The Relationship Between Students’ Myers-Briggs Type Indicator and Their Behavior within Educational Systems

Abstract

Leveraging user behavior has become an increasingly valuable resource for modeling and personalizing systems based on the unique characteristics of each user. While recent studies have recently been conducted along these lines, there is still a lack of understanding of the relationship between students’ Myers-Briggs Type Indicators and their behavior within educational systems. Facing this problem, we conducted a long-term study (15 weeks) with 96 students, analyzing how their engagement metrics and communication frequency in a Moodle Learning Management System are related to their Myers-Briggs personality types (i.e., extroversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving). The primary findings indicate that i) participants identified as extroverted demonstrated heightened activity levels throughout more weeks of the course, and ii) participants characterized by judging and thinking traits engaged in a greater number of activities over the course duration. The results contribute to the field of educational technologies by providing valuable insights into the relationships between different characteristics associated with the Myers-Briggs Type Indicator and students’ behavior when using an educational system.

Full text

The Relationship Between Students’ Myers-Briggs
Type Indicator and Their Behavior within
Educational Systems
Akerke Alseitova
SDU University
Almaty, Kazakhstan
akerke.alseitova@sdu.edu.kz
Wilk Oliveira
Tampere University
Tampere, Finland
wilk.oliveira@tuni.fi
Zhaoxing Li
University of Southampton
Southampton, United Kingdom
zhaoxing.li@soton.ac.uk
Lei Shi
Newcastle University
Newcastle, United Kingdom
lei.shi@newcastle.ac.uk
Juho Hamari
Tampere University
Tampere, Finland
juho.hamari@tuni.fi
Abstract—Leveraging user behavior has become an increas-
ingly valuable resource for modeling and personalizing systems
based on the unique characteristics of each user. While recent
studies have recently been conducted along these lines, there
is still a lack of understanding of the relationship between
students’ Myers-Briggs Type Indicators and their behavior
within educational systems. Facing this problem, we conducted
a long-term study (15 weeks) with 96 students, analyzing how
their engagement metrics and communication frequency in a
Moodle Learning Management System are related to their
Myers-Briggs personality types (i.e., extroversion/introversion,
sensing/intuition, thinking/feeling, and judging/perceiving). The
primary findings indicate that i) participants identified as extro-
verted demonstrated heightened activity levels throughout more
weeks of the course, and ii) participants characterized by judging
and thinking traits engaged in a greater number of activities
over the course duration. The results contribute to the field of
educational technologies by providing valuable insights into the
relationships between different characteristics associated with the
Myers-Briggs Type Indicator and students’ behavior when using
an educational system.
Index Terms—Students’ behavior, learning management sys-
tems, Myers Briggs Test Indicator, Moodle, long-term study
I. INTRODUCTION
The integration of educational technology tools has become
integral to contemporary education, as evidenced by numerous
studies [1]–[3]. Over the past few years, a diverse array
of educational technologies, including Learning Management
Systems (LMS), educational games, and virtual reality sys-
tems, has emerged [4]. The impact of these technologies on
education varies, with positive outcomes such as heightened
This work has been supported by SDU University Internal Research
Funding. This work has been supported by the Academy of Finland Flagship
Programme [Grant No. 337653 - Forest-Human-Machine Interplay (UNITE)].
The authors utilized generative artificial intelligence (i.e., Microsoft Copi-
lot) to improve the grammatical quality of the text.
engagement, improved performance, and increased satisfaction
noted in some cases [5], [6]. Conversely, negative experiences,
such as demotivation and frustration, have been reported in
other instances [7]. To improve the quality of these educational
technologies, recent studies are increasingly invested in under-
standing students’ behavior [8]–[10], as a pivotal for tailoring
designs and resources to suit the individual needs of each user
[11]–[13].
At the same time, the data obtained from LMS concerning
students’ behavior can provide us with important information
about real student engagement [14]. The general differences in
user profiles investigated include learning styles [15], gamer
type [16], demographic information [17] and personality type
[18]. Consequently, the identification and modeling of person-
ality in e-learning emerge as critical issues in education, given
that multiple studies affirm the facilitative role of understand-
ing personality in the learning process [19].
In response to this pressing issue, we conducted a long-term
data-driven study (data collected during 15 weeks) involving
96 participants, to understand the relationship between stu-
dents’ personality types as determined by the Myers-Briggs
Type Indicator (MBTI) and their behavior within a LMS.
We aimed to answer the research question: what is the
relationship between students’ MBTI types (i.e., extrover-
sion/introversion, sensing/intuition, thinking/feeling, and
judging/perceiving) and their behavior within an educa-
tional system? To answer the research question, we modeled
this relationship using partial least squares structural equation
modeling (PLS-SEM).
The main results indicate that i) individuals identified as
extroverted exhibit heightened activity levels throughout the
week, ii) those with judging traits tend to engage in fewer
activities, and iii) thinkers demonstrate a propensity for in-

creased activity participation. This study makes a significant
contribution to the field of learning technologies by establish-
ing a model that delineates the relationship between students’
MBTI types and their behavior within an educational system.
II. BACKGROU ND A ND RE LATE D WORK
The MBTI, proposed by Myers and McCaulley [20], as
“four personal preferences affect how people behave in all
situations” [21] is one of two well-established personality tests
at the facet level, exhibiting a strong correlation with other
tests such as the Big Five, CPI 260, Birkman Method, and
Strong Interest Inventory [22], [23].
MBTI is rooted in Jung’s Theory, which identifies four pairs
of opposing preferences: Extraversion (E) versus Introversion
(I), reflecting how an individual directs attention and derives
energy; Sensing (S) versus Intuition (N), illustrating how a
person processes information; Thinking (T) versus Feeling
(F), outlining the decision-making approach; and Judging (J)
versus Perceiving (P), indicating how one interacts with the
outer world [23].
Over the years, MTBI has been investigated in different
areas such as health [24], sports [25], and education [26]. In
the education realm, recent research endeavors have focused
on analyzing, modeling, and predicting the MBTI profile of
students, exploring how these profiles affect their classroom
behavior and learning outcomes [27], [28].
Adewale et al. [29] created a personalized e-learning plat-
form that adapts teaching methods to students’ MBTI types.
Post-assessment data showed a 78% first-attempt pass rate,
with the remainder passing on the second try [29]. Sari and
Bashori [30] analyzed the personalities of Yogyakarta’s school
principals using MSDT for management styles and MBTI for
personality, involving 39 principals. Findings highlighted a
dominance of Extroverted over Introverted traits among them
[30].
Kodweis et al. [31] investigated the correlation between
MBTI personality traits and Clance Imposter Phenomenon
Scale (CIPS) scores among pharmacy students through a
retrospective study. Results indicated that students with intro-
verted, intuitive, and perceptive traits had higher CIPS scores
[31]. In the same year, Guven and Mustul [32] explored
the impact of student-centered learning and MBTI on music
education. Extroverted students thrived in voice training, while
introverted students excelled in instrumental performance [32].
Zhalgassova et al. [33] developed a new MBTI-based rec-
ommendation system for high school students’ extracurricular
activities, leveraging feature engineering and machine learning
for personalized suggestions. This system, tested against tra-
ditional models using precision, recall, and F1-score metrics,
demonstrated enhanced performance, showcasing its capability
to offer tailored activity options [33].
The aforementioned studies collectively underscore a grow-
ing recognition of the significant role that the MBTI plays
in the educational context. Despite the increasing interest,
there remains a paucity of research specifically addressing
the application of MBTI within educational systems. This gap
highlights the need for further exploration and development
in this area. As far as we know, we are the pioneers in
investigating the relationship between students’ MBTI types
and their behavior within an educational system.
III. STU DY DESIGN
In this section, we present the study’s design.
A. Materials and method
Students’ behavior data was collected from a Moodle LMS,
where the students participated in a freshmen course entitled
“Fundamentals of Programming” within the Engineering fac-
ulty at SDU University, Kazakhstan. The course was provided
during the Fall semester of 2021, catering to a total of 787
students. A subset of 95 students actively participated in
the study, voluntarily providing their MBTI information for
subsequent analysis.
The course, spanning 15 weeks, consisted of four main types
of activities: 1) Mini Tests - to test the theoretical knowledge
of students; 2) Weekly contests - to improve problem-solving
skills of students; 3) Quizzes - to examine students theoretical
and practical knowledge; and 4) Projects - to demonstrate
students comprehension of the concepts covered in class by
applying them to real-world systems. Additionally, the course
follows a flipped classroom approach, wherein video lectures
are provided before in-person sessions to facilitate comprehen-
sive discussions and lectures on the topic. The HackerRank
website1served as the platform for both weekly contests
and quizzes, offering competitive programming challenges. In
these activities, students write solutions to designated prob-
lems, and their submissions are scored based on the accuracy
of the output. To assess students’ practical and theoretical
knowledge, the enterprise-level tool, HackerRank for Work, is
employed. An illustrative example of the system is presented
in Figure 1.
Fig. 1. The screenshot from the contest on the Hackerrank website
The study was organized in three different steps: i) MBTI
identification, ii) course participation, and iii) data analysis.
The first step of the study involves investigating the MBTI
1www.hackerrank.com

types of students. Participants were instructed to learn their
MBTI type through a validated questionnaire2. The website
uses NERIS Type Explorer®assessment to discern users’
personalities. The website uses extra letters A - Assertive and
T - Turbulent type factors added to MBTI type as an identity
aspect showing how confident the user is in his/her abilities
and decisions. Since four type factors were the essentials of
MBTI the last factor was not used. The second step involved
student participation in the course, where students were invited
to participate in the previously described course. The third
step involved the organization, treatment, and analysis of data
obtained during the course.
B. Participants and data analysis
Initially, to guarantee a sample size that allows for the
accurate detection of effects, we employed the a-priori sam-
ple size calculation method (i.e., a method for determining
the minimum required sample size to perform some type
of analysis) [34]. We used the Online Calculator for A-
priori Sample Size Calculator for SEM proposed by Soper
[35]. Due to the absence, to the best of our knowledge,
of comparable experiments, we aimed to detect a range of
effect sizes spanning from low to large. Thus, to set the
correct number of participants for the study, following the
recommendations of Cohen [34] and Westland [36] we used
the following parameters: anticipated effect size: 0.5; desired
statistical power level: 0.8; and probability level: 0.05. Based
on our study, we have eight latent variables and 22 observed
variables. The result indicated a minimum sample size of 44
participants to detect effects.
We used data obtained from 96 undergraduate students,
comprising 75 students from the 1st course, 9 from the 2nd
course, 2 from the 3rd course, and 9 from the 4th course
within the Computer Science and Engineering Faulty. The
participants’ age range averaged between 17 and 22 years.
The dependent variables (i.e., students’ MBTI) were coded
as Extraversion (-1) Introversion (1); Sensing (-1) Intuition
(1); Thinking (-1) Feeling (1); and Judging (-1) Perceiving
(1). The following data (independent variables) were collected
from Moodle LMS as Total Activity Log Files (the complete
dataset is available as an appendix):
•Activity out of the course: Any activity done by the
student in the course, carried out outside the regular
course period (independent variable).
•Most active week: The number of students’ activities
during the course was divided by weeks according to
week dates in the Fall 2021 semester. The most active
week number represents the week number with the high-
est number of activities (considering the 15 weeks of the
course).
•Most active day: Most active day was obtained by
detecting the biggest number of activities done by a
student in each week (considering the weekdays Monday
as 1 and Sunday as 7).
2www.16personalities.com
•Total activities performed: Number of activities carried
out throughout the course.
The data analysis was carried out using PLS-SEM, which in
the realm of exploratory research stands as a well-established
approach for theory development [37]. This class of SEM
elucidates the variance in dependent variables by accom-
modating unobservable variables measured indirectly through
indicator variables. Furthermore, PLS-SEM provides robust
model estimation, even when dealing with relatively smaller
sample sizes [38].
IV. RES ULT S
PLS-SEM provides a form of analysis that remains robust
regardless of the data distribution, eliminating the need for
normality tests [39]. Therefore, our initial focus involved
calculating the discriminant validity (i.e., to ensure that differ-
ent measurements truly reflect separate concepts rather than
being too closely related) of the scale. Table I presents the
discriminant validity results for the scale used in our study.
TABLE I
DISCRIMINANT VALIDITY
AOC I-E S-N J-P MAW MAD T-F
I-E 0.075
S-N 0.014 0.007
J-P 0.079 0.067 0.215
MAW 0.107 0.214 0.069 0.179
MAD 0.267 0.328 0.186 0.185 0.24
T-F 0.152 0.157 0.366 0.248 0.119 0.231
TAP 0.258 0.127 0.026 0.189 0.073 0.442 0.205
Key: I: introversion; E: extroversion; S: sensing; N: intuition ; J: judging;
P: perceiving; T: thinking; F: feeling; AOC: activity out of the course;
TAP: total activities performed; MAW: most active week; MAD: most
active day
Next, Table II displays the path model and Table III presents
the results of a statistical analysis measuring the goodness of fit
of four different independent variables in predicting a depen-
dent variable (i.e., indicating the proportion of variance in the
dependent variable that can be explained by the independent
variables), as represented by their respective R2values.
TABLE II
CORRELATIONAL MATRIX
CI
βSD P-values Bias 2.50% 97.50%
Extroversion →Activity out of the course -0.038 0.116 0.743 0.003 -0.256 0.194
Extroversion →Most active week 0.225 0.109 0.039 0.002 0.009 0.429
Extroversion →Most activity day -0.463 0.319 0.147 0.164 -0.68 0.325
Extroversion →Total activities performaned -0.070 0.112 0.532 -0.001 -0.288 0.155
Intuition →Activity out of the course -0.062 0.112 0.580 -0.003 -0.282 0.168
Intuition →Most active week 0.143 0.113 0.204 -0.001 -0.091 0.358
Intuition →Most activity day 0.040 0.195 0.836 -0.009 -0.394 0.403
Intuition →Total activities performaned -0.078 0.106 0.461 -0.001 -0.282 0.137
Judging →Activity out of the course -0.116 0.118 0.327 -0.008 -0.347 0.118
Judging →Most active week -0.211 0.110 0.054 0.000 -0.413 0.015
Judging →Most activity day 0.097 0.228 0.671 -0.097 -0.334 0.479
Judging →Total activities performaned -0.248 0.104 0.017 0.000 -0.435 -0.025
Thinking →Activity out of the course 0.197 0.107 0.065 -0.001 -0.024 0.399
Thinking →Most active week -0.088 0.115 0.446 -0.002 -0.311 0.135
Thinking →Most activity day -0.087 0.264 0.741 0.088 -0.564 0.401
Thinking →Total activities performaned 0.283 0.116 0.015 0.001 0.023 0.486
Key:β: Regression Coefficient; SD: standard deviation; CI: Confidence interval.
Extroversion was positively associated with the most active
week (β= 0.225 |P= 0.039), Judging negatively associated
with total activities performed (β= -0.248 |p= 0.017),
and Thinking positively associated with total activities (β=

TABLE III
R2RE SULT S
R2Adjusted R2
Activity out of the course 0.042 -0.001
Most active week 0.102 0.062
Most activity day 0.187 0.151
Total activities performed 0.113 0.074
0.283 |p= 0.015). These associations were small in terms
of R2, suggesting they explain only a small proportion of
the variance in activity levels. Also, the confidence intervals
can be considered high, indicating a possible high variation
in the real value of β. Furthermore, extroverted participants
participated in the course for a greater number of weeks, while
judging and thinking participants performed more activities
during the course. In summary, personality traits showed weak
but significant relationships with course activity levels.
A. Discussion
In this study, aiming to advance toward personalized educa-
tion, we analyzed the relationship between students’ MBTI and
their behavior in an educational system. Firstly, the observation
that extroverted participants exhibited heightened activity over
more weeks suggests a compelling link between extraversion
and sustained engagement in online learning environments.
This finding aligns with the notion that extroverts, who thrive
on social interactions [22], [23], [30], may find the collab-
orative and communicative features of the Moodle platform
conducive to their learning preferences.
Our findings surpass those of comparable recent studies
[31], [32], [40], revealing that participants with judging and
thinking orientations exhibited greater activity levels during
the course (see Table II). This observation prompts questions
regarding the decision-making processes and task-oriented
behaviors of individuals within an academic context. It can
imply that students with judging and thinking orientations may
be drawn to structured learning environments (similar to our
case), engaging in a higher number of analytical and objective
learning activities.
Identifying the relationships between personality and en-
gagement patterns guides the development of personalized
and adaptive learning experiences. For instance, understand-
ing extroverted learners’ preferences suggests incorporating
collaborative and interactive elements into course design.
Similarly, acknowledging judging and thinking participants’
engagement emphasizes the importance of creating structured
and goal-oriented tasks that cater to their decision-making and
analytical preferences.
B. Threats to validity and limitations
Our study has some limitations that should be considered
in interpreting the result. Concerns have been raised about the
scientific rigor of the MBTI due to its theoretical underpin-
nings [41], [42]. However, in this study, we opted for the MBTI
given its extensive research base and practical applications
in education and psychology. Nevertheless, to address these
concerns, we employed a well-established MBTI questionnaire
with strong psychometric properties. Another limitation is the
study’s homogeneity, as all participants were from the same
country, same university, and same faculty. This restricts the
generalizability of our findings to broader populations. Also,
it might affect the behaviour of students within the Learning
Management System. The sample size, although sufficient for
a case study, may not allow the generalization of results to
other contexts. Finally, the nature of the study can generate
a series of biases related to student behavior while using the
system.
C. Recommendations for future studies
The present study’s findings offer insights that future studies
can leverage to enrich online learning environments, making
them more effective and inclusive. The variations in student
behavior linked to MBTI personality types emphasize the
significance of incorporating personalized learning approaches
into learning platforms. Educators and instructional design-
ers can enhance the educational experience by person-
alizing course content, assessments, and communication
strategies to align with the cognitive preferences of diverse
personality types.
While the current study focused on MBTI personality types,
it is important for future research to explore additional
user models influencing student behavior within LMS
platforms. Factors like digital literacy levels and motivation
are crucial in shaping how students interact with online
resources. Integrating multiple user models can contribute to
a more comprehensive understanding of the intricate interplay
between individual characteristics and learning behavior.
The study serves as a valuable starting point for elucidating
the relationship between MBTI personality types and Moodle
LMS usage in this specific context. However, to ensure robust
statistical significance and generalizable findings, future re-
search should strive for larger and more diverse samples.
Expanding the sample size can empower researchers to un-
cover subtle patterns and trends that might be overlooked in
smaller cohorts.
V. CONCLUDING RE MA RK S
Despite the acknowledged limitations of the MBTI, it
remains extensively employed in practice for understanding
and modeling user behavior within educational systems. For
this reason, in this particular case study, we investigated the
relationship between students’ MBTI profiles and their behav-
ior within an educational system. Our findings suggest that
distinct behaviors may manifest among students based on their
MBTI profiles, prompting avenues for further investigation.
In future work, we aim to replicate the study with a more
expansive sample size and explore relationships between user
behavior and other types of user models.
APPENDIX
The study dataset can be accessed from this link: https:
//osf.io/t7y5a/.

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