Neuro-network technologies as a mean for creating individualization conditions for students learning

Abstract

The study is devoted to the analysis of the current level of use of neural network tools to create conditions for individualization of student learning. The author presents the results of the analysis of scientific developments of scientists in the questions of the use of neural network approaches in the educational process, presents a range of basic modern directions of research and development, in particular: adaptive tests, individualization of learning, improvement of the level of distance education and others. The author emphasizes that modernity is characterized by an active increase in the number of students who choose for themselves individual and distance forms of education, which leads to the need for the development of modern technological systems of distance learning. For this purpose, the scientist conducts a comparative analysis of distance learning tools, their features and capabilities in terms of implementation of an automated intellectual system of individualization of the educational process.

Full text

* Corresponding author: dessnight@gmail.com
Neuro-network technologies as a mean for creating
individualization conditions for students learning
Dmitriy Bukreiev*
Bogdan Khmelnitsky Melitopol State Pedagogical University, Department of Computer Science and Cybernetics, Melitopol, Ukraine
Abstract. The study is devoted to the analysis of the current level of use of neural network tools to create
conditions for individualization of student learning. The author presents the results of the analysis of
scientific developments of scientists in the questions of the use of neural network approaches in the
educational process, presents a range of basic modern directions of research and development, in particular:
adaptive tests, individualization of learning, improvement of the level of distance education and others. The
author emphasizes that modernity is characterized by an active increase in the number of students who
choose for themselves individual and distance forms of education, which leads to the need for the
development of modern technological systems of distance learning. For this purpose, the scientist conducts
a comparative analysis of distance learning tools, their features and capabilities in terms of implementation
of an automated intellectual system of individualization of the educational process.
1 Problem statement
Nowadays in the conditions of constant decrease of time
for studying of students, in connection with the fast
“aging” of knowledge and the great need of employers in
the qualified personnel “today and now” the problem of
intensification of education in the vector of creating
opportunities of qualitative distance and individual form
of learning with the possibility of assessment and
adjusting the learning vector for each student, based on
his or her personal skills and abilities. In connection with
this, the world scientific and pedagogical space has been
actively engaged in recent years in solving the problems
of development of the means of accumulation and
implementation of electronic means of processing large
volumes of information. However, the current state of the
art is insufficient, so there is a need to create means of
processing, transmission and presentation of information,
so it is advisable to create information systems for
processing and issuing the necessary information,
knowledge, information. Among these developments,
issues related to intelligent information systems and
means of classification and presentation of information in
the educational space of the 21st century.
2 Analysis of recent research and
publications
When conducting research, inevitably work with
information of a non-numerical and informal nature. As a
result, there is a need to build a model and tools for
quantitative description of processes and subjects related
to pedagogy [22, 23]. Of particular importance is the
quality of education, which is understood as a cumulative
indicator that reflects the result of the educational
institution, as well as meeting the needs and expectations
of society in the formation of different competencies of
the individual.
Assessment of the quality of education depends on
many factors, perhaps with unknown influence. Also in
this case, there is a specificity of the “product” of
education – a graduate of an educational institution, which
should be considered as a complex system. According to
research by V. V. Osadchyi and K. P. Osadcha the
introduction of electronic education, services and tools for
educators, students, leaders and parents in order to
cooperate with all the above mentioned participants in the
educational process, development of cloud technologies
[25], which are leading in the scientific space, can be
considered as a major among modern trends in
informatization of education. However, before
determining the quality received at the end of graduate
studies, it is necessary to identify the factors that have the
greatest impact on the student learning process. The
following are traditionally distinguished among them
[24].
1. Quality of interaction with the labor market in
shaping the content of education: the level of involvement
of employers in the formation of educational programs;
the presence of a system of targeted training of specialists
to order businesses; availability of agreements with
foreign universities on joint implementation of
educational programs; percentage of international
students in relation to all students; level of development
of the system of analysis and promotion of employment
of graduates. And as noted in [27], employers’
requirements for engineering and technical knowledge
and skills are constantly being complicated. This is due to
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the accelerated evolution of technical skills, the
emergence of new engineering professions and the
penetration of technology in all areas of human life.
2. Quality of entrants and students: level of mastering
of current educational material; comprehensive
assessment of residual knowledge; the level of ability to
solve applied problems; the level of final certification.
3. Quality of teaching technologies, educational and
methodological and logistical support of the educational
process: the level of the system of continuous control of
student success during the semester and technology of
student rating; the level of educational material; the level
of methodological support of the educational process; the
level of availability of specialized laboratories; level of
organization of support of students’ education according
to individual plans.
4. Quality of the teaching staff: the level of conformity
of the teacher’s basic education with regard to the length
of teaching and specialization; teacher’s qualification
level (academic degree, rank, position); level of social-
psychological relations of the teacher with students;
introduction of the practice of inviting specialists from
different industries to conduct training sessions.
Based on this, we can confidently say that about 50%
of the total number of factors that affect students’ level of
knowledge and their quality as specialists in the future,
focuses on the development of quality methodological
materials and their adaptation to the individual qualities
of students. In addition, as noted in the work of
V. V. Osadchyi and I. V. Krasheninnik, the main
deterrent to improving professional training in such
conditions is a shorter, compared to the standard four-
year, bachelor’s degree. During the two years of study it
is not possible to provide the necessary time for a full-
fledged practical training [26], but the creation of an
individual trajectory of student’s learning provides an
opportunity to intensify the learning load and reduces the
time for obtaining general competences. This confirms the
need for a defined and detailed analysis of the current
level of development of neural network technologies in
the vector of their use as a means to create conditions for
individualization of student learning.
During a detailed analysis of the scientific
background, it was concluded that in the general sense the
problem of development of neural network systems is
devoted to a large amount of research literature, however,
taking into account the specific topic of research
limitations it is advisable to analyze only those studies in
which the development systems are oriented to work in
the educational space, particularly in the vector of
adaptive learning. Questions of studying aspects of
designing adaptive information learning environments
were dealt with by V. Ya. Tsap [12], A. V. Narozhny [5];
from the point of view of the development of intellectual
system of quality assessment of the educational process
published their works А. А. Rybanov, V. P. Shevchuk,
E. A. Prikhodko [9]; O. I. Petkovsky revealed the
peculiarities of developing an intellectual system for
assessing the level of development of general
competences in students, which in turn confirms the view
that the use of adaptive learning means qualitatively
improves the process of information perception by
students [8].
The works of P. I. Fedoruk focused on forecasting
trends in the development of educational space in
accordance with the needs of today and the development
of adaptive testing tools, students, the scientist
emphasizes the need to solve a number of problems of test
control of knowledge in modern educational systems. In
his work, the author notes that the methods and tools of
classical test theory, despite the great potential of this
technology do not solve many of the problems posed by
the current level of educational development. He
emphasizes that adaptive tests, in this case, allow to solve
this problem and can be effectively used for solving any
problems of optimization of educational process –
evaluation of efficiency of pedagogical innovations and
technologies, monitoring, etc. [11]. Summarizing the
results of the scientist, you can identify a number of
positive features of the use of adaptive tests, so adaptive
tests allow you to: adjust to the individual abilities of the
student; to increase the accuracy of the assessment of the
level of knowledge of each individual student by using a
larger bank of questions of different levels of complexity;
reduce the duration of the test and the number of questions
required to achieve sufficient accuracy in assessing the
student’s level of knowledge; reduce student fatigue;
ensure confidentiality by providing each student with an
individual set of test tasks appropriate to his or her level
of knowledge; simplify the procedure for making changes
to the test bank. This study is key to us, as it confirms the
assumption that the use of adaptive learning creates a
powerful platform for creating personalized learning,
which in turn significantly improves students’ perception
of information at every stage of learning.
Confirmation of our assumption is the work of
A. S Dovbysh, who emphasized that further
informatization of the educational environment is based
on computerized work tools and neural network
technologies and requires a fundamentally new
information environment – a set of computer
communication systems, databases and knowledge bases,
as well as software complexes. The author note that
knowledge control systems built on the basis of learning
supportive decision-making systems have become
widespread [4]. Such systems are a further development
of expert systems. They provide a quantitative assessment
of the performance of the system on the basis of selected
criteria and models of alternative situations.
A number of works of Ukrainian scientists are devoted
to the issues of information support of intellectual systems
of vocational guidance and their use in the educational
process. Research by V. V. Osadchyi reflects that the
modern education system, using modern teaching
methods, creates a serious alternative to traditional forms
and methods of learning. With the discovery of computer
intelligent systems by humanity, humanity is increasingly
moving in the vector of transferring the learning process
into an automated electronic process. However, studies
show that a large number of human factors (such as
defining a profession, choosing a life path, etc.) cannot be
fully incorporated into an automated computer model.
Scientist argue that the task is to at least partially support
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and provide the educational process with the means of
intellectual information technology, reflecting the most
important aspects of education and choice of profession
[6]. The author emphasize that nowadays one of the main
problems of education in general is the problem of
effective professional orientation of young people in the
conditions of dynamic labor market and educational
services. We support the opinion of the authors and
consider it appropriate to use intelligent Internet decision
support systems for the choice of a profession to solve this
problem. the user needs recommendations on how to
proceed with the decision, because the choice of career
path is a starting point stage of study of any discipline and
leads to quality and informed learning.
From the point of view of the software of modern
intellectual information systems, a number of works of
scientists describes a neural network information
exchange system for organizations that, using artificial
intelligence concepts such as frames, production rules,
and inheritance networks, aims to help people share and
filter information transmitted through computer-based
exchange systems using a rich set of templates. The works
of H. Fujisaki, H. Kameda, S. Ohno, K. Abe, M. Iijima,
M. Suzuki and others [2] are devoted to neural network
systems for finding information that is the first stage of
development of modern neural network system for
educational needs. An interesting study, from the point of
view of the above issues, is the study of B. Florian-
Gaviria, C. Glahn and R. Fabregat Gesa, regarding an
experiment conducted with the support of 20 teachers. In
the course of the experiment, teachers used a software
suite for the effective use of the European Qualifications
Framework during the students’ study, the result of the
experiment was the confirmation that the modern
pedagogical process requires the development of software
in order to improve quality of the educational process.
According to the results of the experiment, the authors
claim that certain software helps teachers to create
contextual awareness, encourages students to deeper
process of thinking and understanding, increases the
efficiency of their perception of progress in the study of
discipline and helps in creating a model of success or
failure [1]. This research is very important in our scientific
work because it confirms our view that the development
of software education qualitatively improves the outcome
of the entire learning process and provides an opportunity
to influence the perceptions of students who are not
involved or little involved in training.
3 Analysis of the current state of the
software
In the framework of the study, it is advisable to analyze
the current state of the currently existing software oriented
to the pedagogical process. The analysis showed that in
recent years, the use of “virtual classes” in which students
are synchronized programmatically to create the
psychological effects of competition and competition, has
become very popular. In our opinion, one of the most
important roles in the process of distance learning is the
personification, which is carried out by regular periodic
testing of the level of preparation, degree and speed of
mastering the material by students.
During the analysis of the software, the dynamics with
which new developments in the e-learning systems
market are identified, indicate its rapid development.
Each such system contains some special tools that are
designed for the development of disciplines. Generally,
they have the following advantages:
- significantly reduces the time to develop courses;
- reduce the overall costs of the organization to develop
and maintain distance courses;
- modern level of functionality and communication
capabilities of the graphical interface of distance courses
is ensured;
- system bugs for beginning course developers are
eliminated.
The use of specialized tools for the development of
distance learning courses can significantly expand the
audience of potential course developers, including
teachers who do not have a thorough knowledge of
information technology. Here is a description of the most
popular distance learning systems.
BlackBoard offers solutions for Blackboard 9.1
educational institutions. The Blackboard 9.1 solution
(Fig. 1) has three main applications:
- Blackboard Learning System;
- Black Blackboard Community Portal System (a unifying
portal that provides access to resources, course
administration, communications tools, charts, etc. for
relevant categories of users);
- Black Blackboard Transaction System (an online system
that provides student identification, access and
management of tuition fees and more).
The Blackboard learning system consists of the
following applications:
- Course management (a content management system that
provides content management, communication tools,
tests, surveys, exams; and providing various additional
management tools for teachers);
- Blackboard Building Blocks architecture for
interoperability and customization (a component that
integrates various types of content and commercial
applications with the Blackboard platform, as well as
containing various utilities and applications for students
and teachers);
- Advanced integration and system management (a
system that integrates Blackboard solutions with various
information systems).
Learning Space 5.5 (Lotus / IBM) software allows you
to learn and teach in asynchronous mode (access to study
materials at your convenience) and participate in online
classes in real time. The user can create the content of the
course in any program, and then place the created material
in Learning Space 5.5 (Fig. 2).
The program has a flexible system of editing and
administration of the course, allows you to choose
different modes of study and keep track of the current
results of students. Distance learning courses are
organized as a series of lessons that can be self-contained,
interactive or group-based. Self-study usually contains
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reading material and tests that you must complete after
studying the material. Interactive lessons include
attending virtual class lectures, engaging in online
discussion or chatting, working with a virtual whiteboard,
and a web-sharing system. Group classes include offline
classes and online discussions, chats. All entries left
during the course discussion are available throughout the
course. The online classes are scheduled on a specific date
and time and are conducted by the teacher in a virtual
classroom in real time. Students’ current scores (grade
level, grade, time lost, number of hits, etc.) are stored in
the database. This information is available to the teacher
at any time in the form of various forms of reports.
Fig. 1. View of the Blackboard Learning System.
Fig. 2. View of Learning Space Learning System.
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Learning Space 5.5 has two main components: the
Core module and the Collaboration module. The base
module consists of a Core server (on which Learning
Space 5.5 software is installed and running), a database
server and a web server that creates the copyright. The
Learning Space 5.5 server contains the core product
software and is the core of the distance learning system. It
provides an interface for entering and retrieving user and
course information, storing and retrieving student results.
It also supports a student interface designed to attend
classes and view personal enrollment and learning
outcomes. The database stores user data, course
information, and listener ratings that are issued
automatically or at the user’s request. In addition to these
database queries, Learning Space 5.5 contains several
predefined report formats. The database contains course
structure information and the course content is retrieved
from the author’s web server.
Moodle (Modular Object-Oriented Dynamic Learning
Environment) is a suite of open source modular software
(GNU GPL) licensed to create distance learning courses
and websites. This distance learning program is focused
on teacher-student interaction and is also used to support
full-time courses.
Moodle (Fig. 3) can be installed on any computer that
supports PHP and works with MySQL, PostgreSQL,
Microsoft SQL Server; the software is cross-platform
[16]. The project is based on the principles of social
constructivism:
- in today’s learning environment, we are all potential
teachers and students at the same time;
- we are successful learners, especially trying to create or
explain something to people;
- observing the work of our colleagues is a major
contribution to learning;
- understanding others makes it possible to study them
more individually;
- the learning environment should be flexible, providing
participants with an easy-to-use tool to meet their learning
needs [22].
Fig. 3. View of the Moodle Learning System
SharePointLMS (Fig. 4) is a distance learning system
developed on the powerful multi-functional MS Office
SharePoint Server 2007. It is a comprehensive solution
that integrates all users (teachers, students, administrators,
etc.) into a single information and training space and
provides tools cooperation. Unlike Moodle, Claroline and
ATutor, the system is chargeable. It is used not only by
educational institutions and training centers, but also by
enterprises, organizations, and state structures [17].
However, having analyzed the existing software, we
came to the conclusion that today we have many great
examples of Web-based systems for distance learning,
among them Blackboard, LearningSpace, Moodle and
others. Such systems confidently use the benefits of
information technology, but the study raises the need to
determine the level of development of the aspect of the
use of neural network technologies based on distance
learning systems.
During further analysis, it can be concluded that e-
learning should not be developed in a vacuum; rather, it
should be as close as possible to the needs and desires of
students and adaptable as the course progresses.
Assessing open source e-learning platforms in order to
find the platform that is most suitable for adaptive
expansion was thus very important. The extended
platform could then be used in an online learning
environment. Therefore, the overall functionality of the
platform is as important as adaptability, and evaluation
addresses both issues.
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Fig. 4. View of SharePointLMS Learning System
After the preliminary assessment phase, nine open
source e-learning platforms were analyzed in detail. The
platforms were: Moodle, ATutor, Dokeos, dotLRN,
ILIAS, LON-CAPA, OpenUSS, Sakai and
Spaghettilearning. Based on the same survey, Moodle
dominates the rating, achieving a top five times. The
strengths of Moodle are the implementation of
communication tools, their creation and administration of
learning objects. An additional strength of Moodle is its
comprehensive didactic concept as well as data tracking.
In addition, the outstanding usability of Moodle
maximizes the value in the usability category. For other
platforms, ILIAS has received top honors in the technical,
administration, and course management categories.
Moodle achieved the best ratings. In addition, the second
and third rank can be clearly assigned to ILIAS and
Dokeos. According to pairwise comparisons, ATutor,
LON-CAPA, Spaghettilearning and Open-USS ranked
fourth, while Sakai and dotLRN came in last. The reason
for the low Sakai rating is that only the main features are
implemented so far. But the quality of these features is
very good.
Moodle got the best overall performance as well as
specific adaptation evaluation criteria.
After identifying the best platform for implementing
adaptive learning, the level of technology development to
create adaptive learning within individual and distance
learning has been identified, and, with the technology
revolution, lately, supporting adaptability in distance
learning has become the interest of many researchers in
the field, including technology. Agents can play a major
role in empowering existing distance learning systems
and providing adaptive experiences to meet students’
needs. In the industry literature, agency technologies are
also used in the context of e-learning systems to support
adaptability and enhance the learning process. Agents are
autonomous and can act reasonably in their environment.
You can achieve a powerful system that is tailored to the
needs of each student by using intellectual agents to
develop e-learning systems.
Appropriate and up-to-date in the analysis, the Event-
State-Action (ESA) model was identified. This model is
a jet model that responds in real time to any changes in its
environment based on predefined rules and conditions. It
is used in various critical systems, such as autopilot
systems and antivirus systems. It takes form if the
condition takes action. She can sense the environment and
respond in a timely manner according to predefined rules
and conditions. Most distance learning systems are
designed with a database that stores and archives all the
journals and student data and activities. Database triggers
are based on predefined pedagogical rules that can be
updated by teachers for any new requirements without
restructuring the system. In the next section, we offer our
framework to support adaptability across any distance
learning systems that mirror hybrid architecture using
agency technology. Thus, the system consists of three
components: the ESA module, the multiagent module and
the e-learning environment, as shown in Fig. 5, which
qualitatively influences the process of individualization
of the wind of learning and can be integrated with any
system of distance learning.
This environment is an important part of adopting
distance and blended learning. It also provides students
and teachers with the tools and tools they need to improve
the learning process. Moodle and Blackboard are some of
the leading e-learning platforms. However, Moodle is
probably the most popular and popular platform with over
53,346 sites reaching over 70 million users in 222
countries.
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Fig. 5. Learning Automation System.
The ESA module is designed and used as a sensitive
component. It is based on events that can occur in an e-
learning environment and respond accordingly. However,
this process is a rule-based mechanism of database
triggers such that events (training activities) such as
registration and course access are monitored by an
observation team linked to an electronic learning
environment using a communication protocol. The event
is then given to the Event Recognition Department to be
recognized and classified before the evaluation process
takes place. The Rule Evaluation Unit is responsible for
determining whether or not the current event satisfies
predefined pedagogical rules to provide the multi-agent
module with the necessary and necessary data through a
common database to provide adaptive student learning.
4 Conclusions
During a detailed analysis of scientific developments in
the vector of this problem, it was concluded that in the
general understanding of the problem of the development
of neural network systems, a large amount of research
literature is devoted, however, given the specific topic of
limitation research, it is advisable to analyze only those
studies in which the development of neural networks is
oriented to work in the educational space. These studies
have shown that high interest in scientists raises questions
about the development of educational automation tools,
including adaptive testing and individualization of the
curriculum to improve the quality of perception of
information by students, which confirms the assumption
that the development of the neural network of student
individualization, which improves the quality of
education, is an urgent and urgent issue that needs to be
addressed more quickly. For further development,
existing software for creating conditions for distance and
blended education was analyzed, but in the course of the
study we concluded that none of the systems for
introducing distance education met the criteria of full-
fledged learning. Therefore, you need to combine high-
level programming languages with logical or knowledge
languages, web technologies, and database management
systems. In the following, we intend to apply and evaluate
our approach in Moodle as a case study to provide
adaptive content based on student learning styles using the
Felder-Silverman learning style model (FSLSM).
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