=Paper=
{{Paper
|id=Vol-3171/paper95
|storemode=property
|title=Innovative Methods of Assessing the Academic Success of Students in Higher Education Institutions
|pdfUrl=https://ceur-ws.org/Vol-3171/paper95.pdf
|volume=Vol-3171
|authors=Rostyslav Yurynets,Zoryna Yurynets,Nataliia Danylevych
|dblpUrl=https://dblp.org/rec/conf/colins/YurynetsYD22
}}
==Innovative Methods of Assessing the Academic Success of Students in Higher Education Institutions==
https://ceur-ws.org/Vol-3171/paper95.pdf
Innovative Methods of Assessing the Academic Success of
Students in Higher Education Institutions
Rostyslav Yurynets 1, Zoryna Yurynets 2, Nataliia Danylevych 2
1
Lviv Polytechnic National University, Lviv, Ukraine
2
Ivan Franko Lviv National University, Lviv, Ukraine
Abstract
In the article use innovative possibilities of assessing and predicting the academic success of
students in higher education institutions are investigated. The proposed multinomial logistic
regression model allows to assess and predict the success of students based on the obtained
estimates and taking into account various factors. The multinomial logistic regression model
is one of the best tools for determining the quality of training for students, proceeding from the
quantitative (intellectual) and qualitative (behavioral) factors. The following factors were used
in the study: an assessment of the student's presence in the classroom; student's grade in
mathematics; student's grade in computer science; assessment of the student's living conditions.
The use of regression analysis methods allows identifying implicit relationships between
elements of the educational process and solve many problems related to the analysis and
prediction of academic success of students. A multinomial logistic regression model confirms
that assessment of the student's presence in the classroom; student's grade in mathematics;
student's grade in computer science; assessment of the student's living conditions are important
indicators that reflect the academic success of students in higher education institutions. It is
possible to predict individual learning outcomes, evaluate student actions, and assess factors
related to success, create an individual learning plan through the constructed model. This
knowledge is significant for university administration and teachers who want to improve the
quality of teaching and ensure better learning outcomes.
Keywords 1
Academic Success, Students, Higher Education Institutions, Teachers, Multinomial Logistic
Regression Model, Factors
1. Introduction
Educational data mining plays an important role in the development of the learning environment.
Modern higher education institutions operate in a highly competitive and complex environment. The
main problems faced by higher education institutions are: analysis of learning outcomes, quality
education, evaluation of student performance, strategic decision-making, and formation of human
capital at all. Universities need to implement student intervention plans to address the challenges that
students face during their studies.
Different methods of machine learning are used to understand and overcome the main problems.
Various innovative methods and methods of machine learning are used to understand and overcome the
main issues. Modern development of society requires the use of new innovative methods of assessing
students' educational levels in higher education institutions, evaluating of the knowledge gained inside
the higher education, training of students in various subjects, in particular information systems in
management. These methods will allow future professionals to be more competitive in the labor market.
Innovative methods in higher education are characterized as technologies based on innovations:
organizational (related to the optimization of educational conditions), methodological (updating the
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland
EMAIL: rostyslav.v.yurynets@lpnu.ua (R. Yurynets); zoryna_yur@ukr.net (Z. Yurynets); danylevychnatali@gmail.com (N. Danylevych)
ORCID: 0000-0003-3231-8059 (R. Yurynets); 0000-0001-9027-2349 (Z. Yurynets); 0000-0001-9906-1492 (N. Danylevych)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
content of education and improving its quality); technical (related to the use of new tools of artificial
intelligence in the field of informatics).
Machine learning methods play an important role in predicting the success of students at risk and
dropout, thereby improving learning outcomes. Machine learning and statistical methods for
educational data are needed to identify significant patterns that improve students' knowledge and the
learning process in higher education institutions. Predicting student achievement at the grassroots level
and beyond helps universities develop intervention plans in which the executive heads and university
professors are beneficiaries of student performance prediction plans. Predicting academic success of
students provides excellent benefits for improving student retention rates, targeted marketing, and
overall student performance, as well as for effective enrollment management, graduate management.
The hypothesis is that in order to achieve student's academic success and qualifications in
management information systems, it is necessary that internal elements be considered as an integral part
of intellectual and behavioral factors. The proposed multinomial logistic regression model allows
university administration and teachers to assess and predict the success of students based on the obtained
estimates and taking into account various factors. The following factors were used in the study: an
assessment of the student's presence in the classroom; student's grade in mathematics; student's grade
in computer science; assessment of the student's living conditions. Assessing the academic success of
students in the discipline of information systems in management is the ultimate task.
The model also allows predicting the individual learning outcomes, to evaluate student actions and
differences between actions, to assess factors related to success, to create an individual learning plan
through the constructed model. The theoretical and methodological basis of the paper is the scientific
and research works of domestic and foreign academicians, which reveal the issues of educational
management, modeling student performance.
The survey rests on the following general and specific methods: a systematic approach to the study
of machine learning methods to improve the quality of the educational process in higher education
institutions; a deductive method to solve the problem of systematization of the factors that affects the
academic success of students; multinomial logistic regression analysis as a machine learning method to
solve the problem of forming the model of the influence of factors on the assessing the academic success
of students and get an information about the level and quality of the learning in higher education
institutions; analytical method for assessing the state of academic success of students in higher
education institutions.
2. Literature review
Analysis of the academic achievement of students at all levels of training is a prerequisite for the
effective functioning of the management system of the educational process. The process of managing
human capital preparation involves a wide range of interdependent and complementary components.
An important component is the analysis of the organization of the educational process and, foremost,
the process of forming the academic success of students.
The relevance of methods of regression data analysis in educational systems is confirmed by a
number of studies of learning processes.
Kovacic [1] analyzed the results of predicting student performance through machine learning
methods. Demographics (education, employment, gender, status, disability, etc.) and course
characteristics (course syllabus, course unit, etc.) were taken into account. The data were collected at
the Open University of New Zealand. The main features influencing student success were identified
through machine learning algorithms. Three main factors influencing student performance (course
syllabus, course unit, and ethnicity) were identified. It is important to choose subjects that determine
the success of students in the course program.
Lakkaraju [2] suggested identifying failed students or students who risk not graduating on time. To
do this, he used the structure of machine learning. Using this structure, student data was collected from
two schools in two districts. The study took into account five machine learning algorithms: Support
Vector Machine (SVM), Random-Forest (RF), Logistic Regression, Adaboost and Decision Tree. These
algorithms are evaluated using accuracy, response, and AUC for binary classification. Each student is
evaluated based on a risk assessment evaluated according to the proposed classification. The author
also paid important attention to the impact of student presence and involvement in the learning process.
Teachers need to look for opportunities to strengthen presence and involvement of student with regard
to learning process in higher education institutions. The results showed that Random Forest
demonstrates the best performance.
Oyedeji and other scholars [3] have used machine learning techniques to analyze student
performance as an element of the formation of human capital. The obtained results allow educational
institutions to eliminate methods that cannot improve student performance. Their study took into
account individual attributes, including the age of the student, their demographic distribution, individual
attitude to the survey and marital status. The authors noted the need to include in the study of the
academic success of students the student's living conditions. Various machine learning algorithms were
used in the study. For a comparative analysis of productivity, three models have been identified:
learning with a teacher using linear regression and in-depth learning.
Alhusban [4] used machine learning methods to measure and increase the dropout undergraduate
students. They collected data from al-Bayt University students. The following factors were taken into
account: gender, type of enrollment, enrollment marks, city of birth, marital status, nationality and
subjects studied at stage k-12. The researchers used Hadoop and an open source platform based on
machine learning. It has been found that the marks for entrance examinations significantly affect
specialization. The researchers have been stated that students' social status has a significant impact on
academic performance. This is of great importance for the development of higher education institutions
and the maintaining human capital.
Our study took into account the ideas of scientists who emphasize the importance of the influence
of factors (course programs, student's living conditions, student's presence in the classroom) on the
academic success of students. The course program of the specialty "Management information systems"
include various subjects. In our study, important subjects such as computer science and mathematics
were selected. These subjects are the basis of the specialty "Management information systems".
3. Methodology
To assess the academic success of students, a relationship should be established between factors and
academic success. We will take the student’s academic success indicator according to three values
(good, satisfactory and unsatisfactory). Therefore, it is necessary to predict the success of students
through a model of multinomial logistic regression. Logistic regression is used to predict the probability
of an event by the value of a set of features.
Now, consider modeling assessments of students' academic success in order to predict their future
condition. This means estimating a qualitative variable (intellectual factors) by several quantitative
factors (behavioral factors). Discriminant analysis tools can be used to select the most informative
quantitative variables. Logit regression allows determining the group of students' academic success. In
addition, logistic regression makes it possible to consider the probability that a student will be classified
as a certain group success.
The probability function is the basis of the method and expresses the probability density of the
simultaneous appearance of the sample results Y1, Y2,…, Yn [5]:
L(Y1,Y2,…,Yk;Θ)=p(Y1;Θ)⋅…⋅p(Yn;Θ) (1)
According to the method of maximum likelihood, the value of Θ=Θ(Y1,…,Yn) [6], which maximizes
the function L is taken to estimate the unknown parameter.
The calculation process is simplified by maximizing not the function L, but the natural logarithm
ln(L). This is due to the fact that the maximum of both functions is achieved at the same values Θ [7]:
L*(Y;Θ)=ln(L(Y;Θ))→max (2)
All multiple choice options are numbered in random order 0, 1, 2, ..., J [8]. The probability of
occurrence of an option is described by a polynomial logit model
𝑒 𝒙𝑖𝒃𝑗 (3)
𝑃(𝑦𝑖 = 𝑗) = 𝐽
∑𝑗=0 𝑒 𝒙𝑖𝒃𝑗
where bj – unknown parameters that we will evaluate;
x1 – assessment of the students’ presence in the classroom (behavioral factor);
x2 – students’ grade in mathematics (intellectual factor);
x3 – students’ grade in computer science (intellectual factor);
x4 – assessment of the students’ living conditions (behavioral factor).
The following notations are introduced here [9]:
b = (b0, b1,…,bm)T,
xi = (1, xi1,…,xim), (4)
xib = b0 + b1xi1 + b2xi1 +…+ bmxim
To identify the model (3), usually use the rationing bJ = 0 [10]. Then
𝒙 𝒃
𝑒 𝑖 𝑗 (5)
𝑃(𝑦𝑖 = 𝑗) = 𝐽−1 𝒙 𝒃 j = 0, 2, …, J–1,
1+∑𝑗=0 𝑒 𝑖 𝑗
1 (6)
𝑃(𝑦𝑖 = 𝐽) =
1 + ∑𝐽−1
𝑗=0 𝑒
𝒙𝑖 𝒃𝑗
This is one of the features of building a polynomial logit model. According to this feature, only the
coefficients of the first J dependencies b0, b1, ... , bJ-1 are calculated, in accordance with (5) which the
first J probabilities 𝑃(𝑦𝑖 = 0), 𝑃(𝑦𝑖 = 1), ... , 𝑃(𝑦𝑖 = 𝐽 − 1). The probability of choosing the last
option 𝑃(𝑦𝑖 = 𝐽) is not calculated, but determined separately using (6).
The coefficients of the model are estimated by numerically solving the plausibility equations. To
write the plausibility equation itself (its logarithmic form) it is convenient to enter the variable dij, which
becomes 1, if the i-th observation was chosen j-th alternative among (J + 1), and 0 – otherwise. Then
for each i only one dij will be equal to 1.
Using the entered variable dij we write the logarithmic likelihood function [11, 12]
𝑛 𝐽 (7)
𝑒 𝒙𝑖𝒃𝑗
ln𝐿 = ∑ ∑ 𝑑𝑖𝑗 ln ( 𝐽 )
𝑖=1 𝑗=0
∑𝑘=0 𝑒 𝒙𝑖𝒃𝑘
Differentiating expression (7) by bj, we obtain a system of equations of maximum likelihood
𝑛 𝐽
𝜕ln𝐿 𝜕 𝑒 𝒙𝑖𝒃𝑗
= ∑ ∑ 𝑑𝑖𝑗 ln ( 𝐽 )=
𝜕𝒃𝑗 𝜕𝒃𝑗 ∑𝑘=0 𝑒 𝒙𝑖𝒃𝑘
𝑖=1 𝑗=0
𝑛 𝐽 (8)
∑𝐽𝑘=0 𝑒 𝒙𝑖𝒃𝑘 𝑒 𝒙𝑖𝒃𝑗 ∑𝐽𝑘=0 𝑒 𝒙𝑖𝒃𝑘 − 𝑒 𝒙𝑖𝒃𝑗 𝑒 𝒙𝑖𝒃𝑗 ′
= ∑ ∑ 𝑑𝑖𝑗 ( 2 ) 𝒙𝑖 =
𝑒 𝒙𝑖𝒃𝑗 𝐽
(∑𝑘=0 𝑒 𝒙𝑖𝒃𝑘 )
𝑖=1 𝑗=0
𝒙 𝒃
𝑒 𝑖 𝑗
= ∑𝑛𝑖=1 𝑑𝑖𝑗 (1 − ) 𝒙′𝑖 = 0, j = 0, 1, 2, …, J–1.
∑𝐽𝑘=0 𝑒 𝒙𝑖 𝒃𝑘
The solution of this system, given that bj = 0, is carried out numerically using the Newton-Rafson
method [13]. The computer implementation is arranged as follows: the values of the model that
corresponds to the last of these alternatives become zero. In other words, if we want b0 = 0, instead of
bj, then the data corresponding to the alternative number j = 0, must be entered last.
The Newton-Rafson method usually requires several iterations [14]:
−1 (9)
𝜕 ln 𝐿(𝒃𝑡 ) ∂2 ln 𝐿(𝒃𝑡 )
𝒃𝑡+1 = 𝒃𝑡 − [ ]
𝜕𝒃 ∂𝒃 ∂𝐛′
To implement the Newton-Rafson method requires a matrix of partial derivatives of the second order
[14]:
𝑛
𝜕 2 ln𝐿 𝜕 𝑒 𝒙𝑖 𝒃𝑗
= ∑ (𝑑 𝑖𝑗 − ) 𝒙′𝑖 =
𝜕𝒃𝑗 𝜕𝒃′𝑖 𝜕𝒃′𝑖 ∑𝐽𝑘=0 𝑒 𝒙𝑖𝒃𝑘
𝑖=1
𝑛 𝐽 (10)
𝑒 𝒙𝑖𝒃𝑗 ∑𝑘=0 𝑒 𝒙𝑖𝒃𝑘 − 𝑒 𝒙𝑖𝒃𝑗 𝑒 𝒙𝑖𝒃𝑗 ′
=– ∑ ( 2 ) 𝒙𝑖 𝒙𝑖 =
𝐽 𝒙𝑖 𝒃𝑘 )
𝑖=0 ( ∑𝑘=0 𝑒
𝑛 𝒙𝑖 𝒃𝑗
𝑒 𝑒 𝒙𝑖𝒃𝑗
=– ∑ [ 𝐽 (𝑰(𝑗 = 1) − 𝐽 )] 𝒙′𝑖 𝒙𝑖
∑𝑘=0 𝑒 𝒙 𝑖 𝒃 𝑘 ∑𝑘=0 𝑒 𝒙 𝑖 𝒃 𝑘
𝑖=0
In the obtained expression, I(j = l) becomes 1 when j = l and 0 in other cases.
4. Empirical results
The main data for the statistical analysis of the educational process are data on the success of
undergraduate students at the economic faculty of Ivan Franko National University of Lviv (Table 1),
where
x1 – assessment of the students’ presence in the classroom;
x2 – students’ grade in mathematics;
x3 – students’ grade in computer science;
x4 – assessment of the students’ living conditions.
Table 1
Data table for assessing academic success of students
Success in
Students’ Grade in Students’
information Grade in
№ presence in computer living
systems in the mathematics
the classroom science conditions
management
7 1 8 7 7 2
8 0 8 6 6 2
9 2 10 7 8 3
10 0 8 7 6 1
11 1 7 8 7 2
12 1 7 8 6 3
13 0 8 6 7 3
14 2 10 8 8 2
15 0 7 6 6 3
16 1 7 7 6 3
17 2 10 7 7 3
18 1 8 8 7 2
19 1 8 6 8 2
20 0 8 5 8 2
Analysis of the data showed the presence of multicollinearity. Therefore, it is advisable to use the
method of principal components. The main idea of the method is to replace highly correlated variables
with a set of new variables between which there is no correlation. The new variables are linear
combinations of the original variables
𝒛=𝒙∙𝒂 (11)
First, we need to normalize all the explanatory variables:
∗
𝑥𝑖𝑗 − 𝑥̅𝑗 (12)
𝑥𝑖𝑗 = , 𝑖 = 1,2, … , 𝑛; 𝑗 = 1,2, … , 𝑚
𝜎𝑥𝑗
Next, we need to calculate the correlation matrix
1 (13)
𝑟 = (𝑋 ∗𝑇 𝑋 ∗ )
𝑛
Then we need to find the characteristic numbers of the matrix r from the equation
|𝑟 − 𝜆𝐸| = 0 (14)
at the same time
𝑚
(15)
∑ 𝜆𝑘 = 𝑚
𝑘=1
where E is a unit matrix of size m m.
The eigenvalues k (𝑘 = 1,2, … , 𝑚) are ordered by the absolute level of the contribution of each
main component to the total variance.
It is necessary to calculate the eigenvectors a k by solving the system of equations
(𝑟 − 𝜆𝐸)𝑎 = 0 (16)
Finding the main components (vectors) is determined by the formula:
𝑍𝑘 = 𝑋 ∗ ∙ 𝑎𝑘 (17)
As a result of calculations, characteristic numbers and eigenvectors have been found:
0,77 −0,01 −0,18 −0,62 (18)
−0,43 0,71 0,05 −0,56
𝑎=( )
−0,42 −0,71 0,16 −0,55
0,24 0,07 0,97 0,01
𝜆 = (0,53 0,72 1,03 1,72)
We find the connection of the dependent variable Y with the main principal components z3 and z4,
defining all principal components and rejecting those that correspond to small values of characteristic
roots. To do this, we build a model of multinomial logistic regression. The parameters of the obtained
multinomial logit model in the STATISTICA environment are as follows:
Table 2
Parameters of the multinomial logit model
y - Parameter estimates (Spreadsheet1_1)
Distribution : MULTINOMIAL Link function: LOGIT
Level of - Level of - Standard Wald -
Column Estimate p
Effect Response - Error Stat.
Intercept
0 1 -1,0001 0,074498 1,671322 0,18942
1
z3 0 2 -12,9008 9,00237 2,053606 0,151846
z4 0 3 25,0441 12,54538 2,964557 0,085108
Intercept
1 4 3,7657 2,16029 1,918160 0,173349
2
z3 1 5 -11,2274 6,94633 1,874960 0,179488
z4 1 6 19,7129 11,34079 1,809539 0,169254
Scale 1,0000 0,00000
It can be concluded that the obtained estimates of coefficients are statistically significant (all standard
errors are less than the obtained estimates, the values of Wald's statistics exceed the critical level, and
all error probabilities are less than 0,2).
Let's write down the analytical expression for the constructed multinomial logit model:
𝑒 −1+(−12,9)𝑧3 +(25,04)𝑧4
𝑃(𝑦 = 0) =
1 + 𝑒 −1+(−12,9)𝑧3 +(25,04)𝑧4 + 𝑒 3,76+(−11,2)𝑧3 +(19,7)𝑧4
𝑒 3,76+(−11,2)𝑧3 +(19,7)𝑧4
𝑃(𝑦 = 1) =
1 + 𝑒 −1+(−12,9)𝑧3 +(25,04)𝑧4 + 𝑒 3,76+(−11,2)𝑧3 +(19,7)𝑧4
1
𝑃(𝑦 = 2) =
1 + 𝑒 −1+(−12,9)𝑧3 +(25,04)𝑧4 + 𝑒 3,76+(−11,2)𝑧3 +(19,7)𝑧4
The resulting expression can be used to assess academic success of students at different values of the
factors.
In particular, figures 1 and 2 show the dependence of the studied indicator when changing the factor
x3 (students’ grade in computer science) for certain values of the factor x1 (assessment of the students’
presence in the classroom) and the fixed value of the factors x2 and x4 (x2 = 6, x4 = 3).
Figures 1 and 2 show an increase in the probability of receiving a better grade and a decrease in the
probability of obtaining a bad grade in the subject of information systems in management, given the
increasing in the students’ grade in computer science. Different values of the students’ presence in the
classroom have a great influence on the dynamics of increasing the probability of getting a better grade.
We can say that the key to measuring student achievement is assessing student progress by measuring
positive changes in the assessment of the students’ living conditions and students’ presence in the
classroom, as well as gradual changes in grade in computer science.
Assessment of information systems in
the management ("unsatisfactory")
Students’ presence in the classroom
1
0.8
0.6
0.4
x1=6
0.2
x1=5
0
x3=7 x3=8 x3=9
Grade in computer science
Figure 1: The impact of students’ grade in computer science on the assessment of information systems
in the management ("unsatisfactory")
Assessment of information systems in
Students’ presence in the classroom
the management ("satisfactory")
1
0.8
0.6
0.4
x1=6
0.2
x1=5
0
x3=7 x3=8 x3=9
Students’ grade in computer science
Figure 2: Influence of students’ grade in computer science on the assessment of information systems
in the management ("satisfactory")
Figures 3 and 4 show the dependence of the studied indicator when changing the factor x1 (students’
presence in the classroom) for certain values of the factor x2 (students’ grade in mathematics) and the
fixed value of the factors x3 and x4 (x3 = 7, x4 = 2).
Figures 3 and 4 show an increase in the probability of receiving a better grade and a decrease in the
probability of obtaining a bad grade in the subject of information systems in management, given the
increasing in the students’ presence in the classroom. Different values of the students’ grade in
mathematics have a great influence on the dynamics of increasing the probability of getting a better
grade.
We can say that the key to measuring student achievement is assessing student progress by measuring
positive changes in the assessment of the students’ presence in the classroom and students’ living
conditions, as well as gradual changes in grade in mathematics.
Assessment of information systems in
the management ("unsatisfactory")
Students’ grade in mathematics
1
0.8
0.6
0.4
0.2 x2=7
x2=6
0
x1=6 x1=7 x1=8 x1=9
Students’ presence in the classroom
Figure 3: The impact of students’ presence in the classroom on the assessment of information systems
in the management ("unsatisfactory")
Assessment of information systems in
the management ("satisfactory")
Students’ grade in mathematics
1
0.8
0.6
0.4
0.2 x2=7
x2=6
0
x1=6 x1=7 x1=8 x1=9
Students’ presence in the classroom
Figure 4: The impact of students’ presence in the classroom on the assessment of information systems
in the management ("satisfactory")
Figures 5 and 6 show the dependence of the studied indicator when changing the factor x3 (students’
grade in computer science) for certain values of the factor x2 (students’ grade in mathematics) and the
fixed values of the factors x1 and x4 (x1 = 7, x4 = 1).
Figures 5 and 6 show an increase in the probability of receiving a better grade and a decrease in the
probability of obtaining a worse grade in the subject of information systems in management, given the
increasing in the students’ grade in computer science. Different values of students’ grade in
mathematics have a great influence on the dynamics of increasing the probability of getting a better
grade.
We can say that the key to measuring student achievement is assessing student progress by measuring
gradual changes in grade in computer science and grade in mathematics, as well as positive changes in
assessment of the students’ presence in the classroom and students’ living conditions
Аssessment of information systems in
the management ("satisfactory")
Students’ grade in mathematics
1
0.8
0.6
0.4
x2=9
0.2
x2=8
0
x3=8 x3=9 x3=10
Students’ grade in computer science
Figure 5: Influence of students’ grade in computer science on assessment of information systems in
the management ("satisfactory")
Success in information systems in the
Students’ grade in mathematics
management "good"
1
0.8
0.6
0.4
x2=9
0.2
x2=8
0
x3=8 x3=9 x3=10
Students' grade in computer science
Figure 6: Influence of students’ grade in computer science on assessment of information systems in
the management ("good")
The goal-oriented approach has a solid foundation for improving the effectiveness of student
learning. We can rely on modeling results to develop pedagogical frameworks that help students
develop an approach to learning.
Now let's evaluate academic success of students through the developed regression model of an
academic success of students. Information for assessing academic success of students is presented in
table 3.
Table 3
The value of indicators to assess academic success of students
№ x1 x2 x3 x4 P(y=0) P(y=1) P(y=2)
1 7 10 5 1 0,19147 0,80853 0,00000
2 9 7 8 2 0,00006 0,07106 0,92888
3 6 8 6 2 0,82918 0,17082 0,00000
In the first case, we are likely to receive the grade of "satisfactory". In the second case, we are
expecting to receive the grade of "good". It is only in the latter case that a getting the grade of
"unsatisfactory" would be most likely.
It is possible to predict individual learning outcomes, to evaluate student actions and differences
between actions, to assess factors related to success, to create an individual learning plan through the
constructed model. The presented approach has a solid foundation for improving the effectiveness of
student learning. We can rely on modeling results to develop pedagogical frameworks that help students
develop an approach to learning.
5. Discusion
The multinomial logistic regression model confirms the possibility of combining quantitative
(intellectual) and qualitative (behavioral) factors. Our study confirms that student's presence in the
classroom, student's grade in mathematics, student's grade in computer science, and student's living
conditions affect academic success of students. The study highlighted that academic success of students
is driven by variant factors of students’ activities. It has been found that the marks for examinations
(student's grade in mathematics; student's grade in computer science) significantly affect success.
Important quantitative (intellectual) factors that determine the academic success of students are student's
grade in mathematics, student's grade in computer science. Important qualitative (behavioral) factors
that determine the academic success of students are student's presence in the classroom and student's
living conditions.
The current study’s conclusion accords with considered research regarding academic success of
students. The current study’s conclusion accords with considered research regarding academic success
of students. Student's presence in the classroom are much likelier determines the main indicator we are
researching (Lakkaraju). Student's living conditions are usually strongly affects the productivity of the
organization (Oyedeji and other scholars).
The findings indicate that selected factors can significantly affect academic success of students, and
creating an educational environment for students, and developing ways to motivate students. This study
proved that the directions of the students’ activity can be translated into particular approaches to
maintain of academic success.
5. Conclusions
The implementation of the regression method to study educational processes in higher education
institutions is considered an innovative method. The use of the regression analysis methods allows
identifying implicit relationships between elements of the educational process and solve many problems
related to the analysis and prediction of academic success of students. It also allows you to build
individual educational trajectories. The utilization of these obtained regression relations has а great
importance to predict the academic success of students on the basis of statistical information on the
control measures of the curriculum. They make it possible to establish a quantifying impacts of
disciplines on special disciplines.
This innovative approach will have a greater effect in creating incremental learning behaviors and
focusing on the influence of behavioral (non-intellectual) factors. Behavioral factors are difficult to
operationalize and suffer from poor prognostic validity. The calculation strategy shows that the mutual
influence of intellectual and behavioral factors in students' success can have an impact.
The research methodology can be used to determine the dependence of student learning outcomes on
various factors. This is necessary to improve the quality of education in universities. Creative
application of regression equations allows carrying out the quality control for each training course,
based on individual work with students agreed within teachers of the departments. This research showed
to be instrumental in demonstrating practices based on a probit regression model at the level of
development of academic success of students for support for the successful completion of their tasks by
students, better acquisition of knowledge, acquisition of competencies and profession. The proposed
method allows educational institutions to improve the learning process, make learning complete,
increase student achievement strategies.
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