Analysis and Summarization of the Experience of Developing Adaptive

Learning Systems in Higher Education

Kateryna P. Osadcha

1 a

, Viacheslav V. Osadchyi

1 b

, Vladyslav S. Kruglyk

1 c

and

Oleg M. Spirin

2,3 d

Bogdan Khmelnitsky Melitopol State Pedagogical University, 20 Hetmanska Str., Melitopol, 72300, Ukraine

University of Educational Management, 52A Sichovykh Striltsiv Str., Kyiv, 04053, Ukraine

Institute for Digitalisation of Education of the National Academy of Educational Sciences of Ukraine, 9 M. Berlynskoho

Str., Kyiv, 04060, Ukraine

Keywords:

Model of Adaptive Learning System, Adaptive Learning, Adaptive Learning System, Higher Education,

Personiﬁcation of Learning, Professional Training, Individualization of Learning.

Abstract:

The article provides a brief analysis and summarization of the existing experience of developing adaptive

learning systems in higher education. Existing models of adaptive learning systems, which are necessary

for the educational process in higher education, are analyzed. Conclusions are made as for the requirements

for the design and modeling of the author’s adaptive system of future specialists’ professional training in a

blended learning environment. The main ones are requirements for the approaches to modeling, types of

adaptation implemented in the system, ways to ensure individualization and personiﬁcation in the process of

both face-to-face learning and learning with the help of information and communication technologies.

1 INTRODUCTION

In terms of socio-economic and evolutionary changes

in the society, science and technology modern edu-

cational process in higher education requires appro-

priate changes (Ryabinova, 2009) and modiﬁcations

of learning strategies. Among promising and rele-

vant technologies in education today there are adap-

tive learning technologies that help the educational

system to adapt to the speciﬁc features and needs of a

student and are typically controlled by the computa-

tional devices, adapting content for different learners’

needs and sometimes preferences (Shute and Zapata-

Rivera, 2007). With the rapid development of the ar-

tiﬁcial intelligence in education (Abuselidze and Ma-

maladze, 2021), adaptive learning system has become

the trend of web-based e-learning system (Chen and

Zhang, 2008).

Research and implementation of adaptive learn-

ing technologies has contributed to the development

of the adaptive learning system. Adaptive learning

https://orcid.org/0000-0003-0653-6423

https://orcid.org/0000-0001-5659-4774

https://orcid.org/0000-0002-5196-7241

https://orcid.org/0000-0002-9594-6602

system is a platform for individual learning, which

uses different techniques of artiﬁcial intelligence to

adapt instruction to the learner’s individual differ-

ences, such as the learning ability, preferences, learn-

ing style and learning goal etc. (Chen and Zhang,

2008).

Since the emergence and development of adap-

tive learning technologies, scientists have proposed

various models of adaptive learning systems: KFS

(Knowledge Flow Structure), which is based on the

concept of knowledge ﬂow (Kurgan, 2013); DCM

(Dynamic Content Model), which uses a concept

map for the organization and presentation of knowl-

edge (Kristensen et al., 2007); model, designed by

Solovov (Solovov, 2010), the main concepts of which

are the educational element, content graph and spec-

iﬁcation of educational elements (de Marcos et al.,

2007); CDCGM (Competency-Driven Content Gen-

eration Model), which is focused on the availability of

all training materials for the electronic training course

developer; this materials are stored in the form of ed-

ucational units related to the competence bank; SHM

(Structural-Hierarchical Model), which provides tools

for describing the didactic structure of educational

units (Silkina and Sokolinsky, 2016). On the other

hand, Oxman and Wong (Oxman and Wong, 2014)

208

Osadcha, K., Osadchyi, V., Kruglyk, V. and Spirin, O.

Analysis and Summarization of the Experience of Developing Adaptive Learning Systems in Higher Education.

DOI: 10.5220/0010930000003364

In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020) - Volume 2, pages 208-215

ISBN: 978-989-758-558-6

prove that the use of adaptive learning in higher ed-

ucation has been slower to develop, and challenges

that likely contributed to slow adoption remain. It

is facilitated by partnerships between publishers and

adaptive learning companies. In 2010, Knewton ex-

panded beyond its initial GMAT prep product to part-

ner with large universities to create adaptive remedial

math education courses. In 2013 Career Education

Corporation partnered with CCKF (international edu-

cation technology ﬁrm) to build 300 adaptive courses

using its adaptive learning system RealizeIt. In 2017,

the University of Central Florida and Colorado Tech-

nical University partnered with RealizeIt to explore

how best to use an adaptive learning platform to in-

crease student success (Dziuban et al., 2017).

Despite the development of adaptive technologies,

a review of previous research works has shown that

a system that combines the capabilities of adaptive

technologies, individualization and personiﬁcation in

the conditions of blended learning in a higher educa-

tional institution has not been modeled and developed

yet. Such a system is needed to improve the train-

ing of future professionals, which is becoming more

technological, student-centered and variable. In or-

der to develop a new adaptive learning system, it is

advisable to analyze and summarize the existing ex-

perience of adaptive learning systems development in

higher education. This is the aim of our research,

oriented for the identiﬁcation of the requirements for

the design and modeling of our own adaptive learning

system for future professionals’ training in a blended

learning environment.

2 METHODS OF THE STUDY

Methods of research:

• methods of speciﬁcation and systematization of

theoretical knowledge were used for the develop-

ment of the research objectives

• methods of analysis and summarization of

psycho-educational, specialized and technical

sources on the use of adaptive learning technolo-

gies in order to identify the structure and charac-

teristics of existing adaptive learning systems and

requirements for our own adaptive system of in-

dividualization and personiﬁcation of future pro-

fessionals’ training in a blended learning environ-

ment.

3 RESULTS

3.1 Analysis of Existing Adaptive

Systems in Higher Education

Using the search tools of Google Scholar, ERIC, Web

of Science and Scopus, we were searching for sci-

entiﬁc papers on the development of adaptive learn-

ing systems which could be used in higher education.

We highlighted those that were publicly available:

60,462 – in ERIC, 92 – in Web of Science, 183 – in

Scopus. Of the 2,780,000 articles in Google Scholar,

we singled out articles by Russian and Ukrainian au-

thors. We analyzed studies representing the devel-

opment of adaptive learning systems (platforms) in

higher education.

Burdaev (Burdaev, 2006) proposed an adaptive

system EOS “KARKAS”, which is aimed at training

and testing of students of economic specialties. It in-

cludes the following components: an output machine

to provide training; output machine for adaptive test-

ing; learner error analyzer. This system contains a

number of models: 1) a model of learning, character-

ized by the following parameters: learning objectives;

types of errors occurring during the assessment tasks

execution; time of the assessment tasks execution;

need for assistance while executing a task; 2) models

of the learners, characterized by the following param-

eters: psychological type of students’ behavior; level

of students’ intellectual development; level of readi-

ness for the subject under research; motivation. The

adaptability of learning in EOS “KARKAS” is real-

ized in the fact that at each checkpoint a selection of

the learning model takes place depending on the level

of learner’s knowledge. As a result, such parameters

as sequence, depth and forms of content presentation

are modiﬁed.

The structure of Pedagogically Adaptive Learning

System based on Learning Styles, designed by Sia-

daty and Taghiyareh (Siadaty and Taghiyareh, 2007),

includes the domain knowledge module, the learner

model, the pedagogical module and the interface

module (ﬁgure 1).

The domain model is a knowledge representation

of the materials that the learner has to learn and in-

cludes a set of domain concepts such as facts, lessons

and problems forming a kind of semantic network.

The learner model is a hybrid model, i.e., it consists

of a stereotype model (which classiﬁes the learners

based on their entry behaviors or characteristics) and

an overlay model (which is used to represent users’

knowledge of the concepts of the subject domain).

The pedagogical module assumes responsibility for

making decisions about what will be learned, how it

Analysis and Summarization of the Experience of Developing Adaptive Learning Systems in Higher Education

209

Figure 1: Architecture of the system (Siadaty and Taghiyareh, 2007).

will be learned, when it will be learned. The inter-

face module delivers the personalized contents to the

learners and receives their feedback as well.

The adaptive learning system, proposed by Chen

and Zhang (Chen and Zhang, 2008), is oriented for

learning style and cognitive state. Its architecture is

comprised of the media space, domain model, learner

model, instruction model, adaptive model and user

interface. Media space includes the instructional re-

sources database (all kinds of teaching materials, such

as text, picture, audio, video, cartoon, etc.) and in-

structional resources description models (teaching re-

sources by SCORM standard). Domain Model is

the repository of storing and structuring the instruc-

tional content in the particular domain, such as a

course. Domain Model includes the learning objec-

tives hierarchy and domain ontology. Learner Model

is used to provide a foundation for diagnosing the

learning process, providing the adaptive learning sup-

port for learners. Instruction Model is used to simu-

late the teacher’s teaching strategies; it stores the spe-

ciﬁc teaching rules. Adaptive model stores the adap-

tive rules of the system, including the content adapta-

tion rules and adaptive navigation support rules. User

Interface provides the interaction function between

learners and adaptive learning system.

The adaptive distance learning system, proposed

by Gorohovskiy and Troyanovskaya (Gorohovskiy

and Troyanovskaya, 2015), includes the following

modules: module for collecting data on student’s ac-

tivities (to provide primary data on direct and indirect

assessment); module for issuing methodical materi-

als, which directly displays a lecture or practical ma-

terials; automatic module for data processing and is-

suance of material for processing primary evaluation

data and appropriate selection of training materials

and assessment elements (ﬁgure 2). In this compo-

sition, it suits the peculiarities of student’s perception

of educational material.

Red (Red, 2010) described RATOS-AI, an intelli-

gent adaptive system for the development and main-

tenance of distance learning courses, consisting of

the following elements: 1) module for each role

of a teacher (teacher-methodologist module, teacher-

consultant or tutor module, teacher-designer of train-

ing courses module); 2) core IASDL of RASOS-AI;

3) four knowledge bases (subject area, separate aca-

demic course, reference model of knowledge, current

model of pharmacist’s knowledge), database of train-

ing protocols, database of electronic publications and

database of training scenarios; 4) information sys-

tem of monitoring of learning; 5) repository of edu-

cational elements; 6) training system and assessment

system module; 7) systems of communication and ac-

cess to the single information space of University, as

well as the subsystem “Dean’s Ofﬁce”.

The adaptive system of distance learning and

knowledge assessment EduPRO, presented by Fe-

doruk (Fedoruk, 2010), includes a model of lecture

material structuring, model of adaptive testing and a

model of decision-making on transitions between lev-

els of complexity in the adaptive testing system. The

system makes it possible to organize the process of

individualized learning, allowing teachers to form an

individual structure of educational material and iden-

tify the moment of students’ readiness for the transi-

tion to a more complicated level of material.

Information and training system ICT PROFF

(Huang and Shiu, 2012) uses modiﬁed psychologi-

cal tests adapted to the computer procedure of inter-

viewing respondents with automatic calculation of the

coefﬁcients of personalized models of learning mate-

AET 2020 - Symposium on Advances in Educational Technology

210

Figure 2: Technological structure of adaptive distance learning system (Gorohovskiy and Troyanovskaya, 2015).

rial and subsequent calculation of external support of

the learning process. ICT PROFF system basically

uses a modern intelligent CAD software environment

MVTU version 3.0, designed for detailed analysis and

study of dynamic processes in technical, economic

and social systems. The developed adaptive system

of personalized professional training of students al-

lows building the predicted trajectories of knowledge

mastering for each student that can be used for the

formation of homogeneous educational groups.

User-centric adaptive learning system was de-

signed by Huang and Shiu (Huang and Shiu, 2012).

It uses sequential pattern mining to construct adaptive

learning paths based on users’ collective intelligence

and employs Item Response Theory (IRT) with col-

laborative voting approach to estimate learners’ abili-

ties for recommending adaptive materials. Such adap-

tive learning, which is oriented for the user, is com-

parable to expert-designed learning and learners are

more satisﬁed and learn efﬁciently. The system archi-

tecture is illustrated in ﬁgure 3.

Vlasenko (Vlasenko, 2014) has described an

adaptive distance learning system in the ﬁeld of in-

formation technology, which is based on the basic

components used in existing distance learning sys-

tems, namely: blocks (diagnostics of the initial level

of knowledge, formulation of learning objectives, de-

sign and correction of curriculum, testing, model de-

sign, formation of educational elements, correction of

model, assessment of achievements); databases (per-

sonal test tasks, entry-level test knowledge tasks, test

tasks to assess learning outcomes), models (learner

model, adaptation model). Adaptation of the system

to the learner is carried out on the basis of a model

which, in addition to standard parameters, includes

such parameters as the degree of learning outcomes

achievement, system of learner’s advantages, individ-

ual curriculum, etc. Adaptation at the stage of plan-

ning teaching is carried out by developing a curricu-

lum that, on the one hand, meets learner’s needs and

preferences and, on the other hand, provides training

which is appropriate to the competency model and

therefore meets the labor market requirements.

Yasuda et al. (Yasuda et al., 2015) offered adap-

Analysis and Summarization of the Experience of Developing Adaptive Learning Systems in Higher Education

211

217

5. Post-test stage (Steps 18~21): If the learning-path agent senses that the learner has already finished the entire

learning path, it notifies the test agent to provide post-test questions for the learner.

Figure 1. System architecture and operation procedure

Adaptive navigation support

The system collects user-created teaching materials from Web pages and blogs and divides these materials into

concept units. When users share their knowledge on the Web, they usually arrange concept units in order, like a list

or a catalog, based on their cognitions about domain knowledge. Each user-created teaching material presents a

concept sequence, which represents the user’s notion of learning order. The system discovers the concept sequences

that frequently occur in user-created materials. Thus, frequent concept sequences are collaboratively decided by

Internet users. The discovered sequential patterns are a kind of collective intelligence and are used to support

adaptive navigation.

Concept sequences analysis

The following example demonstrates the way to find concept-sequence patterns. Assume that five materials about

C++ programming are collected; then, the frequent concept sequences are generated by the following steps:

1. The concept order in each material is presented as a sequence. Table 1 shows the concept sequences in the five

materials.

2. Find frequent 1-itemset in which all the items’ support levels are higher than the threshold. In this example, the

minimum support is set to 40%, which means that the concept which appears at least twice in the five materials

will be a frequent concept.

3. If a concept is not included in frequent 1-itemset, it can be deleted; on the contrary, frequent concepts are

mapped to assigned numbers for analysis (see Table 2). Table 3 shows the concept sequences after mapping.

4. Use frequent 1-itemset to find other frequent sequences with different lengths. The GSP algorithm is employed

to generate candidate sequences and frequent sequences. Finally, the algorithm returns the maximal sequences.

Table 4 shows all frequent sequences and the sequences < 1 5 > and < 1 2 3 4 > are the maximal sequences.

Table 1. An example of concept sequences

Material ID Concept sequence

1 < data type, overload >

2 < introduction, data type, process control, class & object, function >

3 < data type, string & reference, class & object >

4 < data type, process control, class & object, function, overload >

5 < overload >

Figure 3: User-centric adaptive learning system architecture and operation procedure (Huang and Shiu, 2012).

tive learning system using a Bayesian network (ﬁg-

ure 4). The system has 2 modes: testing mode and

learning mode. The testing mode gauges learners’

understanding in each course unit using the Expected

Value of Network Information (EVINI)-based adap-

tive testing scheme. In the proposed system, the learn-

ing mode assigns each learner drills on course units

that the learner is not good at. The Bayesian net

framework is used to calculate expected value of net-

work information. The conﬁguration of the proposed

system includes such structural elements: the WEB

application server that runs HTML5-based drill con-

tents for both modes; Bayesian net server that infers

the probability that the learner has understood each

of the not-yet-set units, by examining the accuracy

of previous answers; the database server that stores

learners’ logs, which contain each learner’s drill an-

swers.

The developers included the following elements

into the adaptive information learning system (Fe-

dusenko et al., 2017): a subsystem of learning (sub-

system of working with D-graph, subsystem of work-

ing with student model), subsystem of knowledge as-

sessment (subsystem of task design, subsystem of as-

sessment), knowledge base and database. Its use al-

lows increasing the efﬁciency and quality of educa-

tion by selecting an individual learning path for each

student.

To study a foreign language, an adaptive auto-

mated system “Arcturus” was developed (Skliarova,

2016). It solves a number of tasks related to the au-

tomation of the processes of foreign language com-

petence development. The system has the following

features: differentiation of its elements according to

the level of complexity; automation of the process of

test tasks design with different levels of complexity;

multi-criteria methods for the evaluation of education

quality; methods for evaluating student’s psychophys-

ical characteristics while learning with the help of this

system; tools for modeling the process of student’s in-

dividual path in learning a foreign language; methods

for enhancing the quality of optimal control of the in-

dividual learning path; tools of control ﬂow statement

of educational process of system; development of al-

gorithm of system functioning.

The system, proposed by Shershneva et al. (Sher-

shneva et al., 2018), is aimed at teaching mathematics.

This system consists of a subject area module, user

model (information about student, which is needed

to adapt educational content to his or her individ-

ual characteristics and monitor the learning process

in the electronic environment), adaptation model (au-

tomated navigation system and adaptation of educa-

tional content based on learner’s individual character-

istics), and model of learning outcomes assessment

(identiﬁcation of the level of student’s subject com-

petence through the assessment of all its components)

(ﬁgure 5). The authors of this system state that it is a

universal system and it can serve as a basis for the or-

ganization of adaptive learning in the electronic envi-

ronment not only in the ﬁeld of mathematics but also

in other disciplines of educational programs in vari-

ous ﬁelds of training in secondary, higher and extra-

curriculum education.

According to Toktarova and Fedorova (Toktarova

and Fedorova, 2020) in order to train students in

mathematics in the information-educational environ-

ment it is recommended to introduce such adaptive

system which is based on the adaptation of train-

ing to students’ individual features, management of

learning process in the information-educational en-

vironment, use of mobile devices for training, and a

AET 2020 - Symposium on Advances in Educational Technology

212

Figure 4: Conceptual model of adaptive information system (Yasuda et al., 2015).

Figure 5: Structural scheme of the adaptive system of web-based teaching (Shershneva et al., 2018).

set of educational-methodological and technological

means.

3.2 Identiﬁcation of Requirements for

an Adaptive System of

Individualization and

Personalization of Future

Specialists’ Professional Training in

a Blended Learning Environment

Based on the analysis of the above mentioned models

of adaptive systems, we have identiﬁed the follow-

ing requirements for an adaptive system of individual-

ization and personalization of future specialists’ pro-

fessional training in a blended learning environment.

The system should:

1) be based on polyparadigmatic and systematic ap-

proaches to its modeling, which involve the use

of an open cluster of approaches to learning, their

integrated application in a structure of intercon-

nected subsystems;

2) provide the adaptation of educational materi-

als, learning outcomes monitoring, devices (PC,

smartphone, tablet computer), and face-to-face

classes to the students’ individual characteristics;

3) enhance a learner-centered approach in the pro-

cess of both face-to-face learning and learning

with the help of information and communication

technologies; in order to implement this system

the following should be done: automated study of

student’s individual features, tutoring and support

of student’s individual educational program, indi-

vidualization of learning process, development of

student’s individual features and his or her new

characteristics according to personal educational

needs, monitoring and recording of student’s in-

dividual progress;

4) provide the personalization of the electronic edu-

cational environment as well as learning environ-

Analysis and Summarization of the Experience of Developing Adaptive Learning Systems in Higher Education

213

ment;

5) include both modern educational technologies (in-

teractive methods, intensiﬁcation, project work

and creative learning, etc.) and information

and communication technologies (distance learn-

ing technologies, analysis and processing of big

amount of data) for future specialists’ profes-

sional training in a higher educational establish-

ment;

6) include subsystems that characterize certain areas

of future professionals’ training (adaptation, indi-

vidualization, personalization of training) and re-

ﬂect the mixed nature of training;

7) be implemented as a working prototype of an

adaptive system for different groups of stakehold-

ers and use an adaptive system for individualiza-

tion and personalization of future professionals’

training in a blended learning environment.

Based on the analysis of the existing adaptive

technologies (Osadcha et al., 2020) and ICT for indi-

vidualization (Kruglik and Chorna, 2020) and person-

iﬁcation (Osadchyi and Krasheninnik, 2020) of train-

ing and in order to implement the requirements for

the system, we can conclude that it is appropriate to

implement the following learning tools in an adaptive

system of individualization and personalization of fu-

ture specialists’ professional training in the context

of blended learning: Moodle platform and plug-ins

for adaptive learning, capabilities and properties of

Moodle for the implementation of individualization

of learning (competence module and progress block,

tools for imposing necessary restrictions on learning

elements, means of multicriteria assessment, tools for

multivariate presentation of educational information,

etc.), a set of information and communication tech-

nologies and modern technical means of learning to

ensure the personiﬁcation of learning. This will al-

low us to design an adaptive system of individualiza-

tion and personalization of future professionals’ pro-

fessional training in a blended learning environment,

which will consequently promote the impact of dis-

tance technologies on the improvement and intensiﬁ-

cation of learning in higher educational institutions.

4 CONCLUSIONS

Analysis of adaptive learning systems and their mod-

els showed that these solutions have a narrow applied

character (teaching mathematics, languages or teach-

ing students of a particular specialty). They do not

contain all necessary elements for educational pro-

cess organization or do not have the structural ele-

ments and functional features required by teachers

and students in the modern educational process. We

have also generalized the requirements for the adap-

tive system of individualization and personalization

of future specialists’ professional training in the con-

ditions of blended learning. These requirements are

as follows: system modeling has to be done accord-

ing to polyparadigmatic and systematic approaches;

it should take into account the tasks of adaptation,

individualization and personalization of future pro-

fessionals’ training; the system should be structured

in accordance with the functional purpose of its ele-

ments; the system should be implemented in the form

of a working prototype based on the Moodle platform

tools. The adaptive learning system should be easy

to use in the process of learning, have an intuitive in-

terface and appropriate structure, as well as the tools

which are necessary for future professionals’ effective

training.

ACKNOWLEDGEMENTS

This research was funded by a grant from the Ministry

of Education and Science of Ukraine (0120U101970).

REFERENCES

Abuselidze, G. and Mamaladze, L. (2021). The impact of

artiﬁcial intelligence on employment before and dur-

ing pandemic: A comparative analysis. Journal of

Physics: Conference Series, 1840(1):012040.

Burdaev, V. P. (2006). Adaptive learning system in

EOS “KARKAS”. Artiﬁcial Intelligence, (3):458–

467. http://iai.donetsk.ua/public/JournalAI 2006 3/

Razdel6/01 Burdaev.pdf.

Chen, S. and Zhang, J. (2008). The adaptive learning sys-

tem based on learning style and cognitive state. In

2008 International Symposium on Knowledge Acqui-

sition and Modeling, pages 302–306.

de Marcos, L., Pages, C., Martinez, J.-J., and Gutierrez,

J.-A. (2007). Competency-based learning object se-

quencing using particle swarms. In 19th IEEE In-

ternational Conference on Tools with Artiﬁcial Intel-

ligence(ICTAI 2007), volume 2, pages 111–116.

Dziuban, C., Howlin, C., Johnson, C., and Moskal,

P. (2017). An adaptive learning partnership.

https://er.educause.edu/articles/2017/12/an-adaptive-

learning-partnership.

Fedoruk, P. (2010). The use of the EduPRO system for

adaptive learning process organizing. In Proceedings

of the 8th IASTED International Conference on Web-

Based Education, WBE 2010, pages 7–11.

Fedusenko, O. V., Fedusenko, A. O., and Domanetska, I. M.

(2017). Conceptual model of adaptive learning infor-

AET 2020 - Symposium on Advances in Educational Technology

214

mation system. Upravlinnia rozvytkom skladnykh sys-

tem, 32:86–90.

Gorohovskiy, A. and Troyanovskaya, T. (2015). Informa-

tion technology for building the adaptive system of

distance learning. Scientiﬁc Works of Vinnytsia Na-

tional Technical University, (2). https://works.vntu.

edu.ua/index.php/works/article/view/140.

Huang, S.-L. and Shiu, J.-H. (2012). A User-Centric Adap-

tive Learning System for E-Learning 2.0. Journal

of Educational Technology & Society, 15(3):214–225.

https://www.ds.unipi.gr/et&s/journals/15 3/16.pdf.

Kristensen, T., Lamo, Y., Mughal, K., Tekle, K. M., and

Bottu, A. K. (2007). Towards a dynamic, content

based e-learning platform. In Proceedings of the 10th

IASTED International Conference on Computers and

Advanced Technology in Education, CATE ’07, page

107–114, USA. ACTA Press.

Kruglik, V. S. and Chorna, A. V. (2020). Using the Moodle

platform to individualize the training of future profes-

sionals in a blended learning environment. In Informa-

tion technologies in education, science and technol-

ogy: theses add. V International Scientiﬁc-practical

Conf. (Cherkasy, May 21-23, 2020), pages 159–160.

Cherkasy State Technological University, Cherkasy.

Kurgan, G. S. (2013). Cloud technologies of inter-

net education based on the knowledge repre-

sentation model. Bulletin of the Buryat State

University. Mathematics, computer science,

(9). https://cyberleninka.ru/article/n/oblachnye-

tehnologii-internet-obrazovaniya-na-osnove-kfs-

modeli-predstavleniya-znaniy/viewer.

Osadcha, K., Osadchyi, V., Semerikov, S., Chemerys, H.,

and Chorna, A. (2020). The review of the adaptive

learning systems for the formation of individual ed-

ucational trajectory. CEUR Workshop Proceedings,

2732:547–558.

Osadchyi, V. V. and Krasheninnik, I. V. (2020). LMS Moo-

dle as a tool for personalizing the training of future

professionals. In Information technologies in educa-

tion, science and technology: theses add. V Interna-

tional. scientiﬁc-practical Conf. (Cherkasy, May 21-

23, 2020), pages 169–170. Cherkasy State Technolog-

ical University, Cherkasy.

Oxman, S. and Wong, W. (2014). White paper: Adap-

tive learning systems. Integrated Education Solutions,

pages 6–7. https://pdfcoffee.com/qdownload/dvx-

adaptive-learning-white-paper-pdf-free.html.

Red, O. A. (2010). Intelligent adaptive distance learning

system RATOS-AI (part 2.) Conceptual model of the

system for training pharmacists. Zaporozhye Medical

Journal, (12, №4):28–36.

Ryabinova, E. N. (2009). Adaptive system of personalized

professional training of students of technical universi-

ties. Mashinostroenie, Moscow.

Shershneva, V. A., Vainshtein, Y. V., and Kochetkova, T. O.

(2018). Adaptive system of web-based teaching. Pro-

gram Systems: Theory and Applications, 9(4):179–

197.

Shute, V. J. and Zapata-Rivera, D. (2007). Adap-

tive Technologies, chapter 24. Routledge.

https://myweb.fsu.edu/vshute/pdf/shute

Siadaty, M. and Taghiyareh, F. (2007). PALS2: Pedagog-

ically Adaptive Learning System based on Learning

Styles. In Seventh IEEE International Conference

on Advanced Learning Technologies (ICALT 2007),

pages 616–618.

Silkina, N. S. and Sokolinsky, L. B. (2016). Survey of

adaptive e-learning models. Bulletin of the South Ural

State University. Series: Computational Mathematics

and Software Engineering, 5(4):61–76.

Skliarova, O. N. (2016). Adaptive automated learning sys-

tem for learning a foreign language “Aktur”: Method-

ology and requirements. In Regularities and Trends

in the Development of Science in Modern Society:

Proceedings of International Conference, volume 2,

pages 159–162, Ufa. AETERNA.

Solovov, A. V. (2010). Mathematical modeling of e-

learning content, navigation and processes in the con-

text of international standards and speciﬁcations. Re-

search Center for the Problems of the Quality of Pro-

fessional Training, Moscow.

Toktarova, V. I. and Fedorova, S. N. (2020). Theoretical

and methodological bases for designing the concept

of the adaptive system of mathematical training of stu-

dents in the conditions of electronic educational envi-

ronment of the higher educational institution. Siberian

pedagogical journal, (3):74–83.

Vlasenko, A. A. (2014). Development of an adaptive dis-

tance learning system in the ﬁeld of information tech-

nology. PhD thesis.

Yasuda, K., Kawashima, H., Hata, Y., and Kimura, H.

(2015). Implementation of an adaptive learning

system using a bayesian network. In The 11th

International Conference Mobile Learning 2015,

pages 157–159. http://www.iadisportal.org/digital-

library/implementation-of-an-adaptive-learning-

system-using-a-bayesian-network.

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