Contents
- Introduction
- Our Requirements
- Additional Requirements for Publishing
- Understanding VARK
- Ways to Collect VARK Data
- How to Analyze Your VARK Data
- Common Problems with VARK Research
- Sharing Your Research with Us
Introduction
VARK is a popular research topic and every week we receive many requests from people wanting to use VARK in their research. This page is intended to ensure prospective researchers have a good understanding of our requirements when using VARK in research. We encourage you to contact us about your proposed research early on in your planning process – we are happy to provide advice to help you avoid common pitfalls.
If you are interested in using VARK in your research but haven’t yet decided on a research topic, have a look at our suggestions.
Our Requirements
To use VARK copyright materials, such as the VARK Questionnaire, in your research, you must meet the following requirements:
- You must apply for copyright permission from us, using this form: https://vark-learn.com/contact/copyright-permission-form/
- VARK learning preference results must be shared with your research participants.
- You may not replicate the VARK Questionnaire electronically.
See “Ways to Collect VARK Data” below. - You may not modify the VARK Questionnaire.
- Your analysis must use the VARK learning preference categories, NOT the raw scores
i.e. categories such as “mild Visual”, and “VRK”…, not scores such as V=10, A=2, R=9, K=11
See “How to Analyze Your VARK Data” below. - Participants must be at least 12 years old.
Between birth and around 12 years of age, children build their own set of preferences for learning. For example, after birth, they develop preferences for touch (K), voices (A), pictures (V), reading(R), and writing (R) in that development order. It is not helpful to categorize young children as being dependent on any set of preferences when they are in those stages. And they are not suited to respond to questionnaires. - Applicable fees (if any) are paid.
There will be no fee if ALL the following apply to you:- You are a student or employee of an educational institution AND
- You are undertaking the research as part of your role at that educational institution AND
- You use the online version of the VARK Questionnaire on our public website to administer the questionnaire.
In other cases, we will let you know what fees (if any) are applicable after we have reviewed your copyright permission application.
Additional Requirements for Publishing
Our criteria for approving research that is to be published is stricter than for student projects where the research results will only be available within the student’s educational institution. It is not uncommon for us to decline applications for research that is to be published, or for us to require some adjustments to the proposal before approval. For this reason, it is important that you apply early in the process of planning your research so that you can adjust your research design based on our feedback.
If you intend to publish your research, or if you are undertaking the research as part of a Ph.D., in addition to the general requirements listed above, we also require that:
- VARK is appropriate for your research topic.
See “Understanding VARK” below. - Your methodology is robust enough to support your hypothesis.
When describing your methodology, please tell us:- How participants are selected.
- Whether there will be a control group.
- What data you will collect.
- What VARK-related information you will provide to participants.
- How you will obtain and analyze data related to VARK.
- What correlations you will be looking for if any.
- What steps you will take to prove/disprove your hypothesis?
Understanding VARK
VARK is based on an intuitively simple idea, yet it does have some complexities. It is vital that you have a good understanding of what VARK is and what it can (and can’t) do, before deciding on your research topic – if VARK is the wrong tool for your research objective, your results will be meaningless!
Key Points:
- VARK is about helping learners by suggesting study strategies they can use that may improve their learning.
Meta-cognition (thinking about how one learns) has been shown to be effective in improving learning. The primary purpose of using VARK is to encourage learners to think about how they learn best so that they can focus on strategies that work well for them. - It is what learners do that matters.
Just finding out their learning preference is unlikely to improve someone’s learning – it is what they do with that information that matters. That is why VARK includes Helpsheets suggesting study strategies that learners can try. - VARK is NOT about matching teaching materials to learning preferences.
Much research in the past has focused on the idea that learning can be improved by presenting content to learners using the learners’ preferred modalities. This is called the “matching” or “meshing” hypothesis. There has not been any robust research to prove (or disprove) this idea, and in any case, VARK was not designed with this in mind. It would be nice if learning could be ensured through some action on the part of the teacher, but this is wishful thinking – clearly whether learning happens or not has a lot to do with the learner and what they do. In practice, effective learners are often able to translate content into their preferred modalities, no matter which modality is used when learning material is presented to them. For example, someone with a Visual preference may draw a diagram to represent what they are being told, someone with a Read/write preference may take notes (often verbatim), and someone with a Kinesthetic preference may think about how what they are hearing relates to their own experiences. - VARK is about preferences, not strengths.
Just because someone has a low score for Read/write does not mean that they cannot read well! It does mean that they prefer to take in their information in other ways. - There is more to learning than VARK learning preferences.
A wide range of factors influences how well someone learns – motivation, availability of resources, time management, quality of content to be learned, and so on. - “Visual” and “Kinesthetic” are often confused. Visual = Graphic; Kinesthetic = related to experience (particularly one’s own).
VARK’s Visual mode would be better thought of as “graphic” as it is about the graphical representation of information and includes diagrams, maps, charts, drawings, and the use of layout and color.
VARK’s Kinesthetic mode refers to things related to the experience of the real world, particularly one’s own experience. Of course, for some content, real-world experience is impractical or even dangerous – in such cases, the kinesthetic mode encompasses case studies, virtual reality, and models….
What about videos and photos? – They are usually Kinesthetic, not Visual, as they show the real world. - Multimodality: Most multimodal learners need to process information in all/most of their preferred modalities to really feel that they “get it” – just one is not enough! This can make learning more time-consuming but generally leads to a fuller understanding.
- Learning and grades are not necessarily equivalent. Of course, we hope that there is a high correlation between learning and grades and hence that grades measure learning. But unfortunately, this is not necessarily the case – if you intend to measure academic performance in your research, we expect your research design to consider complexities such as how academic success is measured and how closely that relates to learning, whether students are using strategies that match their preference, what part other factors such as motivation play in academic performance, and so on.
- VARK is more helpful for those who are struggling in their learning – students who have already developed study skills that are working well for them are unlikely to get much benefit from using VARK, while it is likely to be much more helpful for those who are floundering in their studies. Any research that attempts to measure the success of VARK without distinguishing between these two groups risks having the data related to the subset of students that VARK may have helped being obscured by the results from the majority who were less affected by VARK.
Ways to Collect VARK Data
There are three ways that you can administer the VARK Questionnaire and collect your data:
- Use the online questionnaire on our website and ask participants to report their results to you. There is no fee for using the online version of the VARK questionnaire.
If using this method, the usual way to do this would be to include a question such as the following in a separate demographic survey:
Please fill in the VARK Questionnaire here and when you have your VARK result, return here.
What is your VARK learning preference result? - Use a VARK Subscription. This will provide you with a dedicated URL for participants to use to fill in the questionnaire online, with their results automatically stored for you. Fees for a VARK Subscription depend on the number of participants you have.
It is usual for research to involve participants answering demographic questions in addition to the VARK Questionnaire – if this is the case in your study, you will need to include a common identifier when collecting the demographic data and the VARK data so that you can match up responses. This might be the participants’ names, student IDs, email addresses, or a random identifier if the data is to be anonymous. - Use paper copies of the VARK Questionnaire and use our Result Analysis service to find out the results.
You will send us a spreadsheet containing the total scores for V, A, R, and K for each student, and we will analyze their scores and return the spreadsheet to you with an additional column showing the VARK learning preferences. There is a small fee for this service.
Note that we do NOT allow anyone to replicate the VARK Questionnaire electronically, under any circumstances. e.g. in Google Forms. So you may NOT create your own online copy of the VARK Questionnaire or include the questions from the VARK Questionnaire in some other survey instrument.
How to Analyze Your VARK Data
Use VARK Categories, NOT Raw Scores
When people fill in the VARK questionnaire, they find out both their VARK learning preference (e.g. “mild Visual”, “VRK”, …), and their scores for Visual, Aural, Read/write, and Kinesthetic (e.g. V=10, A=2, R=4, K=5). When analyzing your data, you need to use the categories of learning preference (“mild Visual”, “VRK” and so on) and NOT the raw scores. See “Common Problems with VARK Research” below for more discussion of this issue.
In your analysis, treat the VARK Preferences more as categories and analyze them in the same way you would analyze categories like gender which don’t have a score associated.
Grouping the Results
It is likely that you would have many different learning preferences in your group of participants, and so if you need to simplify your data, you may group the learning preferences, by talking about, for example, those who have a multimodal preference vs. those who have a single preference, or those who have Visual (or Aural, or Read/write, or Kinesthetic) included in their learning preference (i.e. mild V, strong V, very strong V, VARK, VAR, VAK, VRK, VA, VR, VK) vs. those who don’t.
Common Problems with VARK Research
There are several common issues in both the copyright permission applications we receive and in published research. We outline them here so that you can avoid repeating them in your research.
“Simplifying” the Data
In any group of research participants, there will be a wide range of learning preferences – if most of the combinations of modalities are present in the group, there can be up to 25 different learning preferences (the single preferences: mild, strong, and very strong Visual, Aural, Read/write and Kinesthetic, as well as the multimodal combinations: VARK Type Two, VARK Transition, VARK Type One, VAR, VAK, VRK, ARK, VA, VR, VK, AR, AK, RK). To simplify the analysis, it is tempting to simplify the data, and so some researchers are tempted to use the raw VARK scores, or just look at each person’s highest score.
In a recent study, data was “simplified” by identifying a “dominant” preference for those who have a multimodal learning preference i.e. for the purposes of the research, the modality with the highest score was used, in effect relabeling the multimodal learners as having a single preference. However, many multimodal learners need to use more than one modality to learn effectively, so using just one is unlikely to be satisfactory for them. Identifying one “dominant” modality does not accurately represent the way that those with a multimodal preference like to learn. The researchers went on to look for correlations between the modalities that students were using in their studies, their “dominant” modality preference, and their course grades. It would have been interesting to see whether those who used their preferred modalities in their studies tended to get better grades, but, unfortunately, because of the way that the data was analyzed, we cannot tell. For those students whose used strategies didn’t match the modality that the researchers selected as their dominant modality, we cannot tell whether the strategies they used were from their other preferred modalities. For example, if a student had a VA learning preference, with a higher score for V, their dominant modality would have been labeled as Visual in the research. If they used both Visual and Aural study strategies, but used slightly more Aural strategies, their strategies would be labeled as “Aural”, and as a result, they would have been categorized as having their preference and strategies “disagreeing”, even though they were using study strategies matched to their learning preference. It is no surprise that, after mislabeling up to two-thirds of the students’ preferences and ignoring all but the modality they used the most, no correlation was found between preference, used modalities, and grades.
Using Raw VARK Scores
Because participants can choose more than one option for each question in the VARK Questionnaire, the raw scores for V, A, R, and K are not comparable between people.
A simplified example of a group of 3 participants shows some of the issues encountered when using the raw scores:
Person One: V=5, A=3, R=2, K=3, Preference = mild Visual
Person Two: V=6, A=8, R=8, K=16, Preference = strong Kinesthetic
Person Three: V=7, A=12, R=13, K=6, Preference = AR
In this example, Person Three has the highest score for Visual and yet does not have Visual included in their preference. Person Three’s score for V is higher than Person One’s score for V, and yet Person One has a single Visual preference and Person Three does not have Visual included in their preferences.
When comparing the average score for each modality, K is the highest, and yet only one of the three people has Kinesthetic included in their preferences.
Unsupported Conclusions
Numerous studies focus on describing the distribution of learning preferences among a group of students and then recommend that teaching methods be based on the most common learning preference(s) in the group. The conclusion that learning will be improved by matching the teaching methods to the preferences of the students is not supported by the research methodology; nor is it supported by previous research.
Many researchers propose to look for links between VARK learning preferences and academic performance. However, finding a correlation between the two is not useful unless you can say why there is a link, and unfortunately, there is a tendency to come to erroneous and unsupported conclusions.
For example, if you were to find that those with an Aural learning preference, say, got better grades, you would have no way of knowing whether this was because:
- those with an Aural preference are naturally better at the topic being learned.
- the presentation of the learning content worked better for those with an Aural preference.
- the testing favored those with an Aural preference.
- those with an Aural preference tended to use study strategies appropriate to their learning preference to a greater degree than others.
And so on. With such a wide range of possible explanations, it would be inappropriate to jump to the conclusion that learning content should be presented Aurally, that students with an Aural learning preference should be encouraged to take the course, or that students without an Aural preference should try to improve their Aural skills.
Mis-categorizing Learning Resources
Mis-categorizing Learning Resources
In a recent study, learning content was categorized by VARK modality, with PDF documents labeled as “Read/write”, recorded video presentations labeled as “Visual” and “Aural”, and online video conferencing with following discussion labeled as “Kinesthetic”. While the Read/Write categorization is reasonable (assuming the PDFs contained primarily textual content), the other content is more likely to appeal to those with an Aural preference than those with Visual or Kinesthetic preferences. Clearly, any experimental research that relies on such a miscategorization cannot lead to valid results.
Sharing Your Research With Us
We are always interested in what others are doing with VARK, and greatly appreciate it when researchers share their findings with us. This is not a requirement for copyright approval, but if you are happy to share your results with us, please email them to contact@vark-learn.com.
