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<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>October</journal-title>
      </journal-title-group>
    </journal-meta>
    <article-meta>
      <title-group>
        <article-title>9th Illia O. Teplytskyi Workshop on Computer Simulation in Education</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Arnold E. Kiv</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Serhiy O. Semerikov</string-name>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="aff" rid="aff2">2</xref>
          <xref ref-type="aff" rid="aff3">3</xref>
          <xref ref-type="aff" rid="aff4">4</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Vladimir N. Soloviev</string-name>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Andrii M. Striuk</string-name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Ben-Gurion University of the Negev</institution>
          ,
          <addr-line>P.O.B. 653, Beer Sheva, 8410501</addr-line>
          ,
          <country country="IL">Israel</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Institute for Digitalisation of Education of the National Academy of Educational Sciences of Ukraine</institution>
          ,
          <addr-line>9 M. Berlynskoho Str., Kyiv, 04060</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
        <aff id="aff2">
          <label>2</label>
          <institution>Kryvyi Rih National University</institution>
          ,
          <addr-line>11 Vitalii Matusevych Str., Kryvyi Rih, 50027</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
        <aff id="aff3">
          <label>3</label>
          <institution>Kryvyi Rih State Pedagogical University</institution>
          ,
          <addr-line>54 Gagarin Ave., Kryvyi Rih, 50086</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
        <aff id="aff4">
          <label>4</label>
          <institution>University of Educational Management</institution>
          ,
          <addr-line>52A Sichovykh Striltsiv Str., Kyiv, 04053</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
      </contrib-group>
      <pub-date>
        <year>2021</year>
      </pub-date>
      <volume>1</volume>
      <issue>2021</issue>
      <fpage>0000</fpage>
      <lpage>0002</lpage>
      <abstract>
        <p>This is an introductory text to a collection of selected papers from the 9th Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2021), held in Kherson, Ukraine, on the October 1, 2021. The volume presents the contributions to theworkshops afiliated with the ICTERI 2021: the 17th International Conference on ICT in Education, Research, and Industrial Applications. CoSinE (Computer Simulation in Education) is a peer-reviewed international workshop focusing on theory and practice of computer simulation in education. This workshop was founded by Illia O. Teplytskyi (1941-2018), whose pioneering works in the field of computer simulation in education inspires us. CoSinE topics of interest since 2019 [1, 2, 3] are: • Computer simulation in STEM education</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>-</title>
      <p>i</p>
      <p>• AI in education
• Educational data mining and learning analytics
• Learning environments models
• Learning virtualization
• Modelling systems in education</p>
      <p>
        This volume represents the proceedings of the 9th Illia O. Teplytskyi Workshop on Computer
Simulation in Education (CoSinE 2021), held in Kherson, Ukraine, on the October 1, 2021. It
comprises 9 contributed papers [
        <xref ref-type="bibr" rid="ref10 ref11 ref12 ref4 ref5 ref6 ref7 ref8 ref9">4, 5, 6, 7, 8, 9, 10, 11, 12</xref>
        ] that were carefully peer-reviewed and
selected from 50 submissions. Each submission was reviewed by at least 3, and on the average
3.1, program committee members. The accepted papers present the state-of-the-art overview of
successful cases and provides guidelines for future research.
2. CoSinE 2021 Program Committee
• Program Committee Co-Chairs:
– Vadim Ermolayev, Zaporizhzhia National University, Ukraine
– David Esteban, TECHFORCE, Spain
– Arnold Kiv, Ben-Gurion University of the Negev, Israel
– Serhiy Semerikov, Kryvyi Rih State Pedagogical University, Ukraine
– Vladimir Soloviev, Kryvyi Rih State Pedagogical University, Ukraine
– Andrii Striuk, Kryvyi Rih National University, Ukraine
• Program Committee members and additional reviewers:
– Laima Okuneviciute Neverauskiene, Vilnius Gediminas Technical University,
      </p>
      <p>Lithuania
– Liubov Panchenko, National Technical University of Ukraine “Igor Sikorsky Kyiv</p>
      <p>Polytechnic Institute”, Ukraine
– Stamatios Papadakis, University of Crete, Greece
– Olga Pinchuk, Institute of Information Technologies and Learning Tools of the</p>
      <p>NAES of Ukraine, Ukraine
– Oleg Pursky, Kyiv National University of Trade and Economics, Ukraine
– Nina Rizun, Gdańsk University of Technology, Poland
– Zarema Seidametova, Crimean Engineering and Pedagogical University, Ukraine
– Oleksandra Sokolyuk, Institute of Information Technologies and Learning Tools
of the NAES of Ukraine, Ukraine
– Kateryna Vlasenko, Donbas State Engineering Academy, Ukraine
– Longkai Wu, National Institute of Education, Singapore
– Yuliia Yechkalo, Kryvyi Rih National University, Ukraine</p>
    </sec>
    <sec id="sec-2">
      <title>3. CoSinE 2021 workshop overview</title>
      <sec id="sec-2-1">
        <title>3.1. Session I: Modelling systems in education. Educational data mining and learning analytics</title>
        <p>
          The objectives of work “Computational modelling of stochastic processes for learning research”
[
          <xref ref-type="bibr" rid="ref10">10</xref>
          ] by Oleksandr H. Kolgatin (figure 2), Larisa S. Kolgatina and Nadiia S. Ponomareva were to use
computer-based statistical modelling for comparison and systematisation of various approaches
to non-parametric null hypothesis significance testing. Statistical model for simulation of
null hypothesis significance testing has been built for educational purpose. Fisher’s angular
transformation, Chi-square, Mann-Whitney and Fisher’s exact tests were analysed. Appropriate
software has been developed and gave authors possibility to suggest new illustrative materials for
describing the limitations of analysed tests. Learning researches as the method of understanding
inductive statistics have been suggested taking into account that modern personal computers
provide acceptable time of the simulations with high precision. The obtained results showed
low power of the most popular non-parametric tests for small samples. Students can’t analyse
the test power at traditional null hypothesis significance testing, because the real diferences
between samples are unknown. Therefore, it is necessary to change the accents in Ukrainian
statistical education, including PhD studies, from using null hypothesis significance testing to
statistical modelling as a modern and efective method of proving the scientific hypothesises.
This conclusions correlate with observed scientific publications and the recommendation of the
American Statistical Association.
        </p>
        <p>
          This article highlights further research by the authors, begun in [
          <xref ref-type="bibr" rid="ref13 ref14 ref15 ref16 ref17 ref18 ref19 ref20 ref21">13, 14, 15, 16, 17, 18, 19, 20,
21</xref>
          ].
        </p>
        <p>
          The problems of synchronized curriculum shaping are discussed in the paper “A Petri
netbased simulation of synchronized curriculum for IT-specialists’ training” [
          <xref ref-type="bibr" rid="ref7">7</xref>
          ] by Lyudmyla I.
Bilousova and Liudmyla E. Gryzun (figure 3) in the context of the challenges of contemporary
        </p>
        <p>IT specialists’ training in terms of permanent evolution and rapid expansion of computing.
The authors’ technique of the curriculum simulation based on Petri net applying is presented.
The theoretical framework which allowed us to elaborate the technique includes some
didactical fundamentals of curriculum design and academic disciplines structuring as well as the
theoretical basics of Petri nets in terms of their significant facilities for diferent processes
synchronization. The technique of academic disciplines structuring based on frame model of
knowledge representation is covered as a chain of stages which results in the net of disciplines
modules (frames) including the proper learning elements along with preserving and spreading
links between them. In order to solve the problem of synchronization of the learning elements
mastering throughout the academic disciplines with regard of the established links in the frames,
it is ofered the practically-driven approach to curriculum modeling based on Petri nets
simulation. The diferent types of the precedence relations for the learning elements (inputs and
outputs of the frames) were distinguished. Using these precedence relations, we managed to
simulate through Petri nets a learning element itself and the process of its mastering. Using the
operations of Petri nets modifications and learning elements’ connections of diferent types, we
could obtain a model of a curriculum discipline module and finally – a curriculum discipline
model. Built Petri nets models represent all the evolution of the learning elements mastering by
students in the curriculum courses. Synchronization of their mastering is guaranteed by the
rules of Petri net execution and modifications. The peculiarities of the ofered technique are
analyzed. The benefits in terms of applying such a simulation to the building of synchronized
curriculum for IT-specialists training are formulated. The prospects of the research are outlined
in the lines of using the obtained results for special software development.</p>
        <p>
          This article highlights further research by the authors, begun in [
          <xref ref-type="bibr" rid="ref22 ref23 ref24 ref25 ref26 ref27 ref28">22, 23, 24, 25, 26, 27, 28</xref>
          ].
In this paper “The algorithm for knowledge assessment based on the Rusch model” [
          <xref ref-type="bibr" rid="ref5">5</xref>
          ] by
        </p>
        <p>Alexander A. Kostikov (figure 4), Kateryna V. Vlasenko, Iryna V. Lovianova, Sergii V. Volkov
and Evgeny O. Avramov the algorithm for adaptive testing of students’ knowledge in distance
learning and an assessment of its efectiveness in the educational process has been proposed.
The paper provides an overview of the results of the application of modern test theory, a
description and block diagram of the proposed algorithm and the results of its application in the
real educational process. The efectiveness of using this algorithm for the objective assessment
of students’ knowledge has been experimentally shown.</p>
        <p>
          This article highlights further research by the authors, begun in [
          <xref ref-type="bibr" rid="ref29 ref30 ref31 ref32 ref33 ref34 ref35 ref36 ref37 ref38 ref39 ref40 ref41 ref42 ref43 ref44 ref45 ref46">29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46</xref>
          ].
        </p>
        <p>
          Nowadays, there is a wide variety of scientific articles. Due to this fact, it is hard to read and
be familiar with all of them. Also, it is hard for a young scientist to understand the complicated
terms and methods that are used in a specific research domain. This problem was partially solved
by bibliographic management software and other specific software. The article “Ontology-based
learning environment model of scientific studies” [
          <xref ref-type="bibr" rid="ref8">8</xref>
          ] by Roman A. Tarasenko, Stanislav A.
Usenko, Yevhenii B. Shapovalov (figure 5), Viktor B. Shapovalov, Adrian Paschke and Iryna M.
Savchenko is devoted to the development of an approach for structuration and processing sets
of studies using the IT Platform Polyhedron using an ontology-based hierarchical model. In
its structure, the ontological graph is complex because it has additional branches from child
nodes. The basis of our solution was IMRAD which has been represented in the view of nodes.
Those nodes have been connected with specific representations of IMRAD elements. Specific
articles have been represented in the view of leaf nodes. That could help to use the taxonomies
for the structuration of the articles. Each data block is in the form of separate attributes of
the ontological node. The proposed solution allows to obtain structured sets of studies and to
separate their characteristics. Thus, the proposed ontology provides the possibility to view all
methods, measured parameters, etc. of the studies in a graph node structure and use them to
ifnd the studies where they were used.
        </p>
        <p>
          This article highlights further research by the authors, begun in [
          <xref ref-type="bibr" rid="ref47 ref48 ref49 ref50 ref51 ref52 ref53 ref54 ref55 ref56 ref57 ref58">47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58</xref>
          ].
        </p>
      </sec>
      <sec id="sec-2-2">
        <title>3.2. Session II: Learning virtualization. AI in education</title>
        <p>
          Any person, not even a financier, should be knowledgeable in the field of personal finance
management. As a result of the survey, it was concluded that schoolchildren and students
should be interested in the study of personal finance, even within other disciplines in the form
of separate sections. Successful personal experience and knowledge of the teacher in the field of
personal finance is an important component of the efectiveness of the formation of competence
of pupils, students, and adults in the field of personal finance. Business simulators come in
handy here. That is why Dmytro S. Antoniuk (figure 6), Tetiana A. Vakaliuk, Vladyskav V.
Didkivskyi, Oleksandr Yu. Vizghalov, Oksana V. Oliinyk and Valentyn M. Yanchuk, the authors
of article “Using a business simulator with elements of machine learning to develop personal
ifnance management skills” [
          <xref ref-type="bibr" rid="ref4">4</xref>
          ] developed a business simulator for the development of personal
ifnance management skills, which was developed in the form of a web service. The main features
of this simulator, which are presented in diferent sections of the simulator, are considered. It is
shown what skills of personal finance management are developed using this business simulator.
Since this simulator was designed to develop personal finance management skills, an attempt
was made to apply machine-learning elements to make this business simulator work even better.
The proposed simulator can be used in the future to teach the elements of personal finance
management to people who are not suficiently knowledgeable in the field of such finance.
Moreover, the web application can be useful even for school-age children, so the simulator can
complement the educational process within the economic courses not only in higher education
institutions but also in secondary education institutions of Ukraine.
        </p>
        <p>
          This article highlights further research by the authors, begun in [
          <xref ref-type="bibr" rid="ref59 ref60 ref61 ref62 ref63 ref64 ref65 ref66 ref67 ref68 ref69 ref70 ref71 ref72 ref73 ref74 ref75 ref76 ref77 ref78 ref79 ref80 ref81 ref82 ref83 ref84 ref85 ref86 ref87 ref88 ref89 ref90 ref91 ref92 ref93 ref94 ref95 ref96 ref97 ref98">59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
95, 96, 97, 98</xref>
          ].
        </p>
        <p>
          The article “Modeling of the internal certification system of educational resources view or
update” [
          <xref ref-type="bibr" rid="ref6">6</xref>
          ] by Oksana P. Buinytska and Svitlana V. Vasylenko (figure 7) analyzes the research,
which was carried out for several years to model the system of internal certification of
educational resources for blended and e-learning in Higher education. The authors describe the
prerequisites for the internal certification system modeling and the features of recent changes in
the process of internal certification of educational resources, approved by the university
regulations, which is related to the requirements of the situation with COVID-19. The article describes
the current model of the system of internal certification of educational resources, which is based
on the approbation of the pilot model of e-learning courses (hereinafter – ELC) certification. The
key components of the internal certification system are three mandatory expertise (professional,
resource and technical). Based on the results of which, the methodological commission decides
on the quality of educational resources of the ENC and its certification. Expectations from
the current system of internal certification of ELC implementation were justified to a high
degree, in particular, we have the following advantages of their use: the creation of quality
educational resources, expanding access to various categories of participants in the educational
process to educational content; ensuring the individualization of the educational process under
the needs, characteristics and capabilities of learners; improving the quality and eficiency of
the educational process through the use of digital and innovative educational technologies;
ensuring systematic monitoring of the quality of education, implementation of blended and
e-learning in higher education.
        </p>
        <p>
          Behavioral game theory typically investigate diference between game-theoretic solution
and real behavior, one can observe from experiments. The research question, formulated by
Oleksii P. Ignatenko (figure 8) in the article “Strategic learning towards equilibrium. Exploratory
analysis and models” [
          <xref ref-type="bibr" rid="ref12">12</xref>
          ], is how players learn in complex strategic situations which they
never faced before. We examine data from diferent games, played during lectures about game
theory and present findings about players progress in strategic learning while competing with
other players. We proposed four “pick a number” games, all with similar-looking rules but very
diferent properties. These games were introduced (in the body of scientific popular lectures) to
very diferent groups of listeners. We model and analyse data from the experiments to explain
diferent models of thinking, agent can apply in the games. All data gathered are available in
open repository for replication and analysis. Finally, we discuss the findings propose hypothesis
to investigate and formulate open questions for future research.
        </p>
        <p>
          This article highlights further research by the author, begun in [
          <xref ref-type="bibr" rid="ref100 ref99">99, 100</xref>
          ].
        </p>
        <p>
          Nowadays computer modeling and simulation widely use in computer science education.
There are several ways of increasing the eficiency of educational process. One of the such ways
is visualization, other way is flipped classroom concept. The aim of the article “Some ways of
increasing the eficiency of teaching data structures” [
          <xref ref-type="bibr" rid="ref11">11</xref>
          ] by Zarema S. Seidametova (figure 9)
is to present some ways of increasing the eficiency of teaching data structures (hashing, trees)
during Algorithms and data structures course. Author conducted two experiments with four
study groups of two subjects of the Algorithms and Data Structures – (1) Hashing, (2) Trees (BST,
RBT, AVL), – taught second year bachelor students. In the first experiment study groups were
prepared on the subjects (1) and (2) by the same level of teaching technique with or without
visualization tools. Unlike the first experiment, in the second experiment we used flipped
classroom concept for one study group and a standard way of teaching for another group.
        </p>
        <p>
          This article highlights further research by the author, begun in [
          <xref ref-type="bibr" rid="ref101 ref102 ref103 ref104 ref105">101, 102, 103, 104, 105</xref>
          ].
        </p>
        <p>Currently, augmented reality is one of the most actively developing technologies, which has
also found its application in the field of education. Analysis of various publications has confirmed
that AR technology opens up new opportunities for teachers and increases the attractiveness of
learning for students of diferent ages. mobile AR apps allow the student to see a real-world
environment with overlaid or composite virtual objects. This is especially true for young children.</p>
        <p>
          The article “Using augmented reality for early literacy” [
          <xref ref-type="bibr" rid="ref9">9</xref>
          ] by Olena Ie. Piatykop (figure 10),
and Olha I. Pronina, Iryna B. Tymofieieva and Ihor D. Palii identifies the activities that provide
a child’s personal experience using AR technology. Comparison of existing AR applications for
learning the alphabet is given. A new AR application is described, which was developed using
Unity, C#, Vuforia. The developed mobile AR application provides an opportunity to study the
Ukrainian alphabet, the names of numbers, the sounds of animals. Thanks to this application,
the learning process is accompanied by three-dimensional visualization and sounding of each
letter and number. An analysis of a survey of teachers and parents showed that when using
an AR application, the interest and self-eficacy of children in learning letters and numbers
significantly increased. The use of the AR application increased the speed of memorizing the
material and helped to retain the child’s attention while learning a new material.
        </p>
        <p>
          This article highlights further research by the authors, begun in [
          <xref ref-type="bibr" rid="ref106">106</xref>
          ].
        </p>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>4. CoSinE 2021: Conclusion</title>
      <p>CoSinE 2021 workshop at ICTERI 2021 would not have been possible without the support of
many people. We would like to thank all the authors who submitted papers to our workshop
and thus demonstrated their interest in the research problems within our scope (figure 11). We
are also very grateful to the members of our Program Committees for providing timely and
thorough reviews and being cooperative in doing additional review work. We would like to
thank the local organizers of the conference, the steering committee, and the technical support
team for their valuable service and help. Special thanks go to the sponsors of ICTERI 2021
whose financial and technical contributions enabled the materialization of this instance of the
conference and its sub-events. All these people, their devotion, energy, and eficiency, made our
workshop a very interesting and efective scientific forum.</p>
    </sec>
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