0.001, d  = 0.81 (Fig. 4 ).

Grade ratings were on a 1–10 scale, representing letter grades from Below C-, C-, C, C+,… A+, respectively. Each bar indicates the mean grade rating with error bars showing s.e.m.s. Subject type is divided into core (math, science, language arts, social studies) and non-core (art, gym, music).

Pre-registered Hypothesis 2 A

Visual learners will be rated as having higher grades for math, social studies, and language arts, but not science. For science, we predict an opposite trend wherein hands-on are rated as having higher grades than visual learners.

We ran a mixed linear effects model which tested whether the main effects of Subject (math, social studies, language arts, science), Learning Style (visual, hands-on), and their interaction predicted grade ratings. There were significant main effects of Learning Style, b Learning Style  = –0.53, 95% CI = [–0.60, –0.46], p  < 0.001, d  = –0.79, and Subject, b Subject  = 0.97, 95% CI = [0.85, 1.09], p  < 0.001, d  = 0.83. The interaction between Learning Style and Subject also yielded significance, b Learning Style*Subject  = 0.62, 95% CI = [0.50, 0.74], p  < 0.001, d  = 0.53. A post hoc sensitivity analysis determined that the sample was adequately powered to detect this interaction, 100% power with a 95% CI (99.63%, 100%).

We next decompose the significant two-way Learning Style by Subject interaction using simple effects contrasts. As predicted, participants rated the visual learner to score higher than the hands-on learner in language arts (( M(SD) Visual  = 7.15(1.86), ( M(SD) Hands-on  = 5.67(1.69), t  = 10.23, p  < 0.001, d  = 1.02), math (( M(SD) Visual  = 7.36(1.79), ( M(SD) Hands-on  = 5.82(1.91), t  = 10.65, p  < 0.001, d  = 1.07), social studies (( M(SD) Visual  = 6.96(1.96), ( M(SD) Hands-on  = 5.55(1.72), t  = 9.72, p  < 0.001, d  = 0.97), but not science ( M(SD) Visual  = 7.62(1.55), ( M(SD) Hands-on  = 7.80(1.72), t  = 1.21, p  = 0.226, d  = 0.12, Fig. 5 ).

Grade ratings were on a 1–10 scale, representing letter grades from Below C-, C-, C, C+,… A+, respectively. Each bar indicates the mean grade rating with error bars showing s.e.m.

Pre-registered Hypothesis 2B

Hands-on learners will be rated higher on all non-core subjects than visual learners.

Similar to the analysis of Hypothesis 2 A, we first ran an omnibus analysis which examined whether the main effects of Subject (art, gym, music) and Learning Style (visual, hands-on) and their interaction predicted grade ratings. There were significant main effects of Learning Style, b Learning Style  = 0.66, 95% CI = [0.59, 0.74], p  < 0.001, d  = 1.06, and Subject, b Subject  = 0.73, 95% CI = [0.62, 0.84], p  < 0.001, d  = 0.83. The interaction between Learning Style and Subject was also significant, b Learning Style*Subject  = –0.43, 95% CI = [–0.54, –0.32], p  < 0.001, d  = –0.48. A post hoc sensitivity analysis determined that the sample was adequately powered to detect this interaction, 100% power with a 95% CI (99.63%, 100%).

We next decomposed the significant two-way Learning Style by Subject interaction using simple effects contrast tests. As predicted, participants rated the hands-on learner to score higher than the visual learner in Art (( M(SD) Visual  = 8.61(1.55), ( M(SD) Hands-on  = 9.09(1.10), t  = 3.47, p  < 0.001, d  = 0.35), Gym (( M(SD) Visual  = 7.07(2.04), ( M(SD) Hands-on  = 9.01(1.23), t  = 14.16, p  < 0.001, d  = 1.42), and Music (( M(SD) Visual  = 6.69(1.87), ( M(SD) Hands-on  = 8.25(1.56), t  = 11.42, p  < 0.001, d  = 1.14, Fig. 6 ).

Pre-registered Hypothesis 3

Hypothesis/goal.

Replication of Experiment 2 findings that visual learners are perceived to be smarter than hands-on learners, and that teachers show a larger effect.

Figure 7 displayed the percentage of participants’ answers of student type (visual/hands-on learner) by question type (smarter and harder) and sample group (teacher and parent). We fit a mixed effects binary logistic regression to examine whether the question (smarter, works harder) and group (teacher, parent) predicted participants’ answers (the visual or hands-on learner). The main effect of the question type was significant, b  = –0.83, 95% CI = [–1.04, –0.612], z  = –7.59, p  < 0.001, odds ratio = 0.44. Participants were more likely to pick the visual learner as smarter than the hands-on learner; in contrast (Fig. 7a ), they were more likely to pick the hands-on learner as working harder than the visual learner (Fig. 7b ). There was no main effect of the sample group (parent or teacher), b  = –0.071, 95% CI = [–0.28, 0.14], z =–0.65, p =0.515, odds ratio = 0.93. The interaction between the question and group was not significant, b  = –0.001, 95% CI = [–0.21, 0.21], z  = –0.01, p  = 0.990, odds ratio = 1.00. We did not replicate the differences between parents and teachers from Experiment 2 and so it is not discussed further.

a percentage to the question “Which student is smarter”. b percentage to the question “Which student is sportier”. Each bar indicates the percentage that the sample selected the visual or hands-on learner as “smarter” or “sportier”, adding up to 100%.

Parents, teachers, and children judged children described as visual learners as more intelligent than children described as hands-on learners. Compatible with these judgments, teachers and parents also predicted that children described as visual learners would receive higher grades than those described as hands-on learners in the majority of “core” school subjects, including math, social sciences, and language arts. In contrast, children described as hands-on learners were viewed as being more skilled at non-core subjects including, gym, music, and with smaller effects, art. The exception to this pattern was science wherein children described as hands-on learners were viewed as equally skilled at (elementary school) science. Together, these findings reveal a new consequence of the learning style myth: Perceived learning styles influence people’s thinking about young students’ academic potential and abilities. Describing students as being hands-on or visual learners influenced parents’ and teachers’ thinking about those young students’ intelligence and academic achievement (i.e., report card grades).

The present study is also the first to investigate young children’s (6–12 years) beliefs about the learning style myth (Experiment 1). We provide evidence that learning style information likely influences children’s thinking. Namely, describing peers as hands-on or visual learners influenced some children’s judgments of their peers’ intelligence and athleticism. Together, these findings are the first to suggest that any neuromyth influences elementary school children’s thinking. Moreover, these findings add to the growing body of work that characterizes the multifaceted nature of children’s theories of learners 46 , by showing that learning style categories also influence their thinking. Future work should examine if and how children’s endorsements are linked to their educational choices (e.g., specialized STEM or Arts programs).

The findings more broadly build on prior work suggesting that educators and parents likely essentialize learning styles by examining the specific ways in which information about learning style traits leads to inferences about its category members 6 , 12 . These findings are the first to suggest that perceived learning style traits may lead parents and teachers to make a host of specific (unwarranted) inferences about children’s academic strengths and weaknesses. This behavior is concerning considering that learning styles are unscientifically founded seemingly arbitrary categories. It highlights that we should be wary of describing children as hands-on or visual learners as, despite any good intentions, these categories, similar to other social categories, are likely to trigger incorrect thinking about children’s abilities by educators, parents, and their peers. These findings further suggest that perceived learning styles, and the messages conveyed along with them, likely need to be accounted for in recent drives to improve the scientific foundation of early years curriculum as sensitivity to learning styles emerges early and appears to be widespread—at least in the American populations we surveyed 47 .

There are potential alternative explanations for our findings. First, people’s judgments could reflect the use of “hands-on” as a euphemism for less competent or smart. However, against this possibility are the fairly high ratings hands-on learners received in science, and the moderate ratings they received otherwise.

Second, participants may have been reflecting on real evidence-based individual differences in thinking capacities, as opposed to the VAK myth (e.g., visual/verbal capacities 48 ). But, how exactly this account would explain our findings is unclear. For example, children described as visual learners were judged to be superior to children described as hands-on learners in both math and language arts, but equally skilled in science. Children described as hands-on learners were rated as being more skilled at arts, which is highly visual in nature. Put differently, visual learners’ reported strengths span subject areas that appear to vary greatly in their “visual demands”. In this way, it seems unlikely (real) individual differences in visualization capacity could accurately map onto this pattern of judgments.

Third, individuals may not spontaneously consider learning styles in their thinking about student potential in everyday life. Indeed, the experiments employed a survey method that asked participants to make, often forced-choice, decisions. This kind of method has the potential to make the effects of beliefs larger than they may be in real life because they, by their nature, require participants to pick between outcomes. However, we think it is unlikely that our choice of method alone accounts for our findings as laypeople are often encouraged to associate learning styles with educational outcomes in real-world settings. For example, many educational organizations, web sources, etc. ‘tout’ learning styles and claim that individuals with certain learning styles ‘is good at’ certain domains (e.g., kinesthetic learner—sports and arts 44 , 45 ). Moreover, in Experiment 2, on open-ended questions, people spontaneously listed different school subjects for each learning style when it was not required.

Finally, another limitation is that our parent and teacher samples are not mutually exclusive. For example, some participants in the teacher samples may also be parents. Any potential overlap may have affected our ability to detect differences between the two groups. Regardless of the potential overlap, our findings are still informative in that they show that being a teacher ‘in and of itself’ does not protect someone from thinking that learning styles are informative predictors of learning outcomes.

A final related question is how providing descriptors or information about learning styles as opposed to only labels might have influenced the present findings. The present study is the first of its kind and so we took the most judicious approach in assigning learning styles to students. Based on prior work showing that labels promote essentialist thinking 49 , we suspect that providing labels would have only enhanced the present findings. Nonetheless, how labeling, or adjusting the descriptions, enhances or influences the present effects is an open question for future work.

Future work should investigate the consequences of the beliefs we uncovered. For example, it could be that simply labeling children as visual or hands-on learners influences others’ willingness to recommend or admit them to specialty programs, charter schools, and/or post-secondary programs. Similarly, perceived learning styles might influence children’s thinking about their own academic choices. Recent work suggests that perceived learning styles may be linked to both older children’s (i.e., approx. 10-14-years-old) and adults’ thinking about their identities (e.g., “ I’m a visual person… I would be able to pick up on math (if I was) visually solving the problem ” 50 ; also in personal anecdotes 6 ). In this way, it seems likely that perceived learning styles and their link to students’ identities have many overlooked effects on young students’ lives. In a related vein, future work should examine how learning style identities intersect with other identities. Learners’ identities are intersectional and include, for example, gender and racial identities among many others. Understanding intersections between these and learning style identities may be vital to building a complete picture of how young children are categorized and stereotyped in the classroom (e.g., work on brilliance 36 ). Moreover, future work might also explore how the different qualities of people’s own definitions of learning styles, like the degree to which they view kinesthetic as “hands-on”, relates to their beliefs about academic aptitude.

In sum, we show that providing information about learning styles leads to (unwarranted) inferences about children’s intelligence and aptitude. They suggest more work needs to be done to characterize the impact of perceived learning styles in the classroom on children’s and teachers’ thinking and behavior.

Participants

Seventy-three 6- to 12-year children were included in the final sample (48.1% female, M(SD) age = 9.06(1.88) years, age range 5.67–12.83 years, 67.5% monoracial white, 49.3% female). Children came from mid to high SES families: 71.4% had an annual family income above $60,000 and 88.3% had a college-educated mother. Three additional children were tested, but excluded: 2 for not answering at least one question, and 1 for an experimenter’s error. Children were selected through a lab database of children recruited in Greensboro as well as through social media ads targeting mainly the Greensboro region. Two children were from Canada while the rest were located in the United States. Children were remunerated with an electronic activity booklet and certificate. The study was approved by the University of North Carolina Greensboro Institutional Review Board (IRB #20-0365). Parents provided written informed consent to take part in the study.

Age was treated continuously in our design; however, while recruiting we aimed to evenly recruit across the age range. The final sample included: 24 children under 8 years, 25 children between 8- and 10 years, and 24 children older than 10 years. We had hoped to collect data from 96 children but stopped testing at 73 children due to extreme difficulties with recruiting children during the pandemic and related experimenter turnover at the start of the new semester. For further information about power, including a sensitivity analysis, please see the results section.

Ninety-four parents were also recruited from Prolific and given $1.00 for 5 min of their time. Four additional parent participants were tested but excluded for not passing an attention check (question information provided below). All adult participants were US citizens and residents. They were 51.1% female, M(SD) age = 42.25(13.21), age range 20-79. The parent identity was screened using the screener question built into Prolific, “Do you have any children?” (answer “Yes”).

The data from the parent sample were collected after we collected data from the child sample. The sample size of 100 was decided upon using the significant and adequately powered effect from the child sample. Namely, using that sample as a guide, a rule of not having less than 73 in any sample after exclusions was adopted and used throughout the three studies. Further, a general target sample size of 100 per study was adopted as it is also typical in the neuromyth literature (e.g., Dekker et al., 2012; Newton & Miah, 2017). The study (along with Studies 2 and 3) was approved as exempt from further oversight by the University of North Carolina Greensboro Institutional Review Board (IRB #20-0213). All participants provided informed consent before entering the survey.

Materials and Procedure

Children saw a narrated story created in PowerPoint, which was presented over a video call using either Zoom or WebEx. The story began with an introductory text explaining that the experiment was: “…going to tell you about some kids and ask you to make some guesses about them”. Children were then presented with a silhouette of a teacher leading a class and were told they were going to hear about some students and how they learn best. Children then participated in two test trials: one per learning style: hands-on and visual. In one test trial, children were told about a student who learns best using their eyes (the visual learning style), and in the other, they were told about a student who learns best by using their hands (the hands-on learning style). For example, “This student learns best by using their hands. This student learns best when they can touch and feel things with their hands”. The descriptor ‘hands-on’ was used instead of the descriptor ‘kinesthetic’ or the related descriptor ‘kinesthetic/tactile’ because we suspected the hands-on terminology would be more familiar to parents and children. Figure 8 shows how the learning style information was displayed to participants.

Left panel: introduction to the hands-on learner. Right panel: introduction to the visual learner.

After each introduction, participants were asked the test questions modeled after previously published research 51 . The first questions asked about smartness, and were as follows: “Is this student smart or not very smart?” If a child answered “smart” they were then asked “Is the student sort of smart, smart, or really smart?”. Three circles increasing in size were shown under the sort of smart, smart, and really smart text. The second set of questions asked about sportiness and had the same structure (i.e., asked about whether the student was sporty and then how sporty).

The slideshow used nondescript silhouettes of people to avoid any confounding variables that may result from participants making inferences about gender or other aspects of the students’ social identity (See Fig. 8 ). There were four different versions of the slideshow which crossed the order of the learning styles and sporty/smart questions.

Parents were presented with the same stimuli as the children using Qualtrics. Parents read the stories instead of having them narrated to them. Test questions were identical. Because Qualtrics was used for survey administration versus slideshows created in PowerPoint, parents saw an entirely randomized version of the survey (i.e., the learning style information order and the question order sporty/smart were randomized). At the end of the parents’ survey, there was an attention check item asking parents to select which question was not asked in the earlier story scenario (the answer was “hands-on learners and scientific reasoning”).

All participants were paid $1.00 for 5 min of their time and were US citizens and residents. One hundred slots for both parents and teachers were opened on Prolific. The final samples after exclusions were 79 parent participants (46.8% female, 67.1% white) and 94 teacher participants (67.0% female, 75.5% white, M(SD) age = 34.94 (10.76), age range 20–78). Teachers were considered those who responded “yes” to the Prolific screening question: “You indicated that you worked in the education industry. Does your job involve teaching?”. Additional demographic information collected as part of Study 3 suggested that the majority of those who answer “yes” are teachers working in a public/private school, or university/college setting (see Study 3 for further details). Parent identity was again screened by the question, “Do you have any children?” (answer “Yes”).

Six teachers and twenty-one parents were excluded for either not answering the attention check questions in a meaningful way or not fully completing the survey. For example, in one case when asked to describe each learner some participants copied their participant ID; wrote “yes” as a description; or described the visual learner inappropriately as “deaf”. We outline the attention checks further below. Of note, exclusions were likely higher in the parent sample of this study due to an influx of new Prolific users in the later half of 2021 52 .

Materials and procedure

Participants filled out a survey on Qualtrics. The introductions and learning style information were presented the same as in Experiment 1. This study also included an additional attention check in the learning style introduction: After the presentation of each learner type (visual/hands-on learner), participants were asked to “Briefly describe this learner”. At test, we asked two forced-choice questions in a randomized order: (a) Which student is smarter? and (b) Which student is sportier? When answering, participants had the option to select either the hands-on or the visual learner.

An additional question at the end asked, “Do you believe that ‘individuals learn best when they receive information in their preferred learning style (e.g., visual, auditory, kinesthetic)?” to test for belief in learning styles. This question text was taken from previous research 2 . All parents and 85.1% of teachers believed in learning styles.

At the end of the survey, this study also included two open-ended exploratory questions, presented in a random order, which asked participants to list three school subjects that students with each of the two learning styles would excel at. This was done to understand what sorts of strengths teachers and parents might think those of each learning style might have. Of note, this question was not comparative in nature (e.g., “Who is better at X?”) and so it will not tell us anything about which subjects participants think a visual learner is better at than a hands-on learner or vice versa. Participants were excluded from the main task and this one if they did not follow instructions (i.e., they wrote something nonsensical or nothing) as such responses indicated a lack of engagement with the survey.

Participants were recruited from Prolific and were paid $1.00 to complete a 5-min survey. There were 100 slots opened for each sample (i.e., parent and teacher) and participants had to be US citizens and residents to be eligible. The teacher’s identity was again screened by a Prolific screening question: “You indicated that you worked in the education industry. Does your job involve teaching?” (answer “Yes”). The parents’ identity was again screened by a similar question, “Do you have any children?” (answer “Yes”). The final sample included 100 parent participants (73% female, 81% white, M(SD) age = 45.65 (12.23), age range 23–79) and 100 teacher participants (58% female, 66% white, M(SD) age = 38.76 (12.42), age range 19–75). No participants were excluded in this study as all participants passed the two attention check items which asked participants to write a sentence to describe each learner.

To gather more information about the teacher sample, teachers answered two additional questions regarding their job and work environment: “What setting do you teach in?” and “What age-group do you mainly teach?”. For the teaching setting: 57% taught in a “school (public/private)” setting, 30% in “university/college”, and the rest 13% taught in the home (e.g., home tutor) or in other settings. For the age-group: 35% taught “Adults”, 30% “High school”, 21% “Elementary school”, 11% “Middle school”, and 3% “Preschool”.

Participants filled out a Qualtric survey. The introductions and learning style information were presented the same as in Experiment 1 and 2. The attention check items were the same as in Experiment 2. After being introduced to the two learners in Grade 5, participants were asked to estimate grades for each learner on seven school subjects in a randomized order: math, science, language arts, social studies, art, gym, and music. Specifically, in the introductory slides they were told: “Recently, these students got their report cards. We want you to guess the grades they received on several subjects using the following 10-point scale”. Next, at test, after each learner was described, they were asked: “On the scale below make your best guess of the grades they received for each of the following subjects”. The specific 10-point letter grade scale was as follows: A + , A, A-, B + , B, B-, C + , C, C-, Below C-. These answers were converted into a numeric score for analysis: Below C- = 1, C- = 2, C = 3, C + = 4, B- = 5, B = 6, B + = 7, A- = 8, A = 9, A + = 10. The scale ranged from A+ to C- because we wanted to ensure that it was sensitive enough to detect differences among our groups. Grades below C- are rare and so we suspected that providing too many of these options (i.e., D + , D, D-, F), would lead participants to only use a narrow portion of the scale thereby reducing its sensitivity.

In efforts to replicate Experiment 2, two forced-choice questions were presented at the end of the survey in a randomized order: a) Which student is smarter? and b) Which student works harder? The smarter question was identical to Experiment 2 questions. The comparison question was changed from “sportier” to “works harder” because we were already asking about gym aptitude in the main task. For “works harder”, we predicted a similar pattern as the sportiness question wherein hands-on learners should be perceived as harder workers than visual learners.

The survey concluded with an open-ended question where we asked participants to write at least two sentences to describe what they were thinking about while they were answering the survey. This question provided a way to screen out non-attentive participants and/or bots. Again, all participants successfully passed this attention check (i.e., wrote two relevant sentences). All analyses were pre-registered.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data can be found at https://osf.io/exa9p/?view_only=a7e0927f83a0407a9c7bbce6fece1464 .

Code availability

Codes can be found at https://osf.io/exa9p/?view_only=a7e0927f83a0407a9c7bbce6fece1464 .

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Learning Styles Guide for Every Teacher in 2023

Different students have a different way of understanding and retaining information. Teachers must therefore employ different teaching methods to accommodate all students.

Teachers must understand the different learning styles and determine the style that works best in their classroom.

There are four main learning styles- visual, auditory, kinesthetic and reading/writing — but experts count up to 70. They all aim to help students learn more efficiently and perform better in school.

Today, we’re going to do a comprehensive analysis of the different styles of learning.  We’ll demonstrate how they impact learning and give examples of how you can implement them in a classroom.

What Is a Learning Style? 

A learning style is the strategy each student uses to retain information more efficiently while  studying.

According to Western Governors University ,  learning styles can be traced back to 334 BC when Aristotle declared that “ every child possessed specific talents and skills .”

In a classroom setting, teachers incorporate different teaching methods to cater for different students’ learning styles. This ensures all students have an equal opportunity to learn and succeed academically.

In recent decades, learning styles have continued to gain popularity among educators. Several factors have contributed to this:

• The rise of individualized learning where educators are tailoring instruction to meet the individual learning needs of every student.
• Availability of technology, such as videos and other online resources that have made it easier to incorporate different learning styles in the classroom.
• Increased awareness that is enabling teachers to acknowledge the differences among their students
• Acceptance of culturally responsive learning that allows inclusivity and diversity in the curriculum.

What Is a VARK Model?

The VARK model is a framework for categorizing different learning styles. VARK is an acronym for visual, auditory, reading/writing, and kinesthetic learning styles . 

According to the model, most individuals have a dominant learning method that helps them grasp and retain information better. For example a visual learner may benefit from using pictures, diagrams, and other visuals to supplement normal classwork. 

On the other hand, an auditory learner will benefit from podcasts, audiobooks, debates, and class discussions to reinforce their understanding of concepts and learning material.

Both students will have  improved educational outcomes through different instructional methods. 

Educators should strive to understand their students’ preferred learning methods to create lessons that are more engaging, effective, and tailored to the individual needs of each student.

Benefits of Learning Styles for Teachers

Students learn differently based on their environment, cognitive abilities, and emotional states. When teachers understand this, they can employ various teaching methods in the classroom to help students engage and succeed academically.

Learning about the different learning methods is crucial for teachers because it empowers them to use the skill in the classroom with their students.

If you feel you’re not skilled enough to teach in a particular style, you should develop the skills needed. You should be able to teach in more than one style to meet the learning needs of your students.

Understanding your learning style means you know what works best for you. You can then adapt your education experience accordingly to tailor instruction for your learners.

Benefits of Learning Styles for Students

Adapting different styles to teach is important for your students' academic success. It leaves you self-assured, leading to more productive learning and positive relationships in the classroom and the future.

Acknowledging various learning methods ensures students learn what best works for them, creating an inclusive and positive learning environment.

Learning styles offer a range of benefits that go beyond the classroom. For example:

• Career success
• Improved memory and retention
• Better communication skills

By using teaching styles that align with student’s learning techniques, you can enhance your students' academic performance and improve other areas of their personal development.

Four Main Learning Styles

Students will often require a combination of different learning styles. However, understanding the four main ones will give you a better understanding of what might work and what might not.

The four learning styles include:

• Kinesthetic
• Reading/writing

We’ll explore them deeply next to give you a clear picture of what each entails.

1. Visual Learning Style

Visual learning style is where students learn best through seeing and observing things. Visual learners have a strong visual memory and often use mental images to help them remember when learning different topics.

In a classroom setting, teachers can employ visual learning in different ways. For instance, a history teacher may use videos to teach historical methods.

In another example, a science teacher can use a diagram to help visual learners understand the process of photosynthesis. They can illustrate how plants convert sunlight into chemical energy.

By using visual aids, they help visual learners grasp the concept more effectively than with just a verbal explanation.

Examples of tools that can help you engage your visual learners include:

• Whiteboard sketches
• Visual organizers such as maps and timelines.

2. Auditory Learning Style

Auditory style is where students learn best through listening and hearing things. Auditory learners have a strong verbal memory and tend to recall information they’ve heard.

Auditory learners enjoy participating in debates and discussions because they can process information through the exchange of ideas and arguments.

Unlike visual learners who thrive with purely visual instructions, auditory learners struggle remembering information that’s presented without a verbal explanation.

For example, suppose a teacher is teaching a history class. In that case, auditory learners may struggle with a textbook or other visual aids but find it easy to remember when the teacher explains the events verbally in class.

There are various tools that teachers can use to implement auditory learning style in the classroom, including:

• Class discussions
• Audiobooks and podcasts
• Oral presentations
• Verbal instructions

These auditory learning strategies can help engage your students, leading to improved academic and overall success.

3. Kinesthetic Learning Style

Kinesthetic learning style is a learning method where students learn best through hands-on experience or physical activity.

Kinesthetic learners are also referred to as tactile learners. They rely on movement, touch, and manipulation of objects to remember information.

Kinesthetic learners tend to enjoy sports, dance, and other physical activities. They may find sitting still for long challenging but benefit from activities that allow them to move and interact with the learning material, such as experiments and hands-on projects.

As a teacher, you can help your kinesthetic learners understand and retain information by providing real life experiences. For instance, you can take your history class to the museum to see artifacts from a period you’re teaching them about.

Other tools you may incorporate for your learners include:

• Physical objects such as blocks, puzzles, and counters
• Whiteboard or chalkboard for students to sketch ideas and concepts
• Physical games such as scavenger hunts and relay races
• Virtual reality to simulate physical activities 

4. Reading/Writing Learning Style

Reading/writing learning style is a learning method where students find it easier to learn through text based material. For example, books, articles, and written notes.

Students who prefer this specific learning style prefer reading and writing as a way to engage with new information. They have a strong ability to process written information and retain details from text.

Such students tend to take notes while reading or listening to lectures. They may prefer quiet study environments that allow for focused reading and writing.

As a teacher you can encourage your students with reading writing learning styles to take notes while you teach in class. You can also give them writing assignments such as essays or research papers to help them practice and improve their writing skills.

You can use several tools to help students with a reading/writing learning preference retain information. For example:

• Providing written instructions
• Group discussions where they write reports on ideas

Other Learning Styles

Besides the four learning styles we’ve covered, there are many others that teachers can consider implementing in their classrooms. These methods help educators cater to various student learning abilities. They include:

• Social/linguistic
• Logical/analytical

Let’s explore them in detail.

Social/Linguistic Learning Style

Social/linguistic learning style focuses on the social and communicative aspects of learning. Individuals with this style of learning perform best when they engage in activities that involve social interaction and communication. 

When teaching students, an educator can encourage learners to participate in group discussions, debates and role playing to help social/linguistic learners process and understand ideas.

Logical/Analytical Learning Style

Logical/analytical learning style is a learning preference where students use logic, analysis and reasoning to learn new ideas.

Logical/analytical learners break down complex concepts into manageable parts and then analyze them to arrive at a solution. They enjoy categorizing information and working with numbers and patterns.

Teachers can use charts, graphs and diagrams to help students with this style see patterns and visualize connections that may not be very obvious.

Educators can also use real-life examples to show the students how certain concepts apply to the real world.

Solitary Learning Style

Solitary learning style is a learning method where students prefer to work alone and independently to learn new ideas.

Solitary learners prefer to reflect on their own thoughts and experiences and retain information through self study. Such learners may enjoy setting their own goals and timelines.

As a teacher, you can help solitary learners by giving them assignments to research certain topics by themselves through books and online resources. Once they’ve completed the assignment, you can encourage them to present or explain the concepts to the class.

Providing them with such opportunities of self-study can help improve their learning outcomes and help them enjoy the learning process.

Nature Learning Style

The nature learning style is a learning method where students learn by connecting to the natural world.

Students with this style of learning deeply appreciate the environment and are drawn to activities that allow them to learn about and explore the natural world.

Educators can cater to nature learners by incorporating learning tasks that allow them to connect with nature, For example, taking students to local parks, field trips, and nature reserves.

They can also incorporate class activities such as nature walks, gardening and experiments into the curriculum.

Models and Theories that Influenced Learning Styles

Learning styles didn't always look the same throughout history. Certain theories affected them as we know them today. 

Teacher asking her students a question at the elementary school

1. David Kolb and Experiential Learning 

David Kolb is an American education theorist and psychologist best known for his theory of experiential learning. 

According to Kolb, learning is a process that occurs through experiences, and the knowledge gained from those experiences is used to guide future actions and experiences.

Kolb's theory of experiential learning consists of four learning methods, namely:

• Accommodating
• Assimilating

Kolb’s theory has been influential in educational psychology and management. It emphasizes the importance of active participation in the learning process.

2. Honey and Mumford's Learning Styles

Honey and Mumford’s theory is based on the idea that each individual has a preferred learning style, and identifying these preferences can lead to more effective learning.

Honey and Mumford identified four learning styles, namely:

• Activist: Prefers to learn through active experimentation and experience
• Pragmatist: Prefers to learn through practical application and problem-solving
• Reflector: Learn through observation and reflection
• Theorist: Learn through abstract conceptualization and models

3. Anthony Gregorc's Mind Styles

Anthony Gregorc is an American psychologist who developed a theory of mind styles that identify how individuals perceive, process, and organize information.

According to Gregorc, we have four learning styles which are described in pairs of opposing concepts, namely:

• Concrete vs. abstract thinking: Individuals with a concrete mind style prefer to learn with hands-on experience. In contrast, individuals with abstract thinking mind styles prefer to focus on ideas and concepts.
• Sequential vs. random thinking: Sequential learners prefer to learn ideas in a step-by-step manner, while random learners prefer to learn information spontaneously.

Anthony Gregorc’s mind styles theory emphasizes that individuals have different strengths and preferences. Educators who understand this can employ individualized learning styles to promote effective learning and communication.

4. Visual, Auditory and Kinesthetic Learners (VAK)

The VAK learning style model identifies the three primary sensory modalities that individuals use to receive and process information. I.e., visual, auditory, and kinesthetic

As mentioned earlier, the VARK model recognizes reading/writing as a learning style. VAK is a simpler method that does not include reading/writing learning methods.

The VAK model is widely used in educational training. It’s a useful tool for identifying general preferences and tendencies.

5. The Learning Styles Task Force

The National Association of Secondary School Principals (NASSP) formed a learning styles taskforce .

The NASSP task force model consists of three learning methods:

• Physiological

Accordingito the model these three factors determine an individual’s approach to learning. They have been used over the years to improve educational practices.. 

6. The Index of Learning Styles™

The index of learning styles is a learning model developed by Dr Richard Felderand Barbara Soloman of North Carolina State University.

It recognizes four styles of learning:

• Sensory/Intuitive: This style refers to how learners prefer to focus on concrete, factual information (sensing) or abstract concepts and theories (intuitive).
• Visual/Verbal: Refers to how learners prefer to learn through visual aids and verbal explanations such as  lectures and discussions.
• Active/Reflective: This refers to the extent to which learners prefer to be actively involved in the learning process or to reflect on the material before responding.
• Sequential/Global: This is the extent to which learners prefer to learn information in a step-by-step manner versus  seeing the big picture first and then fill in the details.

The ILS is widely used in educational training to help educators and students adapt to individual learning styles. 

Asking Your Students About Their Learning Styles

Besides learning and understanding different learning styles to incorporate them in the classroom, you can also discuss it with your students. 

For example, you can give your students a learning styles questionnaire to understand their personal preferences.

Here are some questions you can add to the questionnaire:

• Do you prefer reading a book with a lot of a) pictures, b) words, or c)with word searches/crossword puzzles?
• When unsure how to spell a certain word, are you more likely to a) write it down, b) spell it out, or c) trace the letters in the air?
• When waiting in lines, are you most likely to a) look around yourself, b) talk to the person next to you, or c) move back and forth?
• When you see the word “cat,” do you first a) picture a cat in your mind, b) say the word cat to yourself, or c) think about being with a cat?
• When you study for a test, do you a) read the book and your notes, b) have someone asking you questions, and you answer out loud, c) create index cards?

You can use the answers to these questions to understand each student and identify the common learning style in your classroom.

Steps to Determining the Right Learning Style

Analyzing how your students learn can help you in crafting your teaching strategies. It can also help you become more organized, use prior knowledge as a foundation for new learning, and choose effective methods for different learning tasks.

1. Understand the bigger picture.

To understand the big picture, educators must ask the why and the how. This way, they can figure out why a particular learning style approach is better and how to incorporate it in their teaching.

Learning the preferred way refers to the idea that individuals have different learning styles, preferences, and strengths and that teaching and learning should consider these differences.

Learning the efficient way, on the other hand, refers to the idea that there are certain teaching and learning methods that are more effective and efficient for all learners, regardless of their individual learning styles or preferences. 

For example, research has shown that techniques such as active learning, spaced repetition, and retrieval practice can be effective for all learners, regardless of their preferred learning style.

While it is important to recognize and accommodate individual learning styles, it is also important to consider the most efficient and effective teaching and learning methods. 

2. Identify your strengths.

Every teacher is naturally skilled in every learning style. You should identify your strengths and how you naturally approach teaching.

Having this understanding will help you work toward developing different teaching styles in order to meet the needs of your earners.

3. Learn how to communicate chosen learning styles.

Once you’ve learnt your strengths and chosen the learning styles to employ, you should also explain them to your students.

Communicating your chosen learning styles will help your students prepare for the tasks and activities they’ll be doing. It’ll also help students take ownership of their learning and identify the methods that best work for them.

You can introduce learning styles as a topic and encourage students to engage in trying different styles. You can also give examples and demonstrate different learning methods to help them understand.

Learning Styles Go Beyond Classrooms

Learning styles are not only efficient in the classroom, but they also help prepare students for their future.

How a child learns can tremendously affect their ability to connect with the topics you’re taching and how they engage with the rest of the class.

How young children approach learning will also have a strong impact on their future careers and how they deal with everyday situations later in life.

Understanding the style that works best for you as a teacher also affects your classroom management and how you relate to your students.

SimpleK12 can help you understand the different learning modalities and help you in matching instruction with your students’ capabilities.

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Learning styles refer to a range of competing and debunked theories that aim to account for differences in individuals' learning. Many theories share the proposition that humans can be classified according to their 'style' of learning, but differ in how the proposed styles should be defined, categorized and assessed.:8 A common concept is that individuals differ in how they learn.:266 The idea of individualized learning styles became popular in the 1970s, and has greatly influenced education despite the criticism that the idea has received from some researchers.:107–108 Proponents recommend that teachers have to run a needs analysis to assess the learning styles of their students and adapt their classroom methods to best fit each student's learning style. Although there is ample evidence that individuals express personal preferences for how they prefer to receive information,:108 few studies have found any validity in using learning styles in education.:267 Critics say there is no consistent evidence that identifying an individual student's learning style and teaching for specific learning styles produces better student outcomes.:33 Since 2012, Learning Styles have often been referred to as a "neuromyth" in education. There is evidence of empirical and pedagogical problems related to forcing learning tasks to "correspond to differences in a one-to-one fashion". Studies contradict the widespread "meshing hypothesis" that a student will learn best if taught in a method deemed appropriate for the student's learning style. However, a 2020 systematic review suggested that a majority (89%) of educators around the world continue to believe that the meshing hypothesis is correct. Studies further show that teachers cannot assess the learning style of their students accurately.

1. Overview of Models

There are many different learning styles models; one literature review identified 71 different models. [ 1 ] :166–168 Only a few models are described below.

1.1. David Kolb's Model

David A. Kolb's model is based on his experiential learning model, as explained in his book Experiential Learning . [ 2 ] Kolb's model outlines two related approaches toward grasping experience: Concrete Experience and Abstract Conceptualization , as well as two related approaches toward transforming experience: Reflective Observation and Active Experimentation . [ 2 ] :145 According to Kolb's model, the ideal learning process engages all four of these modes in response to situational demands; they form a learning cycle from experience to observation to conceptualization to experimentation and back to experience. In order for learning to be effective, Kolb postulated, all four of these approaches must be incorporated. As individuals attempt to use all four approaches, they may tend to develop strengths in one experience-grasping approach and one experience-transforming approach, leading them to prefer one of the following four learning styles: [ 2 ] :127 [ 3 ]

• Accommodator = Concrete Experience + Active Experiment : strong in "hands-on" practical doing (e.g., physical therapists)
• Converger = Abstract Conceptualization + Active Experiment : strong in practical "hands-on" application of theories (e.g., engineers)
• Diverger = Concrete Experience + Reflective Observation : strong in imaginative ability and discussion (e.g., social workers)
• Assimilator = Abstract Conceptualization + Reflective Observation : strong in inductive reasoning and creation of theories (e.g., philosophers)

Kolb's model gave rise to the Learning Style Inventory, an assessment method used to determine an individual's learning style. According to this model, individuals may exhibit a preference for one of the four styles—Accommodating, Converging, Diverging and Assimilating—depending on their approach to learning in Kolb's experiential learning model. [ 2 ]

Although Kolb's model is widely used, a 2013 study pointed out that Kolb's Learning Style Inventory, among its other weaknesses, incorrectly dichotomizes individuals on the abstract/concrete and reflective/action dimensions of experiential learning (in much the same way as the Myers-Briggs Type Indicator does in a different context), and proposed instead that these dimensions be treated as continuous rather than dichotomous/binary variables. [ 4 ] :44

In an article that addressed Kolb's work through 2005, Mark K. Smith reviewed some critiques of Kolb's model, and identified six key issues regarding the model: [ 5 ]

• The model doesn't adequately address the process of reflection;
• The claims it makes about the four learning styles are extravagant;
• It doesn't sufficiently address the fact of different cultural conditions and experiences;
• The idea of stages/steps doesn't necessarily match reality;
• It has only weak empirical evidence;
• The relationship between learning processes and knowledge is more complex than Kolb draws it.

1.2. Peter Honey and Alan Mumford's Model

Peter Honey and Alan Mumford adapted Kolb's experiential learning model. First, they renamed the stages in the learning cycle to accord with managerial experiences: having an experience, reviewing the experience, concluding from the experience, and planning the next steps. [ 6 ] :121–122 Second, they aligned these stages to four learning styles named: [ 6 ] :122–124

These four learning styles are assumed to be acquired preferences that are adaptable, either at will or through changed circumstances, rather than being fixed personality characteristics. Honey and Mumford's Learning Styles Questionnaire (LSQ) [ 7 ] is a self-development tool and differs from Kolb's Learning Style Inventory by inviting managers to complete a checklist of work-related behaviours without directly asking managers how they learn. Having completed the self-assessment, managers are encouraged to focus on strengthening underutilised styles in order to become better equipped to learn from a wide range of everyday experiences.

A MORI survey commissioned by The Campaign for Learning in 1999 found the Honey and Mumford LSQ to be the most widely used system for assessing preferred learning styles in the local government sector in the UK.

1.3. Learning Modalities

Walter Burke Barbe and colleagues proposed three learning modalities (often identified by the acronym VAK): [ 8 ]

• Visualising modality
• Auditory modality
• Kinesthetic modality

Barbe and colleagues reported that learning modality strengths can occur independently or in combination (although the most frequent modality strengths, according to their research, are visual or mixed), they can change over time, and they become integrated with age. [ 9 ] They also pointed out that learning modality strengths are different from preferences ; a person's self-reported modality preference may not correspond to their empirically measured modality strength. [ 9 ] :378 This disconnect between strengths and preferences was confirmed by a subsequent study. [ 10 ] Nevertheless, some scholars have criticized the VAK model. [ 11 ] [ 12 ] Psychologist Scott Lilienfeld and colleagues have argued that much use of the VAK model is nothing more than pseudoscience or a psychological urban legend. [ 13 ]

1.4. Neil Fleming's VAK/VARK Model

Neil Fleming's VARK model and inventory [ 14 ] expanded upon earlier notions of sensory modalities such as the VAK model of Barbe and colleagues [ 8 ] and the representational systems (VAKOG) in neuro-linguistic programming. [ 15 ] The four sensory modalities in Fleming's model are: [ 16 ]

• Visual learning
• Auditory learning
• Physical learning
• Social learning

Fleming claimed that visual learners have a preference for seeing (visual aids that represent ideas using methods other than words, such as graphs, charts, diagrams, symbols, etc.). Subsequent neuroimaging research has suggested that visual learners convert words into images in the brain and vice versa, [ 17 ] but some psychologists have argued that this "is not an instance of learning styles, rather, it is an instance of ability appearing as a style". [ 18 ] :268 Likewise, Fleming claimed that auditory learners best learn through listening (lectures, discussions, tapes, etc.), and tactile/kinesthetic learners prefer to learn via experience—moving, touching, and doing (active exploration of the world, science projects, experiments, etc.). [ 16 ] Students can use the model and inventory to identify their preferred learning style and, it is claimed, improve their learning by focusing on the mode that benefits them the most. [ 16 ] Fleming's model also posits two types of multimodality. [ 16 ] This means that not everyone has one defined preferred modality of learning; some people may have a mixture that makes up their preferred learning style. [ 16 ]

1.5. Anthony Gregorc's Model

Anthony Gregorc and Kathleen Butler organized a model describing different learning styles rooted in the way individuals acquire and process information differently. [ 19 ] This model posits that an individual's perceptual abilities are the foundation of his or her specific learning strengths, or learning styles. [ 20 ]

In this model, there are two perceptual qualities: concrete and abstract , and two ordering abilities: random and sequential . [ 20 ] Concrete perceptions involve registering information through the five senses, while abstract perceptions involve the understanding of ideas, qualities, and concepts which cannot be seen. In regard to the two ordering abilities, sequential ordering involves the organization of information in a linear, logical way, and random ordering involves the organization of information in chunks and in no specific order. [ 20 ] The model posits that both of the perceptual qualities and both of the ordering abilities are present in each individual, but some qualities and ordering abilities are more dominant within certain individuals. [ 20 ]

There are four combinations of perceptual qualities and ordering abilities based on dominance: concrete sequential , abstract random , abstract sequential , and concrete random . The model posits that individuals with different combinations learn in different ways—they have different strengths, different things make sense to them, different things are difficult for them, and they ask different questions throughout the learning process. [ 20 ]

The validity of Gregorc's model has been questioned by Thomas Reio and Albert Wiswell following experimental trials. [ 21 ] Gregorc argues that his critics have "scientifically-limited views" and that they wrongly repudiate the "mystical elements" of "the spirit" that can only be discerned by a "subtle human instrument". [ 22 ]

1.6. Cognitive Approaches

Anthony Grasha and Sheryl Riechmann, in 1974, formulated the Grasha-Reichmann Learning Style Scale. [ 23 ] It was developed to analyze the attitudes of students and how they approach learning. The test was originally designed to provide teachers with insight on how to approach instructional plans for college students. [ 24 ] Grasha's background was in cognitive processes and coping techniques. Unlike some models of cognitive styles which are relatively nonjudgmental, Grasha and Riechmann distinguish between adaptive and maladaptive styles. The names of Grasha and Riechmann's learning styles are:

• participative
• competitive
• collaborative
• independent

Aiming to explain why aptitude tests, school grades, and classroom performance often fail to identify real ability, Robert Sternberg listed various cognitive dimensions in his book Thinking Styles . [ 25 ] Several other models are also often used when researching cognitive styles; some of these models are described in books that Sternberg co-edited, such as Perspectives on Thinking, Learning, and Cognitive Styles . [ 26 ] [ 27 ] [ 28 ]

1.7. NASSP Model

In the 1980s, the National Association of Secondary School Principals (NASSP) formed a task force to study learning styles. [ 29 ] The task force defined three broad categories of style—cognitive, affective, and physiological—and 31 variables, including the perceptual strengths and preferences from the VAK model of Barbe and colleagues, [ 9 ] but also many other variables such as need for structure, types of motivation, time of day preferences, and so on. [ 29 ] :141–143 They defined a learning style as "a gestalt —not an amalgam of related characteristics but greater than any of its parts. It is a composite of internal and external operations based in neurobiology, personality, and human development and reflected in learner behavior." [ 29 ] :141

• Cognitive styles are preferred ways of perception, organization and retention.
• Affective styles represent the motivational dimensions of the learning personality; each learner has a personal motivational approach.
• Physiological styles are bodily states or predispositions, including sex-related differences, health and nutrition, and reaction to physical surroundings, such as preferences for levels of light, sound, and temperature. [ 29 ] :141

According to the NASSP task force, styles are hypothetical constructs that help to explain the learning (and teaching) process. They posited that one can recognize the learning style of an individual student by observing his or her behavior. [ 29 ] :138 Learning has taken place only when one observes a relatively stable change in learner behavior resulting from what has been experienced.

2. Assessment Methods

A 2004 non-peer-reviewed literature review criticized most of the main instruments used to identify an individual's learning style. [ 1 ] In conducting the review, Frank Coffield and his colleagues selected 13 of the most influential models of the 71 models they identified, [ 1 ] :8–9 including most of the models described in this article. They examined the theoretical origins and terms of each model, and the instrument that purported to assess individuals against the learning styles defined by the model. They analyzed the claims made by the author(s), external studies of these claims, and independent empirical evidence of the relationship between the learning style identified by the instrument and students' actual learning. Coffield's team found that none of the most popular learning style theories had been adequately validated through independent research. This means that even if the underlying theories were sound, educators are frequently unable to correctly identify the theoretically correct learning style for any given student, so the theory would end up being misapplied in practice.

2.1. Learning Style Inventory

The Learning Style Inventory (LSI) is connected with David A. Kolb's model and is used to determine a student's learning style. [ 3 ] Previous versions of the LSI have been criticized for problems with validity, reliability, and other issues. [ 4 ] [ 30 ] [ 31 ] Version 4 of the Learning Style Inventory replaces the four learning styles of previous versions with nine new learning styles: initiating, experiencing, imagining, reflecting, analyzing, thinking, deciding, acting, and balancing. [ 32 ] The LSI is intended to help employees or students "understand how their learning style impacts upon problem solving, teamwork, handling conflict, communication and career choice; develop more learning flexibility; find out why teams work well—or badly—together; strengthen their overall learning." [ 32 ]

A completely different Learning Styles Inventory is associated with a binary division of learning styles, developed by Richard Felder and Linda Silverman. [ 33 ] In Felder and Silverman's model, learning styles are a balance between pairs of extremes such as: Active/Reflective, Sensing/Intuitive, Verbal/Visual, and Sequential/Global. Students receive four scores describing these balances. [ 34 ] Like the LSI mentioned above, this inventory provides overviews and synopses for teachers.

2.2. NASSP Learning Style Profile

The NASSP Learning Style Profile (LSP) is a second-generation instrument for the diagnosis of student cognitive styles, perceptual responses, and study and instructional preferences. [ 35 ] The LSP is a diagnostic tool intended as the basis for comprehensive style assessment with students in the sixth to twelfth grades. It was developed by the National Association of Secondary School Principals research department in conjunction with a national task force of learning style experts. The Profile was developed in four phases with initial work undertaken at the University of Vermont (cognitive elements), Ohio State University (affective elements), and St. John's University (physiological/environmental elements). Rigid validation and normative studies were conducted using factor analytic methods to ensure strong construct validity and subscale independence.

The LSP contains 23 scales representing four higher order factors: cognitive styles, perceptual responses, study preferences and instructional preferences (the affective and physiological elements). The LSP scales are: analytic skill, spatial skill, discrimination skill, categorizing skill, sequential processing skill, simultaneous processing skill, memory skill, perceptual response: visual, perceptual response: auditory, perceptual response: emotive, persistence orientation, verbal risk orientation, verbal-spatial preference, manipulative preference, study time preference: early morning, study time preference: late morning, study time preference: afternoon, study time preference: evening, grouping preference, posture preference, mobility preference, sound preference, lighting preference, temperature preference. [ 35 ]

2.3. Other Methods

Other methods (usually questionnaires) used to identify learning styles include Neil Fleming's VARK Questionnaire [ 14 ] and Jackson's Learning Styles Profiler. [ 1 ] :56–59 Many other tests have gathered popularity and various levels of credibility among students and teachers.

3. In the Classroom

For a teacher to use the learning styles model, the teacher has to be able to correctly match each student to a learning style. This is a generally unsuccessful exercise due to inappropriate tools. For an assessment tool to be useful, it needs to be a valid test, which is to say that it actually has to put all of the "style A" students in the "A" group, all of the "style B" students in the "B" group, and so forth. Research indicates that very few, if any, of the psychometric tests promoted in conjunction with the learning styles idea have the necessary validity to be useful in practice. Some models, such as Anthony Gregorc's Gregorc Style Delineator, are "theoretically and psychometrically flawed" and "not suitable for the assessment of individuals". [ 1 ] :20

Furthermore, knowing a student's learning style does not seem to have any practical value for the student. In 2019, the American Association of Anatomists published a study that investigated whether learning styles had any effect on the final outcomes of an anatomy course. The study found that even when being told they had a specific learning style, the students did not change their study habits, and those students that did use their theoretically dominant learning style had no greater success in the course; specific study strategies, unrelated to learning style, were positively correlated with final course grade. [ 36 ]

3.1. Dunn and Dunn

Various researchers have attempted to hypothesize ways in which learning style theory can be used in the classroom. Two such scholars are Rita Dunn and Kenneth Dunn, who build upon a learning modalities approach. [ 1 ] :20–35 [ 37 ]

Although learning styles will inevitably differ among students in the classroom, Dunn and Dunn say that teachers should try to make changes in their classroom that will be beneficial to every learning style. Some of these changes include room redesign, the development of small-group techniques, and the development of "contract activity packages". [ 37 ] Redesigning the classroom involves locating dividers that can be used to arrange the room creatively (such as having different learning stations and instructional areas), clearing the floor area, and incorporating students' thoughts and ideas into the design of the classroom. [ 37 ]

Dunn and Dunn's "contract activity packages" are educational plans that use: a clear statement of the learning need; multisensory resources (auditory, visual, tactile, kinesthetic); activities through which the newly mastered information can be used creatively; the sharing of creative projects within small groups; at least three small-group techniques; a pre-test, a self-test, and a post-test. [ 37 ]

Dunn and Dunn's learning styles model is widely used in schools in the United States, and 177 articles have been published in peer-reviewed journals referring to this model. [ 1 ] :20 However, the conclusion of a review by Coffield and colleagues was: "Despite a large and evolving research programme, forceful claims made for impact are questionable because of limitations in many of the supporting studies and the lack of independent research on the model." [ 1 ] :35

3.2. Sprenger's Differentiation

Another scholar who believes that learning styles should have an effect on the classroom is Marilee Sprenger in Differentiation through Learning Styles and Memory . [ 38 ] She bases her work on three premises:

• Teachers can be learners, and learners teachers. We are all both.
• Everyone can learn under the right circumstances.
• Learning is fun! Make it appealing. [ 38 ]

Sprenger details how to teach in visual, auditory, or tactile/kinesthetic ways. Methods for visual learners include ensuring that students can see words written, using pictures, and drawing timelines for events. [ 38 ] Methods for auditory learners include repeating words aloud, small-group discussion, debates, listening to books on tape, oral reports, and oral interpretation. [ 38 ] Methods for tactile/kinesthetic learners include hands-on activities (experiments, etc.), projects, frequent breaks to allow movement, visual aids, role play, and field trips. [ 38 ] By using a variety of teaching methods from each of these categories, teachers cater to different learning styles at once, and improve learning by challenging students to learn in different ways.

James W. Keefe and John M. Jenkins have incorporated learning style assessment as a basic component in their "personalized instruction" model of schooling. [ 39 ] Six basic elements constitute the culture and context of personalized instruction. The cultural components—teacher role, student learning characteristics, and collegial relationships—establish the foundation of personalization and ensure that the school prizes a caring and collaborative environment. The contextual factors—interactivity, flexible scheduling, and authentic assessment—establish the structure of personalization. [ 39 ]

According to Keefe and Jenkins, cognitive and learning style analysis have a special role in the process of personalizing instruction. The assessment of student learning style, more than any other element except the teacher role, establishes the foundation for a personalized approach to schooling: for student advisement and placement, for appropriate retraining of student cognitive skills, for adaptive instructional strategy, and for the authentic evaluation of learning. [ 39 ] Some learners respond best in instructional environments based on an analysis of their perceptual and environmental style preferences: most individualized and personalized teaching methods reflect this point of view. Other learners, however, need help to function successfully in any learning environment. If a youngster cannot cope under conventional instruction, enhancing his cognitive skills may make successful achievement possible. [ 39 ]

Many of the student learning problems that learning style diagnosis attempts to solve relate directly to elements of the human information processing system. Processes such as attention, perception and memory, and operations such as integration and retrieval of information are internal to the system. Any hope for improving student learning necessarily involves an understanding and application of information processing theory. Learning style assessment can provide a window to understanding and managing this process. [ 39 ]

At least one study evaluating teaching styles and learning styles, however, has found that congruent groups have no significant differences in achievement from incongruent groups. [ 40 ] Furthermore, learning style in this study varied by demography, specifically by age, suggesting a change in learning style as one gets older and acquires more experience. While significant age differences did occur, as well as no experimental manipulation of classroom assignment, the findings do call into question the aim of congruent teaching–learning styles in the classroom. [ 1 ] :122

Educational researchers Eileen Carnell and Caroline Lodge concluded that learning styles are not fixed and that they are dependent on circumstance, purpose and conditions. [ 41 ]

4. Criticism

Learning style theories have been criticized by many scholars and researchers. Some psychologists and neuroscientists have questioned the scientific basis for separating out students based on learning style. According to Susan Greenfield the practice is "nonsense" from a neuroscientific point of view: "Humans have evolved to build a picture of the world through our senses working in unison, exploiting the immense interconnectivity that exists in the brain." [ 42 ] Similarly, Christine Harrington argued that since all students are multisensory learners, educators should teach research-based general learning skills. [ 43 ]

Many educational psychologists have shown that there is little evidence for the efficacy of most learning style models, and furthermore, that the models often rest on dubious theoretical grounds. [ 44 ] [ 45 ] According to professor of education Steven Stahl, there has been an "utter failure to find that assessing children's learning styles and matching to instructional methods has any effect on their learning." [ 46 ] Professor of education Guy Claxton has questioned the extent that learning styles such as VARK are helpful, particularly as they can have a tendency to label children and therefore restrict learning. [ 47 ] Similarly, psychologist Kris Vasquez pointed out a number of problems with learning styles, including the lack of empirical evidence that learning styles are useful in producing student achievement, but also her more serious concern that the use of learning styles in the classroom could lead students to develop self-limiting implicit theories about themselves that could become self-fulfilling prophecies that are harmful, rather than beneficial, to the goal of serving student diversity. [ 48 ]

Some research has shown that long-term retention can better be achieved under conditions that seem more difficult, and that teaching students only in their preferred learning style is not effective. [ 49 ]

Psychologists Scott Lilienfeld, Barry Beyerstein, and colleagues listed as one of the "50 great myths of popular psychology" the idea that "students learn best when teaching styles are matched to their learning styles", and they summarized some relevant reasons not to believe this "myth". [ 13 ]

4.1. Other Critiques

Coffield and his colleagues and Mark Smith are not alone in their judgements. In 2005, Demos, a UK think tank, published a report on learning styles prepared by a group chaired by David Hargreaves that included Usha Goswami from the University of Cambridge and David Wood from the University of Nottingham. The Demos report said that the evidence for learning styles was "highly variable", and that practitioners were "not by any means always frank about the evidence for their work". [ 50 ] :11

Cautioning against interpreting neuropsychological research as supporting the applicability of learning style theory, John Geake, Professor of Education at the UK's Oxford Brookes University, and a research collaborator with Oxford University's Centre for Functional Magnetic Resonance Imaging of the Brain, commented in 2005: "We need to take extreme care when moving from the lab to the classroom. We do remember things visually and aurally, but information isn't defined by how it was received." [ 51 ]

The work of Daniel T. Willingham, a cognitive psychologist and neuroscientist, has argued that there is not enough evidence to support a theory describing the differences in learning styles amongst students. In his 2009 book Why Don't Students Like School , [ 52 ] he claimed that a cognitive styles theory must have three features: "it should consistently attribute to a person the same style, it should show that people with different abilities think and learn differently, and it should show that people with different styles do not, on average, differ in ability". [ 52 ] :118 He concluded that there are no theories that have these three crucial characteristics, not necessarily implying that cognitive styles don't exist but rather stating that psychologists have been unable to "find them". [ 52 ] :118 In a 2008 self-published YouTube video titled "Learning Styles Don't Exist", Willingham concluded by saying: "Good teaching is good teaching and teachers don't need to adjust their teaching to individual students' learning styles." [ 53 ]

4.2. 2009 APS Critique

In late 2009, the journal Psychological Science in the Public Interest of the Association for Psychological Science (APS) published a report on the scientific validity of learning styles practices. [ 54 ] The panel of experts that wrote the article, led by Harold Pashler of the University of California, San Diego, concluded that an adequate evaluation of the learning styles hypothesis—the idea that optimal learning demands that students receive instruction tailored to their learning styles—requires a particular kind of study. Specifically, students should be grouped into the learning style categories that are being evaluated (e.g., visual learners vs. verbal learners), and then students in each group must be randomly assigned to one of the learning methods (e.g., visual learning or verbal learning), so that some students will be "matched" and others will be "mismatched". At the end of the experiment, all students must sit for the same test. If the learning style hypothesis is correct, then, for example, visual learners should learn better with the visual method, whereas auditory learners should learn better with the auditory method. As disclosed in the report, the panel found that studies utilizing this essential research design were virtually absent from the learning styles literature. In fact, the panel was able to find only a few studies with this research design, and all but one of these studies were negative findings—that is, they found that the same learning method was superior for all kinds of students. [ 54 ] Examples of such negative findings include the research of Laura J. Massa and Richard E. Mayer, [ 55 ] as well as more recent research since the 2009 review. [ 18 ] [ 56 ] [ 57 ]

Furthermore, the panel noted that, even if the requisite finding were obtained, the benefits would need to be large, and not just statistically significant, before learning style interventions could be recommended as cost-effective. That is, the cost of evaluating and classifying students by their learning style, and then providing customized instruction would need to be more beneficial than other interventions (e.g., one-on-one tutoring, after school remediation programs, etc.). [ 54 ] :116–117

As a consequence, the panel concluded, "at present, there is no adequate evidence base to justify incorporating learning styles assessments into general educational practice. Thus, limited education resources would better be devoted to adopting other educational practices that have strong evidence base, of which there are an increasing number." [ 54 ] :105

The article incited critical comments from some defenders of learning styles. The Chronicle of Higher Education reported that Robert Sternberg from Tufts University spoke out against the paper: "Several of the most-cited researchers on learning styles, Mr. Sternberg points out, do not appear in the paper's bibliography." [ 58 ] This charge was also discussed by Science , which reported that Pashler said, "Just so... most of [the evidence] is 'weak'." [ 59 ] The Chronicle reported that even David A. Kolb partly agreed with Pashler; Kolb said: "The paper correctly mentions the practical and ethical problems of sorting people into groups and labeling them. Tracking in education has a bad history." [ 58 ]

4.3. Subsequent Critiques

A 2015 review paper [ 60 ] examined the studies of learning styles completed after the 2009 APS critique, [ 54 ] giving particular attention to studies that used the experimental methods advocated for by Pashler et al. [ 60 ] The findings were similar to those of the APS critique: the evidence for learning styles was virtually nonexistent while evidence contradicting it was both more prevalent and used more sound methodology. [ 60 ] Follow-up studies concluded that learning styles had no effect on student retention of material whereas another explanation, dual coding, had a substantial impact on it and held more potential for practical application in the classroom. [ 61 ]

A 2017 research paper from the UK found that 90% of academics agreed there are "basic conceptual flaws" with learning styles theory, yet 58% agreed that students "learn better when they receive information in their preferred learning style", and 33% reported that they used learning styles as a method in the past year. [ 62 ] It concluded that it might be better to use methods that are "demonstrably effective". [ 62 ] [ 63 ]

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• A 2015 study found no statistically significant improvement in student comprehension when instruction methods were related to learning style preferences; the researchers argued that "educators may actually be doing a disservice to auditory learners by continually accommodating their auditory learning style preference" (p. 77) since most testing is presented in a written word format only, and therefore all students should have strong visual word skills. See: Rogowsky, Beth A.; Calhoun, Barbara M.; Tallal, Paula (2015). "Matching learning style to instructional method: effects on comprehension". Journal of Educational Psychology 107 (1): 64–78. doi:10.1037/a0037478. https://zenodo.org/record/977853. 
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• Cuevas, Joshua (November 2015). "Is learning styles-based instruction effective?: a comprehensive analysis of recent research on learning styles". Theory and Research in Education 13 (3): 308–333. doi:10.1177/1477878515606621.  https://dx.doi.org/10.1177%2F1477878515606621
• Cuevas, Joshua; Dawson, Bryan L. (March 2018). "A test of two alternative cognitive processing models: learning styles and dual coding". Theory and Research in Education 16 (1): 40–64. doi:10.1177/1477878517731450.  https://dx.doi.org/10.1177%2F1477878517731450
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• Nancekivell, Shaylene E.; Shah, Priti; Gelman, Susan A. (2020). "Maybe they're born with it, or maybe it's experience: toward a deeper understanding of the learning style myth". Journal of Educational Psychology 112 (2): 221–235. doi:10.1037/edu0000366. https://www.apa.org/pubs/journals/releases/edu-edu0000366.pdf. 
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