=Paper=
{{Paper
|id=Vol-3179/Paper_10.pdf
|storemode=property
|title=Ontologically-Controlled E-Learning Systems as a Means for Developing Conceptual Thinking of the Individual
|pdfUrl=https://ceur-ws.org/Vol-3179/Paper_10.pdf
|volume=Vol-3179
|authors=Viacheslav Liskin,Taras Olefirenko,Alexander Mytnyk,Sergiy Syrota
|dblpUrl=https://dblp.org/rec/conf/iti2/LiskinOMS21
}}
==Ontologically-Controlled E-Learning Systems as a Means for Developing Conceptual Thinking of the Individual==
https://ceur-ws.org/Vol-3179/Paper_10.pdf
Ontologically-Controlled E-Learning Systems as a Means for
Developing Conceptual Thinking of the Individual
Viacheslav Liskin a, Taras Olefirenko b, Alexander Mytnyk b and Sergiy Syrota a
a
Department of Applied Mathematics National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic
Institute” 37 Peremohy Ave. Kiev, Ukraine
b
Faculty of Pedagogy and Psychology of the National Pedagogical University Dragomanov, Kiev, Ukraine
Abstract
The article describes the mission of orderly knowledge in the formation of the personality
of a competitive specialist. The technology of building an information system based on the
cubic representation of the ontology of the discipline is presented. The presentation of the
educational topic in the form of an ontological cube, which consists of three planes: conceptual,
informative and stimulating, is characterized. It is proved that such presentation of information
in the process of e-learning will promote the development of conceptual thinking and speech,
intellectual skills of the future specialist, provides an opportunity to find an unexplored
scientific niche in a particular professional field, and, as a result, stimulates the desire to create
their own professional image. The essence of the named planes is revealed, the expediency of
division of material of planes into chunks is characterized. The technology of construction of
information system on the basis of cubic representation of ontology of educational discipline
is described. Examples of application of technology for construction of ontology of subject
area on discipline “Algorithms and data structures”, an ontological cube on a subject:
“Psychophysiological bases of creativity" of a course “Psychology of creativity” are resulted.
The results of the introduction of the presented technology in the educational process are
described on the example of academic groups of students majoring in “Applied Mathematics”
KPI Igor Sikorsky and groups of students majoring in “Psychology” of the Pedagogical Faculty
of NPU Drahomanov in 2020-2021. The analysis of the results made it possible to state that
the developed ontological system of e-learning is effective and efficient, to state that its
implementation allowed to form students' knowledge in the form of flexible systems suitable
for use in various educational institutions and life situations.
Keywords 1
E-learning, information technology; ontological cube, chunk, conceptual plane, informative
plane, stimulating plane
1. Introduction
The transformation of Ukraine into a developed European state is associated with the direction of
the system of general and higher education for the development of a competitive individual who can
think and, as a consequence, create material and spiritual values, adequately assess the results of their
own activities. and others. In order to teach a person to think what is important for becoming a
competitive professional, it is necessary to radically change the formal education, aimed primarily at
assimilating "correct" information or presenting it through information technologies in the form of
various presentations, and then checking its assimilation through surveys. and performance of
reproductive tasks, for practical education. At the heart of such education is the assimilation of
meaningful information within the problem-searching dialogues between the subjects of the educational
process and training students to implement it in various bulk and life lessons in general secondary
education and students - in various professional situations in seminars and practical classes in
Information Technology and Implementation (IT&I-2021), December 01–03, 2021, Kyiv, Ukraine
EMAIL: liskinslava@gmail.com (Liskin Viacheslav); to@npu.edu.ua (Olefirenko Taras); mytnyk1969@ukr.net (Mytnyk Alexander);
sergiy.syrot@gmail.com (Syrota Sergiy)
ORCID: 0000-002-9418 (Liskin Viacheslav); 0000-0002-2916-7344 (Mytnyk Alexander); 0000-0003-0795-167X (Syrota Sergiy)
©️ 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)
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institutions of higher education. This creates an educational environment in which each subject of
knowledge from the consumer of ready-made information becomes a seeker and actor who learns to
apply it in practice, and then - to create something fundamentally new in each professional field. Such
an environment can be built in the process of e-learning on the basis of ontologically controlled systems.
The main mission of such systems is the development of conceptual thinking, which is the basis for the
development of divergent, creative thinking and speech. As a result, there is the formation of orderly
knowledge. Such knowledge is a tool for cognition of the objective world, various spheres of
professional activity, self-education, which must be carried out constantly during conscious life.
Studies by T. Berners-Lee, T. Gruber, and K. Sikar are devoted to the development of a methodology
for working with ontologies. The ontological graphs developed and presented by scientists describe a
certain set of subjects, between which there is a certain network interaction, due to the set of information
processes that make it up. Such subjects can be represented in the form of functional nodes, between
which the interaction is realized based on the use of their multiple connectivity [1, 2, 3], which defines
it as an open network-centric environment. Ontological models reflect the researcher's conceptual view
of a subject area and provide an opportunity to unambiguously define its concept, structure, accumulate
and repeatedly use knowledge. The need for such a way of presenting knowledge was strongly felt in
many branches of science and, in response to the urgent need, the ontological approach began to be
used almost simultaneously in many fields. Currently, we can talk about the formation of a scientific
direction related to the development and application of ontological models
O. Strizhak [4] proposed a method of information modeling of subject areas, aimed at ensuring the
systematic assimilation by students of large layers of information that indirectly affects the mental
development of the individual, in general, and in particular, the development of conceptual thinking.
Any information system is a means of developing cognitive processes. For example, George Miller [5]
investigated the role of thinking in information processing, the separation of primary and secondary
information from the flow of information, the establishment of causal relationships and, consequently,
the construction of inferences. However, in our opinion, it is necessary to strengthen the development
function of information systems. For example, the development of "cognitive flexibility", namely: the
ability to perceive, analyze and interpret information (a certain situation) in different ways according to
J. Brown and E. Langer [6]. There is also a lack of information systems that would reflect the
developmental educational space, provide an individual trajectory of development of each subject of
knowledge, and, at the same time, have a methodological nature, would cover the methodology of the
discipline. The purpose of the article: to present an information system that allows you to display,
systematize and visualize the conceptual apparatus, phenomena, processes and relationships between
them of a particular discipline, which will promote the development of conceptual thinking and speech
of the future specialist.
Such systems should become a means of educating the desire to create something fundamentally
new in every professional field.
2. Technology of building an information system based on a cubic
representation of the ontology of the discipline
In the construction of conceptual thought, in addition to mental operations, involved such
components of consciousness as: sensation, sensory perception, emotional intelligence and certain
experiences. Based on the above, electronic coverage of each educational topic should promote the
development in tandem of mind, will and emotions. In addition to the hierarchy of conceptual apparatus
with coverage of the scope, features of each concept and the relationship between them, the subject
must understand the methodology (logic) of a science, namely: methods of scientific knowledge, which
are the basis for scientific approaches, patterns, principles of theoretical and practical activities, their
mutual enrichment. The main methods of study, knowledge of the content and essence of any discipline
in higher education include:
general, philosophical method - dialectical method. The implementation of this method means
the disclosure of the genesis, evolution, features, content and history of a particular science,
theoretical approaches to the disclosure of the content of concepts;
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system method (system analysis method). The implementation of this method involves the
consideration of a particular science as a single system of relationship between theory and
practice (professional activity);
structural method, the implementation of which involves the creation of a system of concepts of
the discipline, their division into types and subspecies;
functional method, the implementation of which involves the disclosure of the meaning of
certain theoretical provisions, which are the basis of existing achievements. It is important that
these achievements evoke in the future specialist intellectual feelings (admiration, surprise, etc.),
which would cause a desire to create something new;
psychological and pedagogical methods, including testing, questionnaires, surveys,
observations, psychological and pedagogical experiment. They are a means of testing the
effectiveness of mental development of the individual, the quality of learning the course.
The main means of meaningful assimilation of information in higher education institutions is a
lecture. In order for each lecture to promote the development of conceptual and divergent thinking,
intellectual skills, education of the desire to think, the pursuit of achievement, it is necessary to present
educational material, using information technologies, in the form of an ontology.
The ontology of the discipline is based on the ontological system [7].
The proposed ontological system consists of a meta-ontology of the discipline, which in turn consists
of two parts [8]:
𝑂𝑀𝑒𝑡𝑎 =< 𝑂𝐷𝑖𝑑𝑎𝑐𝑡𝑖𝑐 , 𝑂𝐶𝑜𝑛𝑡𝑒𝑛𝑡 > (1)
where 𝑂𝐷𝑖𝑑𝑎𝑐𝑡𝑖𝑐 – didactic ontology of the discipline;
𝑂𝐶𝑜𝑛𝑡𝑒𝑛𝑡 – content ontology of the discipline.
In turn, the didactic ontology of the discipline is represented by three:
𝑂𝐷𝑖𝑑𝑎𝑐𝑡𝑖𝑐𝑠 =< 𝐶ℎ, 𝐿, 𝑅 >, (2)
where 𝐶ℎ = {𝑐ℎ𝑖 } – is the set of chunk names that make up the didactic ontology;
𝐿𝑖 = {𝑙1𝑖 , … , 𝑙𝑚𝑖 } – set of links to content elements;
𝑅 is the set of relations, and two relations 𝑅1 and 𝑅2 are considered:
𝑅1 ⊂ 𝐶ℎ × 𝐶ℎ – the relationship is “studied after”;
𝑅2 ⊂ 𝐶ℎ × 𝐿 – the ratio “chunk is related to the content element”.
It is advisable to divide the material into chunks. Chunk points to a set of logically connected
learning material generalized to a topic presented to a student [8].
𝑂𝐶𝑢𝑏𝑒 =< 𝐶ℎ, 𝐷𝐶ℎ𝐼, 𝐿, 𝑅 > (3)
where 𝐶ℎ = {𝑐ℎ𝑖 } – is the set of chunk names that make up the conceptual plane;
𝐷𝐶ℎ𝐼 = {𝐷𝐶ℎ𝐼𝑖 } – is the set of test tasks that make up the stimulus plane;
𝐿𝑖 = {𝑙1𝑖 , … , 𝑙𝑚𝑖 } – set of references to the information plane;
𝑅 is the set of relations, and two relations 𝑅1 , 𝑅_2 and an additional relation 𝑅3 are considered:
𝑅1 ⊂ 𝐶ℎ × 𝐶ℎ – the relationship is "studied after";
𝑅2 ⊂ 𝐶ℎ × 𝐿 – the ratio "chunk is associated with a content element";
𝑅3 ⊂ 𝐶ℎ × 𝐷𝐶ℎ𝐼 – the ratio "chunk is related to the test task".
The conceptual plane of the lecture material presents the key chunks of the topic, the relationships
between them, the links with the chunks of previous and subsequent topics. In order to form a cognitive
interest in the perception of factual material contained in the information plane, it is advisable, in our
opinion, in the conceptual plane to offer a problem to solve which students do not yet have enough
knowledge. Therefore, in the conceptual plane, the future specialist places certain assumptions, and
after understanding the material of the informative plane, the final decision. The problem task, for
example, can be in the form of a certain judgment, where it is necessary to determine and then prove its
truth. This can be a task related to professional activities.
The informative plane reveals the content of the presented concepts, characterizes the methodology
(logic) of a particular educational topic, namely: reveals the implementation of certain methods of study,
knowledge of the content of the presented topic, which we have described above. In the information
plane certain facts concerning use of the theory in practice, in concrete professional activity are
characterized, certain achievements, certain scientific researches and their results are resulted; topics of
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possible scientific researches, developments are offered. Examples of certain achievements and possible
research and development can stimulate students' desire to create their own professional image (unique
image of “I”), to find their professional niche.
Figure 1: General representation of the ontological cube
Another component of the information system - the stimulating plane should be aimed at developing
the ability to use the knowledge of the course in certain educational and professional situations. Based
on the above, a system of tasks of different levels of complexity should be presented: reproductive,
problem-solving and creative. In order to develop conceptual thinking among reproductive tasks, we
recommend offering tasks with a logical load, activating the knowledge of formal logic. For example,
to find an error in the proposed definition of the concept, to put in this definition the closest generic
concept; graphically, with the help of Euler's circles, show the relationship between the volumes of
concepts and make a general, partial and negative judgment with the proposed concepts. It is expedient
to offer tasks for drawing inferences, using the offered concepts. Thus, it is possible to offer students to
build the following inferences: categorical syllogism, conditionally categorical, divisive-categorical.
We believe that problem-solving tasks should be a means of self-education. This is a task to prove the
truth of certain statements based on the course material. Sometimes it is necessary to look for additional
information from the course to build a proof. In order to develop intellectualized speech (clear, concise,
evidential), it is advisable to use quantifiers of generality or existence in the proposed statements. In
order to develop students' ability to work in a team among the creative, it is advisable to offer tasks for
designing, combining, creating certain projects, which provides for the mission of each project
participant.
3. Application of technology to build an ontology of the discipline
In the course of the research, an ontology of the subject area for the discipline "Algorithms and Data
Structures" and with the help of the editor Protégé 5 was built.
In figure 2 presents a generalized principle of constructing an ontology of the subject area.
The Protégé 5 ontology editor has a downward or pyramidal ontology design strategy.
To build an ontology in the Protégé environment, you need to perform the following steps:
An example of numbered list is as following.
1. to analyze the subject area of ontology - to select classes;
2. organize classes in the hierarchy of the base class - subclass;
3. determine the properties and their allowable values;
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4. fill in the property values for the instances of the class.
Table 1 shows an example of classes, relationships and instances for the ontology of the subject area
in the discipline “Algorithms and data structures”.
Figure 2: The general principle of constructing the ontology of the subject area
Table 1
Subject area ontology of the discipline "Algorithms and data structures"
Classes Relations Instances
Prloblems Computing the convex hull
…
Solving methods of the has Brute force
algorithmic problems is Divide and conquer
is resolved …
Time complexity class-subclass Exponential
uses structure Polynomial
… Logarithmic
…
Data structures Arrays
Lists
…
By default, classes can intersect. In order to divide groups of classes, each individual will not be
able to be an instance of more than one class, you need to make the groups of classes non-intersecting
(Disjoint Classes).
Figure 3 presents the classes in the construction of the ontology of the subject area of the discipline
"Algorithms and data structures" in the editor Protégé 5, which are non-intersecting.
Properties (rdf: Property) in OWL (RDF (s), XML) represent the relationships between classes or
their objects [9]. In Protégé 5 there are two main types of properties:
object property (owl: ObjectProperty), which is a relationship between two classes or
individuals;
data type property (owl: DataTypeProperty), which associates the individual with the data type
value of the RDF scheme, ie defines the relationships between the individual and the data values.
Similar to the Thing class, the Protégé 5 editor automatically creates a TopObjectProperty property
by default, to make it easier to combine ontologies. Therefore, all object properties are
TopObjectProperty subproperties. Protégé 5 implements the ability to visualize the created ontology
using an ontograph. An ontograph is an information model of a subject area in the form of an oriented
graph [10], the vertices of which are classes, and the arcs are connections that connect these vertices.
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In fig. Figure 4 presents the ontograph of the discipline "Algorithms and data structures" for 4
entities, obtained by constructing an ontology of the subject area of the discipline in the environment
Protégé 5.
Figure 3: Classes of ontology of the subject area of the discipline "Algorithms and data structures"
Figure 4: A part of ontograph of the discipline "Algorithms and data structures"
The resulting ontology of the subject area with the increase in the number of entities becomes very
big. The generally accepted principle of constructing an ontology of the subject area has advantages,
such as: the presence of structure and the ability to acquire new knowledge. In turn, the disadvantages
of this approach are: the dependence of the structure of the ontology on the subject area and
cumbersomeness. Classes, subclasses, and instances may be different in nature for each subject area.
Therefore, the developed information technology should work equally with different subject areas.
In order to develop such information technology, it is proposed to work with the ontology of the
discipline (course), as all courses have a similar structure.
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Here is an example of the reflection of each of these areas of the presentation of the topic:
"Psychophysiological foundations of creativity" course "Psychology of Creativity" for students
majoring in "Psychology" - future practical psychologists of educational institutions.
Conceptual plane
Figure 5: Conceptual plane
The informative plane reveals the content of these concepts and examples of their implementation
in the professional sphere. It is advisable to divide the material of the planes into chunks. Chunk points
to a set of logically connected learning material generalized to a topic presented to a student. Here is an
example of chunks from the proposed topic in each plane. For example, in the conceptual plane will be:
Objective product novelty: discovery, inventions,improvement.
In the informative plane the characteristic of the named concepts is given. The discovery is
fundamentally new: something that did not exist in a certain professional field. Inventions - a certain
modernization (addition) of the existing. Improvement - the creation of certain tools for optimal (with
minimal time and effort) awareness of the new. There are also examples of discoveries, inventions,
improvements in various professional fields. Thus, in pedagogy, the discovery, for example, the
creation of pedagogical concepts, technologies, author's schools: author's school Guzyka M.; V.O.
Sukhomlynsky's educational system, developmental training according to O. Dusavytsky, etc.;
inventions: methods, techniques of teaching and education: "whispering in the ear", "pedagogical error"
by Sh.O. Amonashvili; lessons of open thoughts from V. Shatalov, "commented management" by SM
Lysenkova, etc .; improvement: creation of a certain system of clarity: drawings, diagrams, tables, etc.
And, as a result, students have a desire to work on building their own professional image (unique image
of "I"). The image construction algorithm is also presented in this plane.
A system of tasks is presented in the stimulating plane. We offer an example of a research task.
Determine whether each of the proposed theses is true or false. Prove your opinion.
Each individual can become creative in the learning process.
The left hemisphere of the brain is responsible for the development of creative thinking.
After students get acquainted with the essence of the presented concepts in the informative plane,
they can determine that the first statement is true, the second - false.
4. The results of technology implementation
On the basis of ontologies of academic disciplines in the form of cubes and generated control
measures with the help of a test task generator [11] an ontologically controlled e-learning system was
created. The use of the developed ontologically controlled e-learning system in the study groups of
students majoring in applied mathematics KPI Igor Sikorsky and in the study groups of students
majoring in "Psychology" ("Practical Psychology" of the Pedagogical Faculty of NPU Drahomanov in
2020-2021 demonstrated the effectiveness and efficiency of the developed ontological control system
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of e-learning [12] and allowed to form knowledge in the form of flexible systems suitable for use in
various educational and life situations
Lectures, practical classes and laboratory classes for filling courses in ontologically controlled e-
learning system (OCELS) were developed and created by the authors of the course, and the bank of test
questions and tests was generated automatically using the developed information technology and tools
to automate the creation of test questions and computational tasks.
An example of integration of an ontologically controlled engine into existing e-learning systems
[13], on the example of an e-course created with the help of developed tools, is presented in Fig. 6.
Figure 6: Integration of OCELS with the system "Moodle" on the example of the discipline "Algebra
and Geometry"
In the main page of each student, the educational content is distributed by modules and topics of the
discipline and is presented in several formats. The student independently chooses a convenient time for
processing materials and format. In order to consolidate the skills after studying each topic, the student
is asked to take a test, which consists of questions from the catalog of questions arranged by DChI.
Based on the results of the student's answers, a grade for the module or recommendations are given, an
individual learning trajectory is created for repeating certain topics (chunks), answers to questions about
which were incorrect. To determine the number of students to participate in the experiment, which
ensures the representativeness of the sample, the formula was used:
𝑁∗𝑡 2 ∗𝜎 2
𝑛= , (4)
𝑁∗∆2 +𝑡 2 ∗𝜎 2
where 𝑛 is the size of the representative sample;
𝑁 is the size of the general sample;
∆ - marginal error of sample representativeness;
𝜎 is the mean square of the deviations;
𝑡 is the confidence factor, which depends on the probability of determining the marginal error.
Consider the flow of academic groups of students, which is a general sample, so 𝑁 = 83. The
marginal error of representativeness of the sample, which characterizes its accuracy or the level of
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significance was chosen to be 5%, in order to maximize the accuracy of the experiment. For ∆= 0.05
the confidence factor or the Student's coefficient will be 𝑡 = 1.96. Thus we obtain:
83 ∗ 1,962 ∗ 0,52
𝑛= ≈ 45
83 ∗ 0,052 + 1,962 ∗ 0,52
To increase the representativeness and in accordance with the study groups of students, each about
25 students, the value obtained was rounded to 𝑛 = 50.
In total, 4 study groups and 83 students were involved in the experiment. The sample of students is
divided into two groups: control group (group A) and experimental group (group B). About 50 students
took part in the experiment in each discipline, which ensures the representativeness of the data
(according to formula 4), in addition, the volumes of the experimental and control groups are almost
the same (table 2).
Table 2
Distribution of students by groups and disciplines
Disciplines Control group Experimental group
Algebra and 29 29 28 29
geometry
Algorithms and 24 27 26 28
data structures
Table 3 shows the results of educational achievements for the control and experimental groups
before the experiment in the discipline “Algebra and Geometry”.
Table 3
Distribution of the level of academic achievements before the experiment
Group Total A B/C D/E F/Fx Success rate
students in %
Group A 27 4 12 9 2 92,6
Group B 28 5 11 10 2 92,9
As can be seen from table 3, the success rate in the control and experimental groups is almost the
same. At the beginning, it is necessary to establish whether there is a difference in the initial levels of
knowledge of group A and group B (before the experiment).
Let's compare hypotheses (hypothesis 𝐻0 - the level of knowledge in groups is the same, 𝐻1 - the
level of knowledge in groups is not the same) by Pearson's criterion:
1 (𝑛1 ∗𝑞2𝑖 −𝑛2 ∗𝑞1𝑖 )2
𝜒2 = 𝑛 ∗𝑛 ∗ ∑4𝑖=1 𝑞1𝑖+𝑞2𝑖
, (5)
1 2
where 𝑛1 is the number of students in group A;
𝑛2 - number of students in group B;
𝑞1𝑖 - the number of assessments of group A in the category;
𝑞2𝑖 - the number of assessments of group B in the category.
The value of the criterion 𝜒𝐴2 = 0.39 calculated by formula 5 and 𝜒𝐵2 = 11.345. Since the inequality
𝜒𝐴 < 𝜒𝐵2 , so 𝐻0 is accepted and 𝐻1 is rejected.
2
Thus, two samples of students belong to the same general population, we have the same distribution
law, they are randomly selected and have the same level of success.
To evaluate the results of electronic testing and identify students with a dishonest attitude to learning,
the calculations of the following indicators were used formula 6 and 7.
Number of points obtained for the test work:
𝑇𝑆 = ∑𝑘𝑖=1 𝑄𝑖 , (6)
where 𝑄𝑖 - points obtained for the i-th question;
𝑘 is the number of questions asked.
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The number of points obtained for the studied chunk:
∑𝑘
𝑖=1 𝐷𝐶ℎ𝐼𝑖 ∗𝑚
𝐶ℎ𝑅𝑖 = 𝑛
, (7)
where 𝐷𝐶ℎ𝐼𝑖 - questions related to the i-th chunk;
𝑚 is the number of correct answers;
𝑛 is the number of questions asked.
In the context of the COVID-19 pandemic, all groups studied in a mixed mode, mainly learning
using e-learning systems. Each control group studied according to the traditional methodology, which
includes conducting lectures, practical, laboratory classes and modular tests throughout the course and
credit or exam at the end of the course. Each experimental group studied according to a mixed
methodology, based on a chunk-oriented approach to learning, which also includes lectures, practicals,
laboratory classes and modular tests throughout the course and credit or exam at the end of the course.
The purpose of the experiment was to prove or disprove the hypothesis of the effectiveness of the
integration of the chunk-oriented approach to learning, as well as to study the rationality of the use of
algorithms for creating test tasks and tests. In the 2020-2021 academic year, 7 tests were conducted in
the discipline of "Algebra and Geometry". Each test work consisted of questions that were generated
using the developed tool. During the 1st semester of the 1st year, 3 tests in the discipline "Algebra and
Geometry" were conducted for the control and experimental groups, and 4 in the 2nd semester.
Figure 7 and table 4 shows the results of groups A and B of 7 tests, as well as the result and general
statistics.
Figure 7: Diagram of test results in the discipline "Algebra and Geometry"
At first glance, on figure 7 it can be seen that from the beginning of the experiment the average score
of group B increased and the time of work decreased, at the same time both of these indicators in group
A changed without visible patterns, indicating possible cases of dishonest attitude to learning or
deterioration of overall success in group, which can be caused by many factors.
Table 4
The average value of test results in the discipline "Algebra and Geometry"
Group Efficiency indicators
Average rate Average time (minutes)
Group A ~ 75 ~ 65
Group B ~ 82 ~ 49
110
After analyzing the data shown on figure 7 and calculating the average rate on tests and the average
time of work, we can conclude that for the discipline "Algebra and Geometry" the average percentage
of correct answers in group B is 7% higher than the result of group A, and the time of work in group B
is less than group A for 16 minutes.
Thus, the control group and the experimental group differ in the results of learning success in favor
of the experimental group. This allows us to make conclusions about the effectiveness of the developed
information technology and chunk-oriented approach to learning. It also provides a person-centered
approach to monitoring student knowledge that eliminates subjective relationships.
5. Conclusion
Such information systems help individuals to systematize large layers of information, get
achievements in a particular field of knowledge and, as a consequence, to find their, not yet explored,
professional niche, on the one hand - have a strong developmental character - are means of becoming a
competitive specialist. Presentation of the educational topic in the form of an ontological cube provides
an opportunity to systematize knowledge and use it in different educational and life situations.
6. References
[1] T. Berners-Lee Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential»,
The MIT Press, 2005, ISBN 0-262-56212-X
[2] Gruber T. R. A translation approach to portable ontologies // Knowledge Acquisition, 1993, V.
5(2), P.199-220
[3] Sycara K. P. Multiagent systems / K. P. Sycara // AI Magazine. – 1998. – №10. – С. 79–93.
[4] Strizhak O. E. Komp’yuternі tezaurusi yak tekhnologіchna platforma stvorennya avtors'kih
metodik vikladannya predmetnih disciplіn / O. E. Strizhak // Aktual'nі problemi psihologії:
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