Personal Learning Profiles

Did you ever want more detailed feedback about your VARK scores – your learning profile. This service is now available for you. It provides a detailed description of your learning preferences using an downloaded PDF and a backup emailed attachment. An example is on this website.

If you would like to use this service, go to the questionnaire, complete it, and look for Personal Learning Profile.

VARK for Athletes and Players

From The Press, Christchurch June 9 2005

Headline: Learning principles in a new coaching book contributed to the Crusaders’ Super 12 success.

Sports Coaching and Learning was written by Neil Fleming, Graeme Robson and Richard Smith and outlines the different ways athletes learn and how best to coach the different styles.
Co-author Neil Fleming, a former employee of Lincoln University, developed a learning preference questionnaire which helps people to find their best possible way to learn. The four ways of learning are visually, aurally, by reading/writing and kinaesthetically, which is by experience.
Canterbury Crusaders coach Robbie Deans said he tried to expose the team to as many different forms of learning as possible. People learn in different ways and “as a coach you give yourself the best chance if you recognise that”.
Deans said after determining the different learning styles, he tried to adjust how he communicated with certain players. Most of the Crusaders were kinaesthetic learners, which was “not surprising”.
The book was launched on Sunday. Coaching manager for the NZ Academy of Sport and co-author Richard Smith said the book was aimed at anyone involved in sport where learning was concerned. The book was not limited to players, and could be used by other learners such as referees and officials.
Smith said as far as he knows the book is the first in the world to combine learning preferences with sport coaching. It was not complicated and the aim was to “provide a coach-friendly tool”.
Head coach of the New Zealand Cricket Academy, Dayle Hadlee, said he too had been using the learning preference techniques. Hadlee said being aware of the different learning styles allowed him to coach groups of players and individuals, differently.

Responding to Your Emails

I receive a large number of emails requesting permission to use the copyright VARK questionnaire and associated materials. I am grateful that people ask for permission and I try to respond to each request within 48 hours. Occasionally my response is returned to me and you do not receive a reply. This may be because:

  • your email address is incorrect,
  • your mailbox is full or
  • your spam checker thinks that VARK is a naughty word.

If you don’t get a reply within 48 hours, please email again, preferably with a solution that will allow me to respond.

VARK and Sports Coaching

VARK is about learning and coaching athletes and players uses many of those same learning skills that teachers use. So with Richard Smith and Graeme Robson, two experienced and skilled coaches I have written a book to help coaches use the concepts of VARK with their athletes and players. It is heavily based on New Zealand examples but you may still be interested so contact Neil Fleming by email.

VARK Software for High Schools

Due to an increased interest in VARK from High Schools, two new versions of the VARK Intranet Software have been made available. These versions of the software include the VARK questionnaire for Younger People and are intended for use in High Schools.

For more information about this software, see the Subscriptions page.

VARK Statistical

Neil D Fleming
October 2002

This is a set of descriptive statistics about the VARK database. Before we analyse the results from the VARK database it is necessary to examine the shape and structure of the questionnaire so that the correct statistical techniques can be used on it.

WHAT DOES VARK INDICATE?

VARK is not a fully-fledged learning style. The word learning style is now used loosely to describe almost any attribute or characteristic of learning but technically the term refers to all the components that might affect a person’s ability to learn. Some inventories report on a number of components in a style (motivation, surface-deep approaches to learning, social and physical and environmental considerations) and some personality inventories have learning characteristics as a part of their descriptions.

VARK deals with only one dimension of the complex amalgam of preferences that make up a learning style. The VARK questions and results focus on the ways in which people like information to come to them and the ways in which they like to deliver their communication. The questions are based on situations where there are choices about how that communication might take place. It is important to say what VARK is not, so that other components are not perceived as being a part of it. VARK has little to say about personality, motivation, social preferences, physical environments, intraversion-extraversion… The choice to limit VARK to modal preferences was made because that is where its designer had most success in assisting with learning. Changing the other dimensions affected learning, of course, but it was the modal preferences that had the most direct application for helping both students and faculty learn.

THE RATIONALE FOR MULTIPLE CHOICES.

Multimodality was the expectation in the questionnaire design. The modal preferences of people are seldom singular. In the majority of cases people will have preferences for a number of modes and they will use strategies associated with their preferences depending on the context or situation. For example they may choose a Read/write response because the situation is biased towards it. Intuitively this makes sense as we seldom act on the basis of input or output from only one mode. For that reason, multimodality (bi-, tri- or quad-) is likely to be the “normal” condition and single-preferences are likely to be less common. Those who have a mild, strong or very strong preference for one mode are still multimodal – it is just that one of their preferences is a little stronger than the others. For example a person with VARK scores of 6 3 3 3 is said to have a single preference for V but is, in fact, still multimodal, though not categorised as such by the VARK algorithm. Some modes, notably K, is itself an amalgam of senses and could be said to be multimodal in the broader sense of that word. An alternative hypothesis, that most people have a uni-modal approach to communication has low probability though it is not impossible.

If multimodality is the expectation in life situations we should allow for it in the structure of the VARK questionnaire and that is why respondents can choose more than one answer to each question. But clearly if everyone chose every answer for every question then VARK would provide few insights into strategies for learning. Allowing for multiple choice, however, reduces the discrimination of VARK. So on one hand we say that multimodality is the norm but on the other hand we are really interested in the relative strengths of particular modes within individuals. It is the ability of VARK to allow multiple choices, yet point out a person’s established preferences in their profile, that is its strength.

SINGLE PREFERENCES

If the database indicated that the respondents choices were distributed evenly across all options then it is likely that the questionnaire would provide less discriminated information for its respondents – most would be VARK. The options to each question are designed so that those with a particularly strong preference will choose the response that matches their preference even when the situation in the question stem is biased towards another mode. That is how VARK discriminates and for that reason the proportion of respondents choosing each option in a question is unlikely to be close to 25% for the four-option questions or 33% for the three-option questions. It is more likely that one or sometimes two options in each question will be very attractive and that only those with a strong preference will choose alternative answers containing their modal preference. We hypothesise that those who have a single-preference continue to choose the “weakest” option despite the attraction of the dominant option (see later). So an uneven distribution across the options is expected. Table One shows this feature in the proportions (percentages) for each question taken from the June-September 2002 database (n=31243).

For ten of the 13 questions there is a dominant or popular choice above an arbitrary benchmark of 37%. For ten questions there is a weak choice (sometimes two) below 15%. V appears as the lowest choice in six questions, A in four, R in one and K in two. Conversely, V is the main choice for three questions, A for two, R for three and K for five questions.

TABLE ONE: Percentages choosing each option
Proportion who chose this option
Question
V
A
R
K
Total
Most popular option
Least popular Option
1
33.8%
28.0%
28.2%
10.1%
100%
V
K
2
11.1%
10.8%
43.8%
34.3%
100%
R
A
3
5.8%
48.6%
26.5%
19.1%
100%
A
V
4
36.2%
30.5%
33.3%
100%
V
R
5
11.4%
15.9%
26.3%
46.3%
100%
K
V
6
12.8%
12.7%
36.5%
38.0%
100%
K
A
7
14.5%
21.0%
15.5%
49.0%
100%
K
V
8
26.7%
43.8%
29.4%
100%
A
V
9
17.2%
27.3%
55.5%
100%
K
A
10
29.3%
29.7%
30.8%
10.2%
100%
R
K
11
11.9%
30.0%
42.7%
15.4%
100%
R
V
12
69.2%
9.1%
21.7%
100%
V
A
13
14.4%
23.8%
24.0%
37.8%
100%
K
V

Note: The table was calculated using all the choices for all respondents for each question. Many respondents chose more than one option.

NUMBER OF OPTIONS CHOSEN

For most questions, (see Table Two) over three-quarters of the respondents chose only one option. Ninety five percent chose one or two options and the percentage that chose all four options was greater than 1% for only four of the nine four-option questions – 5, 6, 7 and 13. The percentage that chose all the three-options questions (4, 8, 9 and 12) was 1%, 4%, 3% and 3% respectively. The fact that single responses were popular may be related to the custom for most questionnaires to allow only one answer per question or to the idea that there are “right answers’ in the VARK questionnaire.

TABLE TWO : How many options did each respondent choose for each question? (%)
Question
One option
Two options
Three options
Four options
No choice
Total
1
71%
25%
4%
0.5%
0.2%
100%
2
79%
18%
3%
0.9%
0.2%
100%
3
78%
17%
2%
0.7%
1.0%
100%
4
88%
10%
1%
N.A.
0.9%
100%
5
72%
20%
5%
2.5%
1.3%
100%
6
70%
24%
5%
1.0%
0.3%
100%
7
65%
26%
6%
2.5%
0.2%
100%
8
79%
17%
4%
N.A.
0.4%
100%
9
76%
21%
3%
N.A.
0.4%
100%
10
72%
24%
4%
0.5%
0.2%
100%
11
74%
20%
4%
0.8%
1.1%
100%
12
84%
12%
3%
N.A.
0.6%
100%
13
64%
24%
7%
3.8%
0.5%
100%
Mean
75%
20%
4%
1%
0.6%

N.A. = not applicable to this question.

THE VARK PREFERENCE SETS – PROFILES

VARK allows for the condition of multimodality but attempts to find, within that obvious multimodality, some strengths for particular preferences. This is similar to saying that everyone has a V, A, R and K profile but some have particular sets of preferences within that. The 23 possible profiles of preferences that VARK identifies are as follows:

VARK Profiles Number
Single preferences V Mild, Strong and Very strong.
A Mild, Strong and Very strong.
R Mild, Strong and Very strong.
K Mild, Strong and Very strong.
12
Bi-modal preferences VA VR VK AR AK RK 6
Tri-modal VAR VAK ARK VRK 4
All four modes VARK 1
Total number of possible profiles 23

 

TABLE THREE: Profiles: Database Distribution of Types
n=31243
Profile
%
Mild
Strong
Very Strong
Subtotals
%
V
3.2
1.8
0.9
0.5
A
5.3
2.7
1.7
0.9
R
15.6
5.9
5.0
4.7
K
17.6
7.4
5.7
4.5
Single preference
41.7
VA
0.4
VR
1.2
VK
3.1
AR
1.9
AK
3.4
RK
4.6
Bi modal
14.6
VAR
0.8
VAK
2.7
ARK
5.0
VRK
4.3
Tri modal
12.8
VARK
30.8
30.8
Total
100%
100%

This means for example that there is some V in several profiles – in the three single V preferences Mild, Strong and Very Strong, and in VA, VR and VK, VAR, VAK, VRK and in VARK. Each mode is therefore represented in 10 profiles, seven of which are overlapping with other modes.

SO WHAT IS NORMAL?

The database samples mainly those who are students and teachers so it is not representative of the total population. Like most databases it also contains duplicates from respondents who visit the site more than once. In the absence of a distinctive distribution what does it mean if R and K are chosen more often? It could be argued that the proportions above indicate biases towards R and K or, that the questionnaire measures what it measures, and that that, is all it does. Is that why there is a trend in the results towards lower proportions for V and A? But this is a circular argument. In questionnaires where only one option can be selected there is a balancing effect. Choosing one option precludes another so if one set of choices is popular then, by definition there will be other less popular choices. If the questions and options were rewritten to balance the proportions we are merely reflecting an hypothesis that modal preferences are balanced within our society. That is a contestable hypothesis and the statistics don’t help us decide, as they are a result rather than a cause.

The content validity of VARK is the best source for resolving this argument. The strength of VARK is that its questions and options are drawn from real life situations and that people identify with the results that they receive. In that lies VARK’s strength. If people found the questionnaire gave them results outside their own perceptions or the perceptions of those close to them, then that would be a reason to re-examine the questionnaire. If we really wanted to balance the results for V, A, R and K we should search/create a number of additional questions so that V and A are the dominant options more often?

As a consequence there is no distinctive distribution of VARK scores – no typical VARK profile for the general population. What we can describe is the most common final scores for each mode (V, A, R and K) which are 3, 3, 4 and 5.

varkPreferencesGraph

 

V, A, R and K PROPORTIONS

The database has 31243 entries but with some respondents having a multimodal profile, the total number of modes represented in the statistics is more than twice that number (72662). If we total all the respondents who had some V, some A some R and some K the results are as in Table Four below . The following calculation is used: -a person with a VA profile is counted as a contributor to both V and A. A person with a VAR profile contributes three modes to the count. Table Four shows the relative proportions of the four modes in the sample.

TABLE FOUR: V, A, R and K proportions (Method One).
V A R K
% 20% 22% 28% 31% 100%
No. 14536 15710 20080 22336 72662

An alternative method of counting is to limit each respondent’s contribution to the count as a single unit by apportioning the modes, ascribing 1.0 for all single modes, 0.5 for each mode where there are two, 0.333 for each mode where there are three and 0.25 where there are four. This is used for Table Five below.

V A R K
% 16% 19% 31% 35% 100%
No. 4964 5841 9545 10894 31243

Note that the total (31243) is the total number of respondents in the database.

TABLE SIX: Groups and the proportions of V, A, R and K.
V A R K n=
Total 16% 19% 31% 35% 31243
Males % 16% 18% 29% 38% 10471
Females % 16% 19% 32% 33% 20173
Students 16% 20% 29% 36% 22380
Teachers 17% 16% 35% 32% 5673
Applied science 16% 18% 30% 36% 2292
Business 15% 20% 31% 35% 4342
Education 16% 19% 29% 35% 4971
Humanities 16% 19% 33% 32% 2097
Other 16% 19% 29% 37% 6882
Science 17% 18% 30% 35% 4564
Social science 15% 21% 32% 33% 2261
Employee 15% 18% 34% 33% 5268
Employer 16% 21% 32% 31% 350
Other 15% 19% 31% 35% 1845
Unwaged 15% 20% 34% 31% 365
Education 16% 19% 30% 35% 26194
Not in education 16% 18% 35% 31% 3076
Pre-university 16% 19% 27% 38% 6680
University 16% 19% 31% 34% 20903

A phenomenon that has been present in VARK since its design is the Alps/Staircase nature of the teacher/student proportions for each of V, A, R and K. The student graph rises from V to K (Staircase). The faculty graph drops from V to A then rises to R and drops again to K (Alps).

The interesting statistics in Table six are:

  • The proportion of females who complete the questionnaire compared with males. Why are there almost twice as many women?
  • The Alps/Staircase shape of the students and teachergraphs.
  • The remarkable symmetry in the results for the subsets. Few groups seem to vary significantly from the total database proportions of 16%, 19%, 31% and 35%. Those that do are often small subsets – waged (n=3650 employer (n=350) or those who could have used the VARK for Younger questionnaire that has questions more appropriate for those aged 12-18. (n=-6680).
  • Most of the variation is in the R and K proportions.

THE DISTRIBUTION OF V, A, R AND K SCORES

The VARK website algorithm calculates each respondent’s profile based on their V, A, R and K scores from the questionnaire. The scores vary from zero (0) to 12. The frequency of each V, A, R and K score for all respondents is shown in the graphs below.

graph_VFreq
graph_AFreq
graph_RFreq

graph_KFreq

The shape of the kinesthetic graph is different from the other three that are remarkably similar (skew). The reasons for that are not known but the effect is to lift the representation of K in respondents’ profiles.

The distribution of those who had a zero score (voids) for a particular mode is shown in Table Seven as is the number of respondents who chose all twelve options for a particular mode. Note that 31 respondents chose all 48 options in the questionnaire!

TABLE SEVEN: Voids and All Options (n=31243)
Mode Number who chose no options for this mode. % of total respondents Number who chose all options for this mode
V 633 2.0% 39
A 1563 5.0% 66
R 798 2.6% 134
K 237 0.8% 53

 

NUMBER OF OPTIONS CHOSEN

VARK has 13 questions with 48 options spread across those questions. Each of the four modes (V, A, R and K) can be selected 12 times. Because each respondent may choose more than one answer for each of the questions the possible number of answers for any single respondent is 48 (12 V, 12 A, 12 R and 12 K). For a valid entry in the database the minimum number of questions attempted has been set at 10. Just over forty percent (40.6%) chose thirteen options (Note: not necessarily one per question) and 85% of respondents chose between 10 and 20 options.

 

graph_noOptions

THE APPROPRIATENESS OF THE OPTIONS.

It might be helpful to know which options were “working” and which were not. One possibility for VARK was to design options so that each would attract significant numbers of respondents. This would have led to almost equal proportions of respondents opting for each choice – for each question there would be equal numbers of respondents choosing each option. But because VARK allows multiple answers to each question and because there is no right or correct answer, the proportions are variable. To test this we looked at the least preferred option in each question and analysed who was choosing it. The hypothesis was that as long as it was being chosen by a number of respondents who had that preference then it was assisting with the overall discrimination.

Who chose the weakest options?

If the weakest option was still being selected by those who had some preference for the mode represented in that option then one could be confident that the questionnaire and its options were working appropriately. The alternative hypothesis that those who had no preference for the mode were choosing that option would indicate that the option was wrongly worded or poorly selected. Table Eight indicates the percentage of respondents who had a single VARK preference that matched the weakest option for each question. The percentage who had the mode of the weakest option (V, A, R or K) somewhere in their profile is also shown. The hypothesis is that a substantial proportion of those with a single preference for the weakest option will have chosen it and that a number of those with that mode as part of their profile will also have chosen it. So where V is the weakest option in a question we would expect that a substantial number of those with V as part of their profile (VA, VR, VK, VAR, VRK, VAK, and VARK) and those with V as their single preferred mode (Mild, Strong or Very strong) will have chosen it.

In Table Eight below the third column shows that 27% of those with a single preference for K chose K for this question. The fourth column shows, for example, that 43% of all those who chose K in question 1 had K as part of their profile (single or multimodal). There is wide variation in these data. Note that questions 4, 8, 9 and 12 have only three options so one would expect their percentages for the weakest option to be higher. That is not the case for question 12.

TABLE EIGHT: VARK profiles of those selecting the weakest options.
N=31243
Question Number Weakest option Percentage of those with a single preference for the mode in column 2 who chose that option in this question. Percentage of those who chose the weakest option who had the mode in Column 2 as part of their profile.
1 K 27 43
2 A 27 28
3 V 23 28
4 R 57* 35*
5 V 36 26
6 A 43 29
7 V 48 27
8 V 66* 25*
9 A 54* 29*
10 K 25 37
11 V 41 27
12 A 21* 28*
13 V 51 27

* Three options in these questions.

SOME INTERESTING PROFILES

Those with voids (zero scores for a mode) have interesting profiles as do those who choose all 12 of a particular mode. Here is a sampling of those from the database.

Those with Double Voids (sample only)
No V or A
V A R K Profile Sex Education/ Not education Student/ teacher University/ Pre-university Discipline Employed?
0 0 9 5 very strong R female education student university education
0 0 10 3 very strong R female education student university education employee
0 0 7 6 RK male education student university applied science
0 0 7 6 RK female education teacher university other employee
0 0 7 6 RK male student university other
0 0 3 10 very strong R female education student university other
0 0 3 10 very strong R female Not in education other
0 0 6 7 RK male Not in education teacher employee
0 0 8 5 strong R male education teacher university science
0 0 9 4 strong R male education teacher university social science
0 0 7 6 RK female education student university science
0 0 7 6 RK male Not in education student university applied science employee
0 0 9 4 very strong R female education student university other
0 0 6 7 RK female Not in education employee
0 0 5 11 very strong R female education student university education

 

No A or R
V A R K Profile Sex Education/ Not education Student/ teacher University/ Pre-university Discipline Employed?
11 0 0 2 very strong V female education student university social science
10 0 0 3 very strong V male education student university science
9 0 0 4 very strong V female education student university science
11 0 0 4 very strong V female education student Pre university science
8 0 0 5 strong V female education student university social science
8 0 0 5 strong V male education student Pre university other
8 0 0 5 strong V female education teacher Pre university other
10 0 0 5 very strong V female education teacher university education
5 0 0 6 VK male education student university applied science
8 0 0 6 mild V female education student university science
8 0 0 6 mild V female education teacher Pre university education employee
6 0 0 7 VK female education teacher Pre university humanities
6 0 0 7 VK female education teacher Pre university education employee
6 0 0 7 VK female education student university science
5 0 0 8 strong K female education teacher university science
5 0 0 8 strong K female education student university social science
5 0 0 8 strong K male education student university applied science
5 0 0 8 strong K male education student university social science
4 0 0 9 very strong K female education teacher Pre university other other
4 0 0 9 strong K female education student Pre university other
4 0 0 9 very strong K female education student university science
4 0 0 9 very strong K male education student Pre university business
4 0 0 9 very strong K female education teacher university education
6 0 0 9 strong K female education student university other
3 0 0 10 very strong K female education student Pre university education
3 0 0 10 very strong K male education student university education
4 0 0 10 very strong K female education student university other
2 0 0 11 very strong K male education student Pre university science
3 0 0 11 very strong K female education student university humanities employee

 

Some of the 31 who chose all the options in all the questions!
V A R K Profile Sex Education/ Not education Student/ teacher University/ Pre-university Discipline Employed?
12 12 12 12 VARK male education student pre university science
12 12 12 12 VARK male education student university social science
12 12 12 12 VARK male education student university applied science employer
12 12 12 12 VARK male education student pre university science
12 12 12 12 VARK male education student pre university business
12 12 12 12 VARK female education student university business employer
12 12 12 12 VARK female not in education teacher pre university business unwaged
12 12 12 12 VARK education student university other employer
12 12 12 12 VARK male education student university business
12 12 12 12 VARK female education student university other unwaged
12 12 12 12 VARK male education student pre university science
12 12 12 12 VARK education teacher university other
12 12 12 12 VARK male education student pre university business
12 12 12 12 VARK male education student university business other
12 12 12 12 VARK male education student university applied science
12 12 12 12 VARK male education student pre university other
12 12 12 12 VARK male not in education student pre university science other
12 12 12 12 VARK female education student pre university science
12 12 12 12 VARK male not in education student employer
12 12 12 12 VARK male education student pre university other
12 12 12 12 VARK female education teacher university applied science unwaged
12 12 12 12 VARK female education teacher university other unwaged
12 12 12 12 VARK female education student university other

Books on VARK

Charles Bonwell and Neil Fleming have completed a book to support the VARK questionnaire and its helpful learning strategies. As well as chapters about each of the single and multiple modalities of VARK there are comments from those with strong preferences and helpful hints for those with strengths and weaknesses in each of the modes. It is available at a price suitable for students.

A second book suitable for faculty and those interested in understanding VARK and its development has been completed by author Neil Fleming and is available for purchase. Use online purchasing or email for an order form.