=Paper=
{{Paper
|id=Vol-2478/paper14
|storemode=property
|title=Investigation of Temperament Characteristics Influencing the Academic Achievement of First-year University Students
|pdfUrl=https://ceur-ws.org/Vol-2478/paper14.pdf
|volume=Vol-2478
|authors=Elena Shadrina,Olga Oshmarina,Marianna Korenkova,Galina Zalesskaya
}}
==Investigation of Temperament Characteristics Influencing the Academic Achievement of First-year University Students==
https://ceur-ws.org/Vol-2478/paper14.pdf
Investigation of Temperament Characteristics
Influencing the Academic Achievement of First-year
University Students*
Elena V. Shadrina[0000-0001-5663-529X], Olga E. Oshmarina[0000-0002-9403-9285],
Marianna M. Korenkova[0000-0002-1611-6708], Galina M. Zalesskaya
National Research University Higher School of Economics HSE branch in
Nizhny Novgorod, Nizhny Novgorod, Russia
eshadrina@hse.ru, oshmarina@hse.ru, mkorenkova@hse.ru,
zalagal@gmail.com
Abstract. The article examines the influence of human temperament on
academic performance and predictions of "risky" students. Analysis was held
with the help of statistics methods and methods of data mining. The baseline data
for the study is information about students, collected using the online support
system for the educational process at HSE - LMS (Learning Management
System). The study found a relationship between temperament and academic
success, making it possible to predict "risky" students. The result of the study
was the recommendations of the education office to draw the attention of "risky"
students, to carry out preventive measures: the organization of electives, the
assignment of a curator, a check of the student's readiness for classes.
Keywords: Psychology of higher education, academic achievements, human
temperament, data mining, decision tree, on-line questionnaire.
1. Introduction
Students' performance is the main parameter, on the basis of which it
is possible to evaluate the clarity of knowledge and how well it is understood.
If there is an opportunity for early prediction of examination results, preventive
measures can be taken: electives and additional course consultations may be
arranged in order to lower the number of students with unsatisfactory results or
academic failures and dropouts. In small groups inclinations of students can be
determined by a teacher during classes due to personal contact. But if the
*
Copyright ©c 2019 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0)
number of students rises, it becomes more difficult to monitor those who will
be likely to fail the examination.
The Faculty of Informatics, Mathematics, and Computer Science (HSE
Nizhny Novgorod) grows and accepts more and more first-year students with
different motivation level, natural aptitude, perseverance, and other personal
features that can give them an advantage or create obstacles on the way to the
academic success. Despite the fact that students come who enter the faculty
have a high score on the Unified State Exam (school leaving tests), the
percentage of those who are expelled after the first year is very high - about
30%.
Since there is high competition among Universities for students, they
(students) have become each University's essential resource. Higher education
institutions must take into account students’ needs more than ever nowadays
and do their best to meet these needs. Consequently, some major goals for a
University today are: to find efficient ways of attracting students; to hold their
interest; to strike a balance between academic requirements and catering for
students’ needs and interests; how to keep students without reducing the quality
of their studies. It is of equal importance to be able to understand who are the
successful students, what they are interested in, and what their intentions and
ambitions are. Therefore, the problem of predicting students' performance on
the basis of their personal features becomes increasingly burning.
During the last 20 years different authors conducted studies detecting
factors, which affect the academic performance most of all. Particularly, works
of Aruselvan, Campbell, Pal describe all-round studies on this subject [1, 2, 3].
Superby with co-authors conducted a large-scale study at a Belgium university
detecting the most influential factors [4]. A newer study was conducted by Lust
and co-authors at Belgium and French universities with the help of advanced
methods of data mining and machine learning [5]. Finally, a similar study was
conducted at the Higher School of Economics in 2014 [6]. All these studies
show that factors connected to academic behaviour (attendance, keeping notes,
confidence in selection of a university and profession, doing homework,
attending electives), as well as personal history factors (parents' education,
presence of both parents in the family, school grades) are of the most
importance. They also demonstrate that character features are also important,
but not so significantly as factors specified. In his turn, Poldin with co-authors
in his article explains that sociable students by means of communication
develop personally, and therefore become more successful [7]. The work of
Valeeva detects a relationship proving that in the course of time social isolation
of students with academic failures takes place, creating additional risks of being
expelled from higher education institutions for them [8].
In the works mentioned above the effect of friendship ties on student
performance is noted, so the idea emerged to look more closely at the
psychological aspects of students' personality.
Jung divided all individuals on the basis of reasons for psyche activity, which
can be external or internal, into extroverts and introverts [9]. This division has
become the basis for a modern understanding of temperament. In this work we
will base on the typology of four temperaments, which logically follows from
it:
• the choleric temperament is characterized with intensity and power of
emotional processes. Choleric people are quick-tempered, passionate
and energetic.
• a sanguine individual is distinguished by comparatively weak intensity
of psyche processes with a quick change of certain processes with
other. Sanguine people are cheerful, hard-working, they easily cope
with various tasks.
• a phlegmatic person is distinguished by slowness, sluggish movements,
lack of energy. Feelings of a phlegmatic person are even and quiet.
Phlegmatic people are devoted persons and it is difficult for them to
switch into new activity types.
• the melancholic temperament is characterized with depth of emotional
expressions, but slow flow of psyche processes. Feelings and emotions
of a melancholic person are usually uniform, such people are sensitive
to external circumstances and often prove themselves to be passive and
sluggish [10].
The temperament shows the equability of mind, emotional disposition,
perseverance of an individual and therefore determines such personality
processes as performance, ability to fulfill a task assigned, to deal with stresses
and problems. For this reason definition of students' temperament may be
helpful in predicting and preventing a number of possible difficulties occurring
in the course of study. Moreover, this will help to produce methods of work
excluding these difficulties taking into account strengths of the temperament of
each student in terms of individualized approach.
This study will define a type of personality, level of person's activeness,
rationality, extroversion, ability to set study priorities of the first and second
year students of the Faculty of Informatics, Mathematics, and Computer
Science to see whether every student in prone to have academic failures.
This work is aimed at studying the influence of personal features (i.e.
temperament as a personality basis) on academic performance and detection of
students who will highly likely fail their examination. It is not rational to wait
until April to guide students who really need to be supported. We want to
establish the statistical model which will help us to predict academic success or
failure as soon as possible to provide necessary support.
Our main hypothesis is that there is a relationship between temperament
and student's academic performance. The information about personal features
of students received by means of questioning via Google online form was used
as a material for the study. First- and second-year students of the Faculty of
Informatics, Mathematics and Computer Science of academic year 2017-2018
were the study subject. Their average grade and its dependence on
temperament, extroversion level, stability and other personal features were the
study object.
2. Data
Our main objective is to classify students into three groups: Good - ‘low-risk’
students, with a high probability of succeeding; Medium - ‘medium-risk’
students, who may succeed due to the measures taken by the university; and
Bad - ‘high-risk’ students, who have a high probability of failing (or dropping
out). Thus, we need to create a data pool in which every student is described
according to a range of personal characteristics.
In order to obtain the data on the temperament and some other specific
characteristics of students we arranged for an online interview using Google
questionnaire forms. It was distributed at the middle of the academic year 2017-
2018. Altogether 90 first-year and 50 second-year students of the Faculty of
Informatics, Mathematics, and Computer Science took part in it.
The questionnaire was made in order to describe each student with a
certain number of criteria. The interview included questions providing the
information on activeness at school, perseverance, and ability to set priorities.
This information describes personality traits of a student and gives additional
grounds for more accurate study. The temperament defining block includes 12
questions, each describing one of the temperaments: choleric, sanguine,
phlegmatic, or melancholic. Then using student's answers we calculated such
parameters as extroversion and rationality (Figure 1) and defined the
temperament based on these traits. It should be noted that the term "rationality"
hereinafter shall be used to mean "stability", i.e. smooth development of
processes, changing of feelings and mood. Besides, the information on with
whom students live (alone, with parents, with mates or in a dormitory) was also
collected as this may, to different extent, have an impact on academic
performance depending on student's personality traits.
Fig 1. Temperament classification on the basis of extroversion and rationality
Final questionnaire comprised 17 closed questions. The information about
intermediate academic success after the first half of the academic year from the
internal University database has also been collected. So after merging the
information we extracted 14 binary variables for each student and one in
percentage rate. The decision variable used for the validation of our model is a
variable of the final year rate, built a posteriori, grouping students according to
their academic performance.
Discussing the questionnaire results, allocation of temperaments in the
sample is shown in Figure 2. Melancholics (27 persons) and sanguines (26)
represent separately approximately one-fourth of the interviewed. The number
of cholerics (34) three times exceeds the number of phlegmatics (13).
Hereinafter, for illustration purposes, red colour will be used for cholerics,
yellow — for sanguines, green — for phlegmatics, and blue — for
melancholics.
Fig 2. Allocation of types of temperament
We used a grade average and the information on retaking of
examinations from the general rating of the Faculty of Informatics,
Mathematics and Computer Science for assess the academic performance.
As the number of interviewed first-year students was twice as much as the
number of the second-year students we decided to make a training sample from
the total number of the second-year students and a half of the first-year students,
100 persons in total. A test set that will be used for checking the model
performance includes 40 students of the first-year groups.
Training sample students was divided into Good, Medium and Bad
categories based on their grade average and the occurrence of retaken
examinations, with each categories representing low, medium and high
probability to fail any of exams (see Figure 3). The HSE academic year consists
of 4 modules, with exams at the end of each and a 10-point grading system.
After summer and winter sessions, the education office ranks the students of
the faculty according to the average grade from the highest score (the most
successful students) to the lowest (unsuccessful students). In our study the
category of Good includes students of the first 33% of rating without retaken
exams, the Medium category includes next 33% without retaken exams, and the
Bad category includes all the rest students (see Figure 3a, Figure 3b).
Fig 3. Breakdown of students by categories
Fig 3a. Categorization of temperament(in number). Fig 3b. Categorization of temperament(in
%)
Fig 3c. Categorization of temperaments using characteristics of Exam success/Grade average
Such surprisingly interesting breakdown data are explained by the fact
that almost each second student of the interviewed had a retaken examination
in a term. This is typical for the Faculty of Informatics, Mathematics and
Computer Science because of a number of disciplines studied at the beginning
of the first and second years of education that are rather difficult to be passed
successfully. It is worth noting that the allocation to category Bad does
not mean that the student will not pass three or more exams and is on the
verge of expulsion. From the graph (see Figure 3b), it can be seen that at
the stage of initial analysis, both general tendencies and differences
between temperaments can be traced. The pattern of categorization in
choleric and phlegmatic students is similar (mostly Bad, then Medium,
and then Good), while half of sanguine persons belong to category
Medium. For melancholic people, the largest number of students falls
into category Bad.
It should be noted that allocation to the bad category does not indicate
that a student fails all the examinations and is verging towards expulsion. Many
of the students assigned thereto have a high grade average and one failed exam.
Since the aim of this study is to define all the students who are expected to have
failures, therefore, even students with good progress and some academic failure
may be categorized as Bad.
3. Methods
The task of dividing students into the categories of high, medium and low
probability of having academic failures is a task of classification based on
supervised learning [11].
At the first stage of work, we have to select these characteristics that
significantly influence the grade average and retaken examinations.
At the second stage of work, we are to develop the best algorithm of
classification of students from the training sample (with the greatest number of
guessed Bad category students). In other words, such method should enable us
to assign each next student to one of the predetermined classes subject to their
formalized characteristics. For this purpose we used such simplest methods of
computer-assisted learning as k-Nearest Neighbors algorithm [12] and a
decision tree [13, 4]. As we did not know beforehand which method would give
the most exact result, we tested both of them and to tune the parameters for the
maximum guessing.
At the third stage of work, the best model will be determined and used for
predictions for the test group.
3.1. Correlation Calculation
We represented the data obtained in a convenient form for subsequent
analysis and interpretation. Students' answers were translated into Boolean
variables: 1 was used if a student agreed with a statement and 0 if otherwise.
First of all, we selected from all the characteristics those which produce the
most impact on the grade average and retaken examinations using the
correlation factor. The correlation factor enables to evaluate the dependence
between two or more values and is quite successfully used in education
sociology and data mining [10]. To calculate the correlation, we used the
function cor(), the "pearson" method, of the RStudio software.
Calculation results are shown in table 1. We assume that the value of the
correlation coefficient is significant for the purpose of our investigation if its
absolute value is more than 0.2 and insignificant, if it is between 0 and 0.2.
Table 1. Dependence between academic performance and temperament
Characteristic Grade average Retaken
correlation examination
correlation
Activeness at school 0.06 0.09
Perseverance 0.19 -0.24
Setting of priorities 0.29 -0.18
Living in a dormitory 0.06 -0.05
Living with mates -0.01 0.01
Living with parents -0.01 0.02
Living alone -0.04 0
Extroversion 0.07 -0.18
Rationality -0.004 -0.08
Choleric -0.1 0.044
Sanguine 0.05 -0.16
Phlegmatic 0.13 0.017
Melancholic -0.04 0.2
As we can see, the characteristics of “perseverance” and “setting of
priorities” have a significant dependence on academic performance, which
seems obvious, while temperament and the resulting psychological
characteristics are less correlated with the studying success. But since the
research is based on these characteristics, they were highlighted in bold, if their
absolute value of correlation with at least one of the signs is greater than 0.1.
So, the dependence between academic performance and temperament
does exist, and it is rather significant in some cases. For example, cholerics
have a lower grade average (cor = -0.1) while perseverance of phlegmatics
provides for higher marks (cor = 0.13). Sanguine re-take examinations rarely,
and for melancholics the probability of retaking exams is considerable (cor =
0.2).
The obtained results show that there is dependence between the academic
performance and temperament.
3.3. Correlation Calculation Comparison of kNN and Decision
Tree algorithms
kNN evaluation function demonstrated as follows.
Three series of calculations were carried out:
• using all characteristics with high correlation and a grade average. The
best result was achieved with 8 nearest neighbors (84%).
• all characteristics without grade average. The best result was shown
with 5 neighbors (60%). This is a good indication as probability of
allocation to a required category is twice as higher as random guessing
of category (which is 33%)
• using only grade average. The best result was obtained with 4
neighbors and was 77% of guessing. This is well-reasoned because a
student with a low grade average is more likely to be assigned to the
bad students category and, accordingly, more neighbors of such student
with a low grade average will be assigned to Bad.
So, as we can see it better not to use psychological characteristics when
applying kNN method for classification since they impair calculation accuracy
as compared with the case of using only grade average.
Using of decision tree demonstrated as follows:
• the accuracy was 74% when all the characteristics were used for
formation
• when grade average was excluded the tree was based on Extroversion
and Rationality characteristics and the accuracy was 62%
• the tree based only on the grade average provided the accuracy of 76%
• the best result was obtained when the tree was generated basing on
Perseverance, Setting of Priorities, Extroversion, Rationality and
temperament type and achieved 84%
Fig 4. Decision tree that predicts the categories of the students
The accuracy of answers of both models was about 75% but the better result
was shown by the decision tree model. Therefore we used this method with
these parameters to analyse a test set. Figure 4 shows the decision tree that
predicts the categories of the students.
4. Results
The students of the first-year groups were classified using the decision
tree generated at the previous step. According to this classification students
were divided into three groups: Bad, Medium and Good. In order not to reveal
personal information, the last name of each student was replaced with the
symbol Student 1, Student 2, .., Student N. The distribution of the students in
the three categories is the following: 22 students were listed in the Bad category
(the category of risky students), 12 students - in the Medium category, 6
students - in the Good category. After the final session in the 4th module and
all the retaken exams, it became possible to check the results of the research.
Table 2 provides summarized data of the research results.
Table 2 Summarized data
Predicted Total Students Dropout high average
category number retaking exams students or score (more than
of students with 7.5)
students ILP2
people % people % people %
Bad 22 14 64% 6 27% 0 0%
Medium 12 3 25% 1 8% 4 33%
Good 6 0 0% 0 0% 6 100%
The category of Bad has 22 students, 14 students (64%) – retook
exams, 2 of 6 students (27%) were expelled from the University and 4 stayed
on the basis of ILP due to academic debts on one or two subjects.
The category of Medium accounts 12 students, 3 students (25%) retook
exams, and were transferred to the second year of studies without any academic
2
ILP – Individual learning plan – is a plan of repeating a failed discipline or a
number of disciplines for a certain fee.
debts. One student from the Medium category left the University as she made
a decision to change her career aspiration.
There was no exam retaking among the students of Good category (6 people),
all the students from this category continued to study at the University.
Thus, we can conclude that the result of the distribution of students into
categories is in agreement with the real situation. Attention also should be paid
to the average score of students: there are more students with a high average
score in the Good category, fewer in the Medium category and there are no such
students in the Bad category.
Temperament determination also opens new opportunities for
individual work with a student. For example, in order to get the best
performance of a phlegmatic it is better not to switch from one task to another
too often, as a phlegmatic is able to work at one task effectively and for a long
time, while switching between tasks is difficult for such person. At the same
time new, interesting tasks should be always assigned to a sanguine. In case of
a melancholic severe criticism, rudeness or irony are inappropriate, and a
negative estimate should be softened or given very carefully. A choleric should
always compete and get challenges, however, it is also necessary to sometimes
encourage them and help to overcome difficulties, as cholerics prone to quit a
matter without seeing it through.
As a result of the study, the following recommendations were given to
the educational office of the Faculty of Informatics, Mathematics and Computer
Science HSE - Nizhny Novgorod:
• pay close attention to the students from category Bad (e.g. conversation
with students or as a last resort their legal representatives);
• provide availability of additional classes in the form of elective for the
students of categories Bad and Medium;
• engage training assistants into helping students with understanding and
fulfilling home assignments;
• to pay additional attention to students from the category Medium with
a low average score after the first half of the academic year.
5. Conclusion
In this paper, the influence of temperament on the academic success of
students in the University HSE in Nizhny Novgorod was studied. In the 2017-
2018 academic year we conducted a survey among students of the first and
second year of study. This study defined the most important psycho factors
affecting students' academic performance: "Perseverance" and "Ability to set
priorities": their presence dramatically raised the average grade and decreased
the probability of retaking exams. As a rule, hot-tempered cholerics have a
lower average grade, while quietness and steadiness of phlegmatics helps them
to study better. Sanguines retake exams more rarely, while the risk of it for
melancholics is significant. The more extroversion is expressed in a student, the
higher his or her average grade is.
As a result, using the decision tree method, a statistical model was built.
The model distributes students into three notional categories, showing low,
medium and high risk of retaking exams: Good, Medium, Bad. The model
showed a generally good percentage of prediction: 64% of guessed students
retaking exams.
Thus, we have found a connection between temperament and academic success,
making it possible to predict "risky" students.
We believe that our research might be useful to other universities for:
1. identifying academically unsuccessful students and focusing on "risky"
students. One of the most important goals of the Faculty of Informatics,
Mathematics, and Computer Science (HSE Nizhny Novgorod) is to train
and transfer without academic debt to the 2nd as many first-year students
as were accepted for the program. Otherwise, the resources of the state (in
the case of budget education) or student personal funds (in the case of paid
education) spent on education will not be used rationally.
2. forming an individual educational trajectory. At HSE-Nizhny Novgorod
today, there are flexible opportunities for switching from one educational
program to another using ILP. We assume that forming studying groups,
taking into account students personal characteristics’ will increase the
performance of each student. In the Faculty of Informatics, Mathematics,
and Computer Science (HSE Nizhny Novgorod) in the 2018-2019
academic year several training groups were formed according to the level
of students’ knowledge in the discipline of Programming based on the test
results. Those with higher results were put together in one group, medium
studied with medium, and there was also a group of weaker students.
Training becomes more effective if you teach discipline by level: by
loading strong students with complex tasks (without losing their
motivation to study) and “bringing” the weak and medium ones to the level
of strong ones.
Our colleagues from the Linguistic Department (HSE Nizhny Novgorod) have
been teaching foreign languages by levels for several years already: the total
pool of 1-year students, regardless of the educational program, is divided into
levels such as A (Upper Intermediate) B (Intermediate) C (Pre-Intermediate),
which improves the quality of teaching a foreign language for each student.
We understand that there are certain limitations to our study. In this
paper, a small pool of baseline data is presented (140 student responses). And
the Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny
Novgorod) is not large enough to talk about “real” Big Data and use all the
opportunities of machine learning methods. In the future, we plan to use the
longitude data for results verification over several years. It would also be
interesting to try other data mining methods to predict academic failures and
dropout.
We would like to thank the education office specialists of HSE
University Nizhny Novgorod for providing data for conducting the research.
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