Friday, October 28, 2011

VARK: Take a look at the sources

After the past few posts, I needed one that was a little shorter and less intense. One of the things I first noticed when reviewing some of the VARK papers (Fleming, 2006; Fleming, 1995) was that they didn’t cite other research. One of the papers had a link to a research bibliography (http://www.vark-learn.com/english/page.asp?p=bibliography). On this page, along with in the initial VARK paper (Fleming & Mills, 1992), I found some interesting sources.


Some of these are expected based on my previous posts (Bandler and Grinder’s The Structure of Magic Vol I and II, and Frogs into Princes- for more on these see my post on Neuro-Linguistic Programming here). But some of these came as a surprise. For example, Mike Gazzaniga’s and Roger Sperry’s work in the split brain experiments are listed here. Also, listed is Alan Baddeley’s work with memory. My experience with these three is minimal, but any student of psychology has some experience with the work of these three researchers.


To elaborate a bit: Roger Sperry won a Nobel prize in 1981 for his work with cats and, later, with monkeys. So, his work was obviously well received. I was lucky enough to see Mike Gazzaniga give the keynote address at Cognitive Neuroscience Society a few years back. He discussed his work with split brain populations (some of which he worked on with Sperry, I believe). Gazzaniga writes well-received textbooks in psychology (Psychological Science, and a book in Cognitive Neuroscience as well- the name has slipped my mind).


As far as Alan Baddeley, he is a HUGE name in learning and memory research. Baddeley has developed one of the main models for processing memories which is discussed in most introductory courses. He has written many articles and books on memory as well.


So, I guess this post is a bit of a teaser. Seeing these articles cited within the VARK bibliography makes me wonder what exactly from these articles was used to develop VARK. Going into these will be a topic of my next few posts.

References:

Fleming, N.D. & Mills, C. (1992). Not Another Inventory, Rather a Catalyst for Reflection. To Improve the Academy, 11, 137-155.


Fleming, N.D; (1995), I'm different; not dumb. Modes of presentation (VARK) in the tertiary classroom, in Zelmer,A., (ed.) Research and Development in Higher Education, Proceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia (HERDSA),HERDSA, Volume 18, 308 – 313.


Fleming, N., and Baume, D. (2006) Learning Styles Again: VARKing up the right tree!, Educational Developments, SEDA Ltd, Issue 7.4, Nov. 2006, 4-7.

Wednesday, October 19, 2011

Classroom based research: K vs Non-K learners with "hands-on" material

There is a decent amount of classroom based research out there using learning styles. Here is a review of one paper I found that has some interesting information.
For this study, instructors in an Introduction to Design course (from the Engineering Dept) restructured their course to include more of a “hands-on” component. They wanted to know whether the restructuring helped some types of students more than others. So, students in the course were given the VARK questionnaire and the Myers-Briggs Type Indicator (MBTI) to classify their learning preferences. To assess the effectiveness of the lectures, the students were given a survey (immediately following each lecture) to measure various self-perceptions about how the material had been taught (including things like interest level and applicability). Through this, the authors tried to examine whether the class restructuring was more “helpful” for certain types of learners compared to others.
Two different professors that taught the course were involved in this study. They examined each of the lectures in the class and evaluated them individually for the amount of “hands-on” content in the lecture. Weights of 0, 1, 2, or 3 were assigned to each lecture. A 3 indicated continuous hands-on interaction throughout the lecture. A 2 indicated hands-on activity for most of the lecture, while a 1 indicated that there was a hands-on demo in the class (and a hands-off lecture). A weight of 0 would indicate no hands-on component for the lecture (this is my inference, since this was not articulated in the paper).
They examined the Kinesthetic (K) learners vs non-Kinesthetic (non-K) learners from the VARK questionnaire and they examined the Intuition (N) vs the Sensing (S) types from the MBTI. The N type (from MBTI) “focuses on possibilities, future use, [and the] big picture.” The S type (from MBTI) “focuses on the five senses and experience.” According to the MBTI, these are indicative of the “manner in which a person processes information.” Jenson and Bowe hypothesized that their course restructuring will help S types more than N types. They also hypothesized that the K learners will be helped more than the non-K learners (see previous posts for explanations of the VARK theory and questionnaire).
To examine whether the lectures were helpful, they gave a quick survey to their students. The survey asked students:
            Please rate the following statements on a scale from 1 to 10:
1.      Today’s class kept me interested.
2.      Today’s class was a good learning experience.
3.      This class prepared me well to apply today’s concepts to problems.
4.      This class motivated me to further explore today’s concept.
Although these are all valuable questions to ask, there is a fairly large issue (in my opinion) with this survey. These survey questions are asking for opinions. In question 3, students were asked whether they felt that the lecture prepared them to apply what they learned to problems. This is a good question; however, the survey was given before the students actually did their HW (it was given right after the lecture). A student may feel that the lecture has prepared them, but how accurate is that perception? Where is the evidence that these students are actually prepared? There is nothing here about class performance. How did these changes affect the grades of these students? That would have made a much more compelling argument in favor of learning styles.
Let’s look through the data:
The first noticeable thing is that the mean rating for each of the four questions that are provided for S types, N types, K types, and non-K types show interesting patterns. Notice that N-types rate higher than S-types (on average) for each question except the first. Non-K types rate higher than K-types on all four of the questions. This is a bit shocking considering that, according to the paper, 75% of the lectures have a significant hands-on component after the restructuring. If there is such a large hands-on component, why are the non-K types rating them all higher? One explanation may be that the 75% given early in the paper is not an accurate number. In some of the graphs provided in this paper, I was able to count the number of lectures that were given each particular hands-on weight. The weights reveal that 8 of the 17 lectures received either a 1, 2, or 3. This particular contradiction is not addressed in the paper. How can 75% of the lectures have a significant hands-on component, yet less than 50% of them get assigned a hands-on weight of 1, 2, or 3. That just doesn’t make sense.

The top column indicates mean (X-bar) and standard deviations (sigma) for each question (Q =1, 2, 3, or 4)
From Jenson and Bowe (1999)

Next, the authors decided to look at average deviation from the mean. This can be useful when looking at how ratings changed. Because the N types started off higher than the S types, a direct comparison of their scores would be misleading. So, instead the authors chose to examine how the scores changed. They asked questions like: are the S types more likely to deviate above the mean than the N types? They asked the same question for the K types vs the non-K types.

Again the data are interesting. The authors report their largest differences for questions 1 and 2. According to the paper, for question 1 (about whether the lecture was interesting), S types scored ½ a standard deviation above the mean and N types scored 1/20 of a standard deviation below the mean. K types scored about 1/10 of a standard deviation above the mean while non-K types scored 1/3 of a standard deviation below it. Is it surprising that people that identify themselves as having a preference for kinesthetic information find lectures with hands-on components more interesting? Not really. The same can be said for the difference between the S and N types in this question- this data is really not very surprising considering the S types prefer to have information through their senses rather than to be given a hypothetical or theoretical idea to deal with. The more hands-on a lecture, the more satisfied the K and the S types.
The data for question 2 is actually a little disturbing from a teaching perspective. Question 2 is about whether the lecture was a good “learning experience.” S types see a small increase above their mean response (1/5 of a standard deviation) with the hands-on lectures and N-types see no real change for hands-on when compared to non-hands-on lectures (1/100 of a standard deviation decrease below the mean). While S types find that the hands-on components increase their learning experience, the N types don’t seem to care. Again, since we are dealing with preferences of sensory modality here, this is not a surprising result. The surprise comes when the K vs Non-K data is examined.
K types rated the hands-on lectures approx 1/20 of a standard deviation higher than the mean rating. That is not a large effect by any stretch of the imagination. The non-K types rated the hands-on lectures nearly ½ of a standard deviation below the mean. Now, I know the purpose of this study was to find ways to benefit those students with “learning styles” that are usually neglected with traditional teaching methods- I get that. But their “hands-on” lectures actually led to a large portion of the class saying that those lectures were a lower than average learning experience. This decrease for the non-K types was not accompanied by an increase for the K types. One can conclude that the addition of the hands-on components decreased learning experiences for some students while providing no increase for others. That is a net loss for students- why make such a change?
Thus far, I have given my explanations and responses for their explanations of their data. As usual, I like to look at the data itself to draw my own conclusions. The figures provided in this study provide some additional information that isn’t given in the author’s conclusions. For example, the ratings are all over the place. I have summarized how many data points (lecture ratings) were above and below the mean ratings for the various groups below. In italics are all of the ratings that fly in the face of the general pattern described by the authors (namely that S types do better with hands-on than N types and that K types do better at hands-on than non-K types).

Fig 3:   S-3: 3 above 1 below, S-2: 2 above, S-1: 1 below 1 above
N-3: 1 above, 2 at the mean, 1 below, N-2: 1 above 1 below, N-1: 2 below


Fig 4:   K-3: 3 above 1 below, K-2: 1 above 1 at the mean, K-1: 2 below
NK-3: 1 above, 3 below; NK-2: 1 above 1 below; NK-1: 1 above 1 below


Fig 5:   S-3: 2 above, 2 below; S-2: 1 above, 1 at the mean; S-1: 1 above, 1 below
N-3: 2 below, 2 above; N-2: 1 above, 1 below; N-1: 1 below, 1 at the mean


Fig 6:   K-3: 2 above, 1 below, 1 at the mean; K-2: 1 above, 1 below; K-1: 2 below
NK-3: 4 below; NK-2: 1 above, 1 below; NK-1: 2 above


Notice that of the 12 comparisons made overall for the lectures with hands-on content in these figures, 5 of them are bolded and italicized. That means that 5/12 of their comparisons either show no difference between the type of learner or show a difference that is the exact opposite from what the authors proposed. I did not go through this analysis for their ratings from survey questions 3 and 4. The data from those are even less convincing.
Also I believe it is important to note that the information that was being studied was a greater predictor for the class responses than the amount of hands-on components in the lectures. For example, lectures 7 and 14 have universally low ratings. This was true across nearly all of their survey questions and across all of their learning types (in both MBTI and VARK). Perhaps the more important factor is the information being provided and communicated to the students and not the way it was communicated. Notice that lecture 7 was weighted as a 3 for their hands-on scoring and that lecture 14 was weighted a 0 for their hands-on scoring. Are learning style theorists making this an issue of style over substance? Maybe all that matters is the substance (the material) and the style (Visual, Auditory, Reading, Kinesthetic) is irrelevant? These data seem to support that notion.
The authors conclude with “…overall it is shown that the addition of the hand-on experiences significantly improves design courses.” I am disappointed with the use of the word “significantly” here- from what is written in the paper, no statistical significance testing was conducted in this study. Also, I really wonder what is meant by “improves design courses.” We have no idea whether students were more successful in these courses due to their restructuring.

References

Jensen, D., Bowe, M. (1999). “Hands-on Experiences to Enhance Learning of Design: Effectiveness in a Reverse Engineering / Redesign Context When Correlated with MBTI and VARK Types,” Proceedings of ASEE Annual Conf., Charlotte, NC.

Friday, October 14, 2011

NLP begat VAKOG, VAKOG begat VAK, VAK begat VARK

As I have been promising, here is the much awaited post on the origins of VARK (for information on what VARK is, see previous posts).
Searching for the origins of VARK led me on what I can only compare to a family tree research project. Trying to track the origins of VARK was not easy; many of the papers on VARK (including many of the Fleming papers I have previously reviewed) are lacking in citations. However, in the initial VARK paper (Fleming & Mills, 1992) the authors mention that there had previously been discussion of the influence of sensory modality preferences on behavior and learning in research and theories that surrounded a topic known as Neuro-linguistic programming (NLP).

Now, I have a BS in neurobiology and a PhD in psychology (my research was in cognitive neuroscience). My graduate advisor studies language and the brain and has helped to develop one of the eminent models for how language is processed in the human brain. So, one would naturally conclude that I have studied something called Neuro-linguistic programming…right?
Wrong! I can honestly say that before VARK I had never heard of NLP. I would venture to assume that my former advisor, an expert on language and the brain (he is writing a textbook on the subject), has never heard of NLP. So, what the heck is NLP?
My searching led me to find that NLP was created by Richard Bandler (a student of mathematics and computer science) and John Grinder (professor of Linguistics). According to Tosey & Mathison (2007), NLP “denotes a view that a person is a whole mind-body system with patterned connections between internal experience (‘neuro’), language (‘linguistic’), and learned behavioral strategies (‘programming’).”
From what I can piece together, the original NLP studies consisted of case studies of three psychotherapists: Fritz Perls (founder of Gestalt therapy), Virginia Satir (a family therapist), and Milton Erikson (a hypnotherapist) (Tosey & Mathison, 2007). Bandler and Grinder noticed that there were certain characteristic in common with the way that these therapists treated their patients. They noticed language use, tone of language, body language, and many other behaviors these therapists emplyed during their psychotherapy sessions. Bandler and Grinder deemed these three therapists as very successful therapists (I have no idea whether they are correct or not- that's getting way off topic). The point was, if they could specifically address and model the specific behaviors of a successful therapist, other therapists could be trained to employ such techniques as well and more patients could be helped.

So, Bandler and Grinder's theory was specifically developed with therapeutic applications in mind, but (since the 1970's) has been more generally applied as a method of communication and personal development in the following fields: managerial, sales, marketing, consulting, medicine, and law.
To backtrack a bit, I have a confession to make. When I first saw that there was this topic (NLP) which I had never heard of, I did what I usually do whenever I want to look up information on something that I otherwise know nothing about- I googled it and followed the first link to Wikipedia. Now, this is pretty hypocritical of me. I don't allow students to use Wikipedia as a source for their research papers. I always tell my students to look at Wikipedia for information, but to just make sure you go and validate that information with a more reputable source (a journal article or a book). But here I go quoting Wikipedia anyway:

Reviews of empirical research on NLP showed that NLP contains numerous factual errors, and failed to produce reliable results for the claims for effectiveness made by NLP’s originators and proponents. According to Devilly, NLP is no longer as prevalent as it was in the 70s and 80s. Criticisms go beyond the lack of empirical evidence for effectiveness; critics say that NLP exhibits pseudoscientific characteristics, title, concepts and terminology. NLP is used as an example of pseudoscience for facilitating the teaching of scientific literacy at the professional and university level. NLP also appears on peer reviewed expert-consensus based lists of discredited interventions. In research designed to identify the “quack factor” in modern mental health practice, Norcross et al (2006) list NLP as possibly or probably discredited, and in papers reviewing discredited interventions for substance and alcohol abuse, Norcross et al (2008) list NLP in the “top ten” most discredited, and Glasner-Edwards and Rawson (2010) list NLP as “certainly discredited”.

So I had just started looking into NLP and already my “uh-oh” radar was going off .

When looking for the theoretical papers on NLP, I found that most of that information had been published in a handful of books. These were titled The Structure of Magic I: A book about language and therapy and The Structure of Magic II: A book about communication and change. Their most popular book on NLP was titled Frogs into Princes. This set my "uh oh" radar going again. This focus on book writing instead of journal article writing is important in academics. Books are not peer-reviewed. True scientific writing, theory, and research goes into a journal. All writing in journals goes through a rigorous peer review process (if you have ever been on the receiving side of a tough review, you know just how rigorous this process can be!).
Alas, my purpose for this post was not to write about NLP's merit or lack of merit. My purpose was to write about how NLP led to VARK. This is why it has taken me so long to write this particular post. I wanted to find specific parts of NLP and how those eventually branched into VARK, not get into the “NLP is good or bad” argument. After much searching, I eventually found what I was looking for.

So, one way to define NLP is as a method/model of effective communication. Such a model would contain various components of the communication process including specifics on the sending and receiving of information. The NLP model includes all of this and even further subdivided effective communication into verbal and non-verbal processes and many other categories.

To elaborate, NLP proposed that we have “representational systems” (also known as sensory modalities) as part of our communication process: "At the core of NLP is the belief that, when people are engaged in activities, they are also making use of a representational system; that is, they are using some internal representation of the materials they are involved with, such as a conversation, a rifle shot, a spelling task. These representations can be visual, auditory, kinesthetic, or involve the other senses. In addition, a person may be creating a representation or recalling one. For example, a person asked to spell a word may visualize that word printed on a piece of paper, may hear it being sounded out, or may construct the spelling from the application of a series of logical rules." (Druckman, 1988)
To represent all of the senses, the abbreviation VAKOG (Visual, Auditory, Kinesthetic, Olfactory, and Gustatory) was formed. Over time, it was refined to VAK, as those are the three main sensory modalities that are used. One of the more bizarre and refuted aspects of NLP is that someone’s eye gaze reveals their mode of internal processing. This portion of the theory is just plain bizarre (and according to research, just plain wrong!- see Sharpley (1987) for more on this). See the image below- this was claimed to be one of the common arrangements for how eye movements and internal processing related.


From Bandler and Grinder (1979)


Vc = constructed visual image (ie. imagining something in pictures)
Vr = recalling visual image (ie. remembering something in pictures)
Ac = constructed auditory signal (ie. imagining something in sounds)
Ar = recalling auditory signal (ie. remembering something in sounds)
K = kinesthetic processing (ie. Revealed by their use of words- for example, if someone is thinking about a topic and they say “I just can’t get a grip on it” – the use of the word grip indicates kinesthetic processing)
Ai = auditory internal dialogue (ie. Talking to one’s self)

Bandler and Grinder also proposed that we have preferred "representational systems" that we use when communicating with others. It is not hard to see how this particular aspect of NLP lead to current VARK learning style theories.

On a related side note, Bandler later backpedaled on the "preferred representational systems" part of NLP theory and had revised NLP to minimize the importance of such systems (Druckman, 1988)).

So, it appears NLP suggested preferred representational systems (sensory modalities), labeled them as VAKOG, then VAK, and Fleming & Mills (1992) tweaked it to VARK.


Daniel Druckman (Ed.) (1988), Enhancing Human Performance: Issues, Theories, and Techniques(pp.138-139)

Sharpley C.F. (1987). "Research Findings on Neuro-linguistic Programming: Non supportive Data or an Untestable Theory". Journal of Counseling Psychology, 34, (1), pp 103–107,105. 

Bandler, R & Grinder J. (1979). Frogs into Princes: Neuro Linguistic Programming. Moab, UT: Real People Press.

Fleming, ND & Mills, C. (1992). "Not Another Inventory. Rather a Catalyst for Relection." To Improve the Academy, 11, 137-155.

Tosey, P & Mathison, J. (2007). "Fabulous Creatures of HRD: A Critical Natural History of Neuro-Linguistic Programming." International Conference on Human Resource Development Research and Practice across Europe, Oxford Business School.

Sunday, October 9, 2011

VARK Learning style preferences: group comparisons

My apologies for the delay between posts- I have been reading through quite a few learning style papers out there and had some trouble deciding where to go with my next post. The vast majority of my reading focused on the topic of Neuro-Linguistic Programming, which will be the focus of a post in the near future.

For the current post, I decided to review a paper published last year by John Dobson from the University of Florida. Dobson published a paper looking at learning style preferences in an exercise physiology class. The article is written with the assumption that learning styles exist; so, the purpose of Dobson’s paper wasn’t to examine the existence of learning styles. His purpose appears to be to examine the proportion of learner type amongst groups of students in an exercise physiology class. Specifically, he wanted to know whether graduate students and undergraduate students have different learning style preferences. He also wanted to know whether a difference in preferences existed across genders and, at the end of the study, he examines whether course performance had anything to do with learning style preference.

In the study, Dobson compared perceived modality preferences and assessed modality preferences across his domains of interest. Basically, he asked students what they thought was their modality preference for learning (Visual, Auditory, Reading, or Kinesthetic). He called this their perceived learning style preference. Then he gave them the VARK questionnaire to assess their learning style preference. He called this their assessed learning style preference. Some similarities were found as well as some interesting differences.

The study found that 59% of the time the perceived modality preference and the assessed modality preference matched. The authors claim that this matches the numbers that Neil Fleming has found (see previous posts for discussion of Fleming’s  work). A closer look at the data reveals that this number is not very impressive. 64 students completed the study. According to the data, 38 students (59%) had a match between their perceived modality preference and the assessed modality preference. However, in this study, many students (24) were classified as VARK learners by the assessment (which means that they have no real preference, but instead are happy receiving information in either visual, auditory, reading, or kinesthetic modality). It is not very impressive that these particular 24 students matched their VARK score and their perceived preference; given that they have no specific preference, they could have answered ANYTHING on the perceived preference portion of the experiment and Dobson would have concluded that their perceived and assessed learning style preferences “matched.” Again, that means 24 of the 38 that “match” would have matched regardless of what they had listed for their perceived preference!

So, we can get a more accurate gauge about how the perceived preferences and the assessed (ie VARK) preferences matched by excluding that group of 24 students. If we do this, there are 40 students that had a specific preference of one or more modality (ie. all students that were not classified as a VARK learner). Only 14 of those 40 matched with their perceived preferences. That is only a 35% concordance rate for the VARK questionnaire matching perceived preferences, which is quite low and may question the validity of the assessment tool. In another 14 of these 40 students, VARK found a minor preference for their perceived learning style preference. That leaves 12 students (of the 40) where VARK didn’t even list off their perceived learning style preference at all. So these data from this study question the validity of the VARK assessment. How accurate is VARK if it doesn’t match perceived preferences in a majority of cases?

The study wanted to examine whether status, gender, and class performance (grade) varied across learning style preference. For status, the study found no difference between preferences of graduate and undergraduate students (either perceived preferences or assessed preferences).

For gender, the study found no significant differences between preferences of males and females. However, it should be noted that the test statistics were very close to significance for the gender domain. The author claims that this may indicate that a difference really does exist, but due to a small sample (only 40 women and 24 men) they did not achieve significance. A closer look into the data suggests that there might be more going on here than meets the eye. As previously discussed, there were large divergences between assessed preferences and perceived preferences. This was when comparing males and females as well. The perceived preference data shows different patterns for males and females: R preference: 35% female, 17% male; V preference: 25% female, 54% male; A preference: 22% female, 8% male and K preference: 18% female, 21% male. Notice the largest differences between male and female here are in the visual modality and in the reading modality, with the kinesthetic modality having nearly equivalent proportions of males and females.
Assessed preferences show that largest gender difference between those that are K learners (13% male, 5% female), those that are A learners (7% female, 0% male), and many of the combinations of modality preferences (ie the VARK will classify people as a VK (visual and kinesthetic) or a VA (visual and auditory) and all other combinations). I have pasted the table with these data below so that you can see for yourself. The take home point here is that there were no significant differences between genders and the differences that might be there are different depending upon whether using perceived learning style preference or assessed learning style preference. I’ll spend more time on this later in this post.
From Dobson, 2010
For class performance, Dobson found no difference between undergraduate and graduate students (I would assume that grad students would get more difficult assignments and exams which makes comparing these groups difficult- if not, one would question the quality of graduate students that barely outscore undergraduates, no?). No gender differences were found in class performance.

For assessed sensory modality (by VARK), there were no differences found in class performance. With perceived sensory modality, the kinesthetic learners significantly underperformed compared to the rest of the class. Here again is another difference between the perceived learning style preference and what VARK found: as assessed by the VARK, no difference was found with kinesthetic learners.

The author lists the four main conclusions of the study as “1) Nearly two-third of the respondents correctly matched their perceived and dominant assessed sensory modality preferences, 2) there was a significant relationship between perceived sensory modality preferences and course scores, 3) there was no association between sensory modality preferences and status, and 4) there was a nearly significant trend in sensory modality preferences and sex.”

Three of the four conclusions are questionable. Conclusion #3 is ok in my book. Conclusion #1 is a bit misleading, as a large portion of those that “matched” their perceived and assessed learning style preferences had been classified as a VARK and thus would have matched with ANY perceived learning style preference. Conclusion #2 is supported by the data. In fact, the author discussed a previous study that he had conducted which found similar low scoring K learners in physiology classes. But why is this only found in those that perceive themselves as a K learner and not in those that are assessed as a K learner? This serves as further disagreement between VARK assessed learning style preferences and the perceived learning style preferences (more evidence against conclusion #1).

Dobson suggests that teachers may need to focus on K learners in physiology classes and review their teaching styles and practices to ensure that these learners are being helped. Another interpretation comes from other evidence which suggests that those that perceive that they have a learning style preference that is not in line with a teacher’s style of teaching may assume that they are going to do worse and simply not try as hard. Thus, they may be self-handicapping themselves (Reiner, 2010-2011). It is possible that the K learners may be doing this, and thus they will score lower. Thus, the classification of these students into a specific learning style preference may be detrimental to their success in class.

For conclusion #4, the data is quite messy. Statistically speaking, the difference between learning style preferences for males and females is not significant, but it is close. However, a comparison between gender differences between the perceived preferences and the assessed preferences shows no real pattern. Again, the disparity between the perceived preferences here and the assessed preferences provides more support against conclusion #1, but also makes interpretation of conclusion #4 impossible.

Overall, this study doesn’t really provide evidence for or against learning styles; that wasn’t their purpose. However, the data from this study question the validity of the VARK as an assessment of learning style preference.


References

Dobson, J. (2010). A comparison between learning style preferences and sex, status, and course performance. Advances in Physiological Education, 34: 197-204.

Riener, Cedar. Learning Styles: Separating Fact and Fiction. Psychology Teacher Network from the American Psychology Association Education Directorate. Winter 2010-2011. Vol 20, Issue 4.