Personality Type, Learning Modalities and Academic Performance in
Undergraduate Engineering
Adele H.T. Kam, H.P. Chionh, R. Ram, C.H. Goh
INTI International College Penang, Malaysia
adele@intipen.edu.my
Abstract: The purpose of this study was to investigate the extent to which academic performance
of engineering students are associated with variables related to the realm of learning, specifically
personality type and learning styles. The Myers Briggs Type Indicator® was administered to a
group of 85 students and the VARK inventory to 123 students from the School of Engineering and
Technology at INTI International College Penang. Observations made showed that students who
preferred the MBTI® Thinking dimension performed significantly better academically compared to
students who preferred the Feeling dimension. There was no significant difference in academic
performance associated with the other personality dimensions or any of the VARK learning modes.
The MBTI® and VARK inventories assessed independent aspects of learning which were useful
for consideration in course design. Recommendations according to the research findings were
made.
Introduction
One of the challenges of educators in the classroom is to find effective ways of teaching that can
enhance the learning process and hence improve academic performance of their students. This is
essentially important in engineering education where attrition rates can be high and the
understanding of factors that affect academic performance can be applied to enhance student
retention. Two factors that have been broadly explored are learning styles and personality types.
The Myers Briggs Type Indicator®, MBTI® (Myers and McCaulley, 1985) has been widely
used to assess personality type and learning styles. It has been employed in research to
investigate the relationship between learning styles and academic performance of engineering
students (Rosati, 1997; Felder et. al, 2002; Felder and Brent, 2005) as well as studies concerning
the effects of personality type in engineering education (McCaulley et. al, 1983; O’Brien et. al,
1998). One objective of this study is to investigate the relationship between students’ personality
type and learning styles identified through the MBTI® preferences and their academic
performance in the engineering faculty of INTI International College Penang (IICP).
A learning style is the method of learning particular to an individual that is presumed to allow
that individual to learn best. To date, there are numerous models and theories to describe
learning styles (Felder, 1996), MBTI®, being one of them. Another commonly known model is
VARK (Fleming and Mills, 1992), which emphasizes the preferences of taking in and giving out
information through sensory channels. The VARK instrument helps to identify whether a person
prefers to take in stimuli through visual, aural, read-write or kinesthetic representation. These
representations are commonly found in classroom experiences and investigating the relationship
between students’ preferences for these learning modes and their academic performance may
yield useful information for classroom design considerations, the second objective of this study.
Apart from investigating whether the academic performance of students is dependent on their
personality type and learning styles, this paper also describes the distribution of personality types
and learning styles of the engineering students at IICP. Implications of these findings for
teaching and learning are also discussed.
Personality Type and Learning Style Models
The Myers Briggs Type Indicator®, MBTI® (Myers and McCaulley, 1985), is a reliable and
validated inventory that assesses a person’s personality type. It is based on Jung’s personality
theory (Jung, 1923), which identifies an individual’s orientation of energy and their basic mental
functions. Jung identified that an individual would have an inborn tendency to either focus their
energy outwardly (Extraversion) or inwardly (Introversion). He also identified four basic mental
functions, two opposite ways for perceiving information (Sensing and Intuition), and two
opposite ways to making judgment (Thinking and Feeling). The MBTI® was developed to
implement Jung’s theory so that it could be applied and examined empirically. While the
MBTI® purports to measure personality preferences, it also measures a number of constructs,
cognitive in nature, which relate to the concept of learning styles. The MBTI® type preferences
can be combined to form 16 different personality and learning style types (Lawrence, 1982;
Lawrence, 1997). Characteristics of each style are summarized in Table 1.
Table 1: Learning Style Characteristics for each Personality Preference
Personality
Function
Learning Style Characteristic
Preference
Extraversion (E) Energy is directed outwards, focus Learn best through interacting with
on events and people in the outer people, action and things
world
Introversion (I)
Energy is directed inwards, focus on Learn best through quiet reflection
internal thoughts and ideas
and individual ways
Sensing (S)
Takes in information through the Learn
best
through
concrete
five senses, focus on concrete facts experience, moving step by step with
and experiences
known things to the abstract
Intuition (N)
Takes in information through Learn best through inspiration,
patterns and associations, focus on starting with concepts before practical
imagination and possibilities
details
Thinking (T)
Make decisions using logical Learn best through clear logical
reasoning, focus on objectivity and material, analyzing experiences to
people’s thoughts
find objective truth
Feeling (F)
Make decisions using personal Learn
best
through
personal
values, focus on harmony and relationships, personalizing issues
people’s feelings
and causes that are important
Judging (J)
Oriented to the outer world in a Learn best through instruction that is
planned and controlled manner, organized and which moves in
focus on making decisions and predictable ways, toward closure
setting limits
Perceiving (P)
Oriented to the outer world in a Learn best through stimulation of
flexible and spontaneous manner, something new and different,
focus on exploring options and opportunity for open exploration
being resourceful
The VARK (Fleming and Mills, 1992) is an acronym for Visual, Aural, Read-Write and
Kinesthetic and is another instrument widely used to identify the learning modes of students. The
inventory includes a systematic presentation of questions to identify the preferred learning
modalities of a student. Visual learners prefer the use of diagrams, pictures, videos, slides,
graphs and flow charts to represent printed information. They may also favor teachers who use
gestures and facial expressions. Aural learners concentrate on what teachers and people say.
They prefer to listen rather than take notes and tend to remember interesting examples or stories.
To aid their learning, aural learners may discuss and explain ideas to others or put summarized
notes onto tapes and listen to them. Read-write learners prefer printed words and text as a means
of taking in information, for instance, textbooks, lecture notes and handouts. These learners
benefit from turning reactions, actions and diagrams into words and practicing writing exam
answers. Kinesthetic preference refers to learning achieved through the use of experience and
practice. Kinesthetic learners learn through engaging their five senses, real-life examples, handson approaches and role-plays.
Methodology
The MBTI® Form G questionnaire and VARK Learning Modes Inventory were administered to
the engineering students at IICP through organized workshops and classroom participation
respectively. 85 students completed the MBTI® Form G and 123 students completed the VARK
inventory. Participation was from various cohorts of students and voluntary. Student MBTI®
preferences were obtained through the tabulation of the Form G questionnaire while VARK
preferences were calculated by the VARK scoring spreadsheet (details provided at www.varklearn.com) after totaling the visual, aural, read-write and kinesthetic scores. The generated
VARK results would indicate the preferred learning mode(s) for an individual, which may
consist of only one mode (single modal) or a combination of two or more modes (multi-modal).
Basic descriptive statistics were utilized to evaluate the distribution of personality types and
learning styles of engineering students. Independent samples t-tests and analysis of variance
(ANOVA) were used to explore potential significant differences in academic performance
related to differences in personality types and learning styles. Academic performance is
measured using the cumulative average (CAVG) scores of the student.
Results and Discussion
Distribution of MBTI® personality type and learning styles among engineering students
Descriptive statistics revealed the following distribution of personality preferences among the 85
engineering students included in the study:
(a) Extroversion 40 (47%), Introversion 45 (53%)
(b) Sensing 50 (59%), Intuition 35 (41%)
(c) Thinking 30 (35%), Feeling 55 (65%)
(d) Judging 34 (40%), Perceiving 51 (60%)
This distribution shows that the majority of engineering students in this study are Introvert,
Sensing, Feeling and Perceiving. This is considered an atypical distribution as previous studies
(McCaulley, 1983) observed that engineering students tend to have preferences for Thinking
(74%) and Judging (61%) but the reverse is observed at IICP with Feeling and Perceiving as the
majority types. Demographically, 18 (21%) students were female and 67 (79%) students were
male.
In regard to academic performance, cumulative average (CAVG) scores ranged from 21.94 to
88.0 on a 100 point scale; a range of 66.06. The mean CAVG for the sample is 60.33 with a
standard deviation of 13.73.
MBTI® Personality type and academic performance
Independent sample t-tests were used to compare the academic performance (CAVG) between
students who either preferred Extraversion or Introversion, Sensing or Intuition, Thinking or
Feeling and Judging or Perceiving. Table 2 presents the results of this test.
Dimension
Table 2: MBTI® personality types and academic performance
N
Mean
Std dev
t
df
P-value
Extraversion
Introversion
Sensing
Intuition
Thinking
Feeling
Judging
Perceiving
40
45
50
35
30
55
34
51
57.67
62.70
59.74
61.18
67.17
56.60
58.23
61.74
13.74
13.43
12.81
15.09
12.38
13.07
15.58
12.31
1.706
83
0.092
0.474
83
0.637
3.629**
83
<0.001
1.156
83
0.251
** Significant at 0.01 level
The results in Table 2 showed that there was no significant difference in academic performance
between students with preference for either Extraversion or Introversion, Sensing or Intuition,
and Judging or Perceiving. However the difference in academic performance between students
with preference for Thinking or Feeling was highly significant (t (83) = 3.629, p<0.001). The
95% confidence interval for mean difference was 4.78 to 16.36. The effect size, d, was 0.82. For
the Social Sciences and organizational research, a small effect is viewed as a “d” of about 0.2, a
medium effect, about 0.5, and a large effect, 0.8 or more (Cohen, 1988).
The academic performance of students who preferred Thinking was significantly higher than
those who preferred Feeling, and the effect size was large. This finding is consistent with those
of prior studies (Godleski, 1984; Rosati, 1993; Rosati, 1997; Felder, 2002) and with type theory,
which suggests that the objective and impersonal nature of engineering subjects would be
conducive for students who preferred Thinking but may not be engaging for students who
preferred Feeling. This suggests a need for a better balance in the course design between
technical and social aspects of engineering. More so, given the fact that the majority of
engineering students at IICP had a preference for the Feeling function. However, the observation
made in prior studies (McCaulley, 1983; Rosati, 1993; Rosati, 1997) that students with
preference for Introversion, Intuition and Judging outperformed their opposite counterparts did
not emerge in this study.
Distribution of VARK learning modes among engineering students
The distribution of learning modes among the 123 engineering students included in this study is
as shown in Figure 1. 50% (62) of the students demonstrated a single mode of preference while
another 50% (61) demonstrated multi-modal preferences. Among the students who had a single
mode of preference, the kinesthetic preference is the most dominant (25%), followed by Aural
(17%). The least preferred mode was Visual (2%). Multi-modal students comprise of those who
had preferences for two or more of the VARK modes with 20% being Bi-Modal, 11% being TriModal and 20% having preference for all the VARK modes. Demographically, 31 (25%)
students were female and 92 (75%) students were male.
Figure 1: VARK Proportions of the engineering students
VARK Proportions (n = 123)
VARK
20%
Tri Modal
11%
Bi Modal
20%
V
2%
A
17%
R
5%
K
25%
Learning modes and academic performance
Independent samples one-way ANOVA were carried out to explore potential significant
differences in the academic performance (CAVG) among students who were single-modal, bimodal, tri-modal and those who preferred all modes. The descriptive statistics are presented in
Table 3. The results showed that there was no significant difference in performance across the
four groups (F (3,119) = 1.508, p = 0.216).
Table 3: Descriptive statistics for CAVG with 1,2,3 & 4 modalities (VARK)
No of modality
N
Mean
Std dev
1
62
63.37
11.46
2
24
66.01
10.80
3
13
66.93
13.37
4
24
59.85
12.52
Independent samples t-tests were used to determine whether academic achievement was
associated with a preference for a particular VARK mode. This was accomplished by comparing
whether the academic performance (CAVG) of V and non-V students was significantly different.
Similarly, t-tests were carried out to compare performances of A and non-A students, R and non-
R students and K and non-K students. Table 4 shows the results of this test. The results showed
that there was no significant difference in performance between V and non-V, A and non-A, etc.,
which indicated that academic achievement of the engineering students did not depend on their
learning mode preferences.
Table 4: Independent samples t-test for V, A, R and K preference and CAVG
N
Mean
Std dev
t
df
P-value
V
Non-V
A
Non-A
R
Non-R
K
Non-K
39
84
73
50
46
77
87
36
63.62
63.55
63.17
64.16
62.88
63.99
62.85
65.32
11.43
12.80
12.33
11.16
12.48
11.48
11.83
11.81
0.030
121
0.976
0.458
121
0.648
0.502
121
0.616
1.052
121
0.295
The results of the analysis showed that learning mode preference was not a factor in determining
the academic performance of engineering students. Most engineering faculties teach using the
lecture method, where information is presented through visuals, the spoken or the written word.
In this way, students who preferred either the visual, aural or read-write modes would have an
opportunity to receive information through their preferred channels. Students who preferred the
kinesthetic modality however may prefer to learn through active experimentation and direct
experience. These approaches may be used in laboratory sessions but are not the norm in lectures
as additional time, resource and planning are often required to incorporate practical activities. It
was initially hypothesized that kinesthetic students may attain lower academic scores for these
reasons but it was not so. There are a few possible explanations for this. Firstly, kinesthetic
students may also benefit from doing tutorials that provide practice exercises and solution
samples and this is commonly practiced at IICP. Secondly, lecturers themselves differ in the way
they teach, some giving more emphasis on visuals, some on speaking, some on hands-on
activities, and so forth. The cumulative effect of this is that students of any learning mode should
not be permanently disadvantaged as they take differing subjects by differing lecturers, hence
obtaining a CAVG that does not differ significantly. It is possible that learning modes may play a
significant role if results of specific subject scores, taking into account the teaching style of the
lecturer concerned, are tested with student learning style preferences, an implication for future
research.
Another initial assumption was that multi-modal students would have better academic
performance compared to those who are single-modal since multi-modal students may adapt
more easily to differing lecturing styles. Statistical results however, showed no significant
difference between these two groups. While some multi-modal students would benefit from their
ability to match their preferences with whatever mode(s) being used, there may be others who
need to have at least two, three or four modes involved in learning before they feel satisfied or
secure. This could be a disadvantage compared to students with single preference who may “get
it” once the information channel aligns with their preference. For this reason, multi-modal
students are not necessarily advantaged for academic performance as reflected by the results.
Correlation test between MBTI® learning styles and VARK modes
As two instruments for learning styles were used in this study, a test to investigate whether any
of the MBTI® learning styles correlated to the VARK modes was done. This serves to confirm
that each instrument sought to assess separate cognitive aspects in determining learning
preferences.
Chi-square test of association was performed to find out whether there was an association
between each of the MBTI® dichotomy (E-I, S-N, T-F, J-P) and each of the VARK modality
(V,A,R,K). For example, Chi-square test of E-I against V (Visual and Non-Visual) was used to
find out whether a person’s preference for Extraversion or Introversion is related to the visual
modality (V or non-V). In other words, the test attempted to find out whether the proportion of
visual mode in extrovert students was different from the proportion of visual mode in introvert
students. This approach was repeated between other MBTI® dichotomies and VARK modes.
The result of 16 Chi-square tests performed is shown in Table 5. None of the test was significant
at 0.05 level of significant. This implies that MBTI dichotomies are not related to VARK
modality.
Table 5: Chi-square test of MBTI dichotomy against VARK modality
Sample
Pearson’s Chi-square
df
P-value
Size
with Yate’s continuity
correlation
EI & V
55
0.034
1
0.854
EI & A
55
0.340
1
0.560
EI & R
55
0.802
1
0.370
EI & K
55
< 0.001
1
1.000
SN & V
55
0.001
1
0.978
SN & A
55
0.248
1
0.618
SN & R
55
0.203
1
0.653
SN & K
55
1.041
1
0.308
TF & V
55
<0.001
1
1.000
TF & A
55
<0.001
1
1.000
TF & R
55
0.191
1
0.662
TF & K
55
0.405
1
0.524
JP & V
55
0.339
1
0.560
JP & A
55
<0.001
1
0.996
JP & R
55
0.037
1
0.847
JP & K
55
2.010
1
0.156
One of the MBTI® dichotomies differentiates the way information is perceived, whether through
the dependence of the senses (Sensing) or through relationships and associations (Intuition).
VARK, on the other hand, identifies the preferred sensory channel to receive information, which
is independent of the MBTI® Sensing and Intuition predisposition. It is associated with the
MBTI® Sensing dimension but does not overlap with the other aspects assessed by the MBTI®
as confirmed by the results.
Conclusion
Comparing individual personality types and cumulative average scores clearly showed that
engineering students who preferred Thinking performed better than those who preferred Feeling.
This finding strongly suggests that consideration needs to be given to balance the course design
between technical and social aspects of engineering in order to increase its relevance to Feeling
learners. This ought to be emphasized since a substantially large majority of engineering students
at IICP recorded a preference for Feeling. It may be beneficial to employ a systematic approach
to analyzing the differing needs of Thinking and Feeling students, and then providing them with
different types of learning opportunities highly congruent with their respective styles to reduce
this variance in academic performance. According to type theory, Feeling learners tend to
appreciate personal motivation from their teachers and relationships are critical in encouraging
them to pursue an interest. There was however, no significant difference in the cumulative
average scores between differing personalities in the other dichotomies. Future research
however, could be implemented to observe if personality type would have a greater effect on
students at the lower end of the academic spectrum compared to the better students as noted by
Rosati (Rosati, 1993; Rosati, 1997).
Comparisons between VARK learning modes and cumulative averages showed that academic
performance is not associated with the preference for any particular mode or the number of
modes. Students of differing learning modes showed equal achievement academically. Likewise,
for students who were single modal in comparison to those who were multi-modal. Majority of
the engineering students had a preference for the kinesthetic modality. While there was no
significant difference in the academic performance of this group of students compared to the
other groups, it would be beneficial to take into account their learning needs as it may improve
the level of student satisfaction for the curriculum and learning environment. A lecture style
classroom tends to rely more on one-way communication and written text, which rarely gives
opportunity for a student to learn through practical and experiential ways. Perhaps Asian students
are somewhat used to more passive ways of learning and have little expectation of other
alternatives but incorporating experiential learning into the classroom would probably yield
higher levels of engagement and motivation especially from the kinesthetic students. Although
the results of this study imply that consideration of learning modes in course design is less
critical, it is good practice to utilize multiple modes in our teaching. If this is done, the students
will be taught in a manner that suits their preferences at least for part of the time and yet push
them to function in their less preferred modes at other times, helping them to balance their skills
in those modes.
The MBTI® dimensions and the VARK modes have been found to measure independent aspects
of learning styles as results show no correlation between these two inventories. The former
measures inborn mental functions while the latter measures the preferred sensory channel. Both
inventories have been used in this study to understand the factors that contribute to academic
performance of engineering students. The insights obtained have been applied in the
consideration of course design.
Acknowledgements
We are thankful for the contributions of the members of the Center of Action Research in
Education (CARE) at IICP in organizing the MBTI® workshops. We are grateful to Beh Yeow
Hui who conducted some of these workshops and to the CARE committee’s advisor, Dr. Wong
Teck Foo for his support and guidance.
References
Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences (2nd Ed). Hillsdale, NJ,
Lawrence Erlbaum Associates.
Felder, R.M. and Brent. R. (2005) ‘Understanding Student Differences’, Journal of Engineering
Education, 94:1, 57-72.
Felder, R.M., Felder, G.N. and Dietz, E.J. (2002) ‘The Effects of Personality Type on
Engineering Student Performance and Attitudes’, Journal of Engineering Education, 91:1, 3-17.
Felder, R.M. (1996) ‘Matters of Style’, ASEE Prism, 6(4), 18-23
Fleming, N.D. and Mills, C. (1992) ‘Not Another Inventory, Rather a Catalyst for Reflection’,
To Improve the Academy, 11, 137-155.
Fleming, N.D. VARK: A guide to learning styles. At: www.vark-learn.com/english. Accessed:
March 3, 2004.
Godleski, E.S. (1984) ‘Learning Style Compability of Engineering Students and Faculty’,
Proceedings, 1984 Frontiers in Education Conference, IEEE.
Jung, C. (1923). Psychological Types. New York: Harcourt Brace.
Lawrence, G.D. (1982). People Types and Tiger Stripes: A Practical Guide to Learning Styles
(2nd ed.). Gainesville, FL: Center for Application of Psychological Type, Inc.
Lawrence, G.D. (1997). Looking at Type and Learning Styles (2nd ed.). Gainesville, FL: Center
for Application of Psychological Type, Inc.
McCaulley, M.H., Godleski, E.S., Yokomoto, C.F., Harrisberger, L., and Sloan, E.D. (1983)
‘Applications of Psychological Type in Engineering Education’, Engineering Education, 73,
394-400.
Myers, I. B. and McCaulley, M. H. (1985). Manual: A guide to the development and use of the
Myers-Briggs Type Indicator (2nd ed.). Palo Alto, CA: Consulting Psychological Press.
O’Brien, T.P., Bernold L.E. and Akroyd D. (1998). ‘Myers-Briggs Type Indicator and Academic
Achievement in Engineering Education’, International Journal of Engineering Education, 14,
311-315.
Rosati,P. (1993) ‘Student Retention from First-Year Engineering Related to Personality Type’,
Proceedings, 1993 Frontiers in Education Conference, IEEE.
Rosati, P. (1997) ‘Psychological Types of Canadian Engineering Students’, Journal of
Psychological Type, 41, 33-37.