=Paper=
{{Paper
|id=Vol-2300/Paper43
|storemode=property
|title=Software System for Formation the Composition of Academic Groups (Subgroups) Based on the Diffusion-Like Model
|pdfUrl=https://ceur-ws.org/Vol-2300/Paper43.pdf
|volume=Vol-2300
|authors=Natalia Porplytsya,Serhii Dubovyi
|dblpUrl=https://dblp.org/rec/conf/acit4/PorplytsyaD18
}}
==Software System for Formation the Composition of Academic Groups (Subgroups) Based on the Diffusion-Like Model==
https://ceur-ws.org/Vol-2300/Paper43.pdf
179
Software System for Formation the Composition of
Academic Groups (Subgroups) Based on the Diffusion-
Like Model
Natalia Porplytsya, Serhii Dubovyi
Department of Computer Science, Ternopil National Economic University, UKRAINE, Ternopil, 8 Chehova str.,
email: ocheretnyuk.n@gmail.com, dubovyj.sergij@gmail.com
Abstract: In this paper, the method of forming a laboratory (practical) classes on individual disciplines can be
composition of academic groups (subgroups) in an educational divided into subgroups. In this case, such a division is usually
institution was developed. The method based on a diffusion-like carried out in alphabetical order. Such an approach is
model of the dissemination of knowledge potential. The completely unreasonable, since it does not take into account
application of this method ensures the formation of academic the assessments received by students for related disciplines in
groups (subgroups) in such a way as to maximize the coefficient
of distribution of knowledge potential within each of the formed
accordance with the structural-logical scheme specialty.
groups (subgroups). Software system, which implements the Therefore, it is important to develop a method for forming
specified method, is also considered. a composition of academic groups (subgroups) in an
Keywords: diffusion-like model, knowledge potential, educational institution, in such a way as to ensure the
academic group. maximum effectiveness of the educational process. To
construct this method expedient to use a diffusion-like model
І. ACTUALITY OF THE PROBLEM of the propagation of the knowledge potential, which
Student academic group is a kind of small group, with its simulates the transfer of knowledge between students by
stages of transformation into a collective, with parameters of analogy with the crystallization process of the solid body
development and criteria of formation. Constant studying of from the melt at the outlet from the heat [7, 8]. In addition,
the level of development and knowledge of each student and it’s need to develop the software module for the
the team of the academic group enables to effectively develop implementation of this method.
the educational process in a higher educational institution,
taking into account the changes that the student’s team
II. MODELING OF THE COMPOSITION OF
undergoes in general and each member in particular, to ACADEMIC GROUPS AND SUBGROUPS BY
correct the content and methodology of this process [1-5]. APPLICATION OF THE KNOWLEDGE POTENTIAL
The initial formation of academic groups is based on the DISTRIBUTION COEFFICIENT
level of knowledge of each entrant, which usually reflects as
the average rating point for the implementation of During the formation of the composition of academic
certification works on selected external subjects of external groups, we will take into account the calculated coefficient of
independent testing (EIT). distribution of the knowledge potential calculated for them
External independent testing (EIT, formerly also External [8], that is, the higher this value is the better. It is important to
testing, ET) - entrance exams for higher education in note that the possible maximum value of this coefficient is
Ukraine. The complex of organizational procedures (first of unknown in advance. In addition, the distribution of
all - testing) aimed at determining the level of educational compositions to academic groups or subgroups is carried out
achievements of graduates from secondary schools when they in such a way as to ensure the highest and virtually equivalent
enter higher education institutions [6]. At the same time, the values of these coefficients for all formed academic units.
division of students into groups (subgroups) in educational Then the task of finding such a distribution of student
institutions, usually carried out on the basis of the average compositions can be equationted in the form of a multivariate
rating point without taking into account its detail that is, the discrete optimization problem.
compliance of competing subjects of EIT chosen specialty. At the same time, we will examine all possible variants of
An applicant may receive a high score from the “Ukrainian the composition of the academic groups, which in turn shows
language and literature” and at the same time and a low from that this task is extremely complex and belongs to the NP-
the “Mathematics” at the entrance to the technical specialty, complex class. For each combination, the value of the
where his level of knowledge plays an important role in the coefficient of distribution of the knowledge potential of the
field of mathematics. In addition, in other cases, the group will be compared, which will be based on the level of
formation of academic groups from the list enrolled to study knowledge of each student, taking into account the individual
entrants can be carried out in alphabetical order, without characteristics of each discipline and specialty.
considering at the same time the level of knowledge of So, determination of the coefficient of distribution of the
k k
applicants, which does not provide in the future the known potential will be calculated C n times, where C n -
maximum effectiveness of the educational process. Further, number of combinations from n to k; k – number of students
during the studing process, formed training groups for in the academic group (subgroup); n – total number of
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
180
students; m – required number of groups (subgroups). It will The formation of subgroups of students from groups in the
be received C k
sets of coefficients of distribution first semester of the first year will also be based on the results
n
of the EIT. Thus, the equation for determining the coefficient
𝑏𝑏𝚤𝚤 = 𝑏𝑏1 … 𝑏𝑏𝑚𝑚 , i = 1...k which
knowledgeable potential ���⃗ of distribution of the knowledge potential of the formed
we will store, for further determination of the "best" subgroup A j , g , m +1 will look like:
according to the following criteria:
j
���⃗
⎧𝑏𝑏1 → 𝑚𝑚𝑚𝑚𝑚𝑚, 𝑏𝑏1 ∈ 𝑏𝑏𝚤𝚤 ; A j , g ,m +1 = ∑ ϕ j ,k ,m +1 − ϕ j ,k ,m where
⎪ … j =0
⎪
𝑏𝑏𝑚𝑚 → 𝑚𝑚𝑚𝑚𝑚𝑚, 𝑏𝑏𝑚𝑚 ∈ ���⃗
𝑏𝑏𝚤𝚤 ; ϕ j ,k ,m +1 − ϕ j ,k ,m = f j ,k ,m +
(1)
⎨|𝑏𝑏1 − 𝑏𝑏2 | → 0, 𝑏𝑏1 , 𝑏𝑏2 ∈ ���⃗ (4)
⎪
𝑏𝑏𝚤𝚤 ; + D j ,k ,m ∑ σ k ,k ,k (ϕ j ,k ,m − 2ϕ j ,k ,m + ϕ j ,k ,m ),
⎪… 1≤ k < k < k ≤ k j
⎩|𝑏𝑏1 − 𝑏𝑏2 | → 0, 𝑏𝑏𝑚𝑚−1 , 𝑏𝑏𝑚𝑚 ∈ ���⃗ 𝑏𝑏𝚤𝚤 ; f j ,k ,m - source of knowledge, D j ,k ,m - coefficient which
For example, when dividing students into two subgroups characterizing the ability k-agent j-group redistribute
k information (knowledge) at the time m (analog of diffusion
will be implemented C n /2 comparisons of the distribution coefficient)[7].
coefficients of the knowledge potential of the formed It should be noted that for the use of equation (4), the
subgroups As can be seen from expression (1), as a result of student's marks with the EIT, which are calculated in the 200-
the test, an option is chosen for which the difference in the point system, should be converted into 100-point system. It is
knowledge potential among the groups formed will be the known that the minimum passing point of the EIT is 100
smallest, and the value of the knowledge potential will be points, while the minimum score required for passing the
greatest. One of the options for introducing generalized discipline at the university is 60 points. We will make the
potential K j -group 𝜑𝜑𝑗𝑗,𝑚𝑚 . There is a representation of it in the appropriate proportion for transfer of points from the 200-
form of some function from 𝜑𝜑𝑗𝑗,𝑘𝑘,𝑚𝑚 , in particular, in the form point system to 100 points, which is represented by the
equation:
of a generalized arithmetic mean:
1 𝑘𝑘𝑗𝑗 60 * c ,
𝜑𝜑𝑗𝑗,𝑚𝑚 = ∑𝑘𝑘=1 𝑎𝑎𝑗𝑗,𝑘𝑘 𝜑𝜑𝑗𝑗,𝑘𝑘,𝑚𝑚 (2) x= (5)
𝑘𝑘𝑗𝑗 100
where α j,k – some weight factor. where с – EIT score for a subject.
In the initial iteration of the use of the method that is, the
formation of a composition of academic groups of students of
the first year will determine the knowledge potential of each
student based on the results of the EIT. In this case, each item
will be given a coefficient of importance that corresponds to
the chosen specialty. Thus, the knowledge potential of a
particular student will look like:
𝜑𝜑𝑗𝑗,𝑘𝑘,𝑚𝑚 = ∑𝑡𝑡𝑖𝑖=1 𝑧𝑧𝑖𝑖 𝑝𝑝𝑖𝑖 (3)
where - coefficient of importance of an object, -
subject score, а t – number of subjects. The sum of the
coefficients of importance ∑ti=1 zi = 1.
To form a composition of subgroups for the subject, you
should introduce the notion of the source of knowledge,
which will be the lecturer who will conduct practical classes.
Depending on the qualification, each lecturer will be assigned
the appropriate efficiency factor, which will characterize him
as a source of knowledge. Table one shows the adequacy of
the efficiency coefficients for the qualifications obtained.
Fig. 1. A fragment of the structural-logical scheme of the related
TABLE 1. CONFORMITY OF THE POINTS FOR THE disciplines of the specialty "Software Engineering"
CALCULATION OF THE QUALIFICATIONS
After forming a set of all possible combinations of the
Obtained Level of Efficiency formed groups (subgroups), we calculate the value for them
qualification qualification coefficient
A j , g ,m +1 (on initial iterations j,m and then determine the
Lecturer trainee 1 60
Lecturer 2 65 best combination by the equation (1). The time spent on
Lecturer, Ph.D. 3 80 forming all possible combinations of the subgroup depends
Senior Lecturer, Ph.D. 4 90 on the number of groups (subgroups) and the number of
Docent, Ph.D. 5 100 students in these groups (subgroups). Reduction the timing of
these operations may be the subject of a study in the future.
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
181
Note, during the program implementation of equation (1), it z1 = 0,5 - the coefficient of the importance of discipline
is advisable to select several "best" combinations of groups
(subgroups) and give the user the opportunity to select the "Object-oriented programming", z 2 = 0,25 - "Algorithms
"optimal" combination. Formation of subgroups of students and data structures", z 3 = 0,25 - "Fundamentals of
in all subsequent semesters will be carried out on the basis of programming". Student's points for the above-mentioned
the results of previously studied adjacent disciplines, asking
them with the coefficients of importance, based on the disciplines will be, for example, p1 = 75 , p 2 = 77 ,
structural-logical scheme of the specialty. For example, in p3 = 86 accordingly. Then, according to equation (3), his
Figure 1, a fragment of the structural-logical scheme of
knowledge potential in relation to the discipline ".NET
adjacent disciplines for the specialty "Software Engineering".
Technology" will equal:
The equation for determining the knowledge potential of a
𝜑𝜑𝑗𝑗,𝑘𝑘,0 = 0,5 ∗ 75 + 0,25 ∗ 77 + 0,25 ∗ 86 = 78,5
subgroup based on the results of related disciplines
corresponds to equation (4). The equation (3) will be used to Now we can equationte an algorithm for the
determine the knowledge implementation of the proposed method for forming the
For example, the formation of subgroups for the discipline composition of academic groups (subgroups) with the use of
".NET Technology" will be based on the results of previous a diffusion-like model, whose block diagram is shown in
related disciplines, where, according to the equation (3) Figure 2.
Fig.2. Algorithm for the implementation method for forming the composition of academic groups (subgroups) with the use of a diffusion-
like model
III. SOFTWARE REALIZATION groups, as well as other data necessary for the functioning of
the system are stored in RDBMS MySql.
The software system of forming the composition in Consider functional details of system. After launching the
academic groups based on a diffusion model is developed program, the user will be given full access to the entire
using an object-oriented approach and .NET technology, functional system. The "Form groups" function gives the user
programming language С#. Student data, lists of formed the ability to form groups of first-year students based on the
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
182
results of external testing. In this case, each item will be The logical and conceptual description of the functionality
given a coefficient of importance that corresponds to the of the system for the function "Form subgroups", is reflected
chosen specialty. in the sketch of the form, which is presented in Figure 3.
The logical and conceptual description of the functionality Once the groups and sub groups have been formed, the
system for the function "Form groups" is reflected in the user will be able to save them or form them again, having
sketch of the form, which is presented in Figure 2. previously changed the parameters.
IV. CONCLUSION
The paper analyzes the existing methods of forming the
composition of academic groups (subgroups) and shows that
they are not effective, because they do not take into account
the influence of the interaction of students between groups
and the lecturer on the effectiveness of the educational
process. It is shown that for constructing the method of
forming the composition of academic groups it is expedient
to use the diffusion model of the process of dissemination of
knowledge potential.
A new method is proposed for the formation of
composition of academic groups based on a diffusion-like
model using the coefficient of distribution of the knowledge
Fig. 3. Program’s window for forming a composition of groups potential of the group as the main comparison parameter.
The function "Form subgroups" is similar to the "Form
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ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic