=Paper=
{{Paper
|id=Vol-2732/20200559
|storemode=property
|title=Personalized and Adaptive ICT-Enhanced Learning: A Brief Review of
Research from 2010 to 2019
|pdfUrl=https://ceur-ws.org/Vol-2732/20200559.pdf
|volume=Vol-2732
|authors=Viacheslav Osadchyi,Iryna Krasheninnik,Oleg Spirin,Serhii Koniukhov,Tetiana Diuzhykova
|dblpUrl=https://dblp.org/rec/conf/icteri/OsadchyiKSKD20
}}
==Personalized and Adaptive ICT-Enhanced Learning: A Brief Review of
Research from 2010 to 2019==
https://ceur-ws.org/Vol-2732/20200559.pdf
Personalized and Adaptive ICT-Enhanced Learning:
A Brief Review of Research from 2010 to 2019
Viacheslav Osadchyi1[0000-0001-5659-4774], Iryna Krasheninnik1[0000-0001-6689-3209],
Oleg Spirin2[0000-0002-9594-6602], Serhii Koniukhov1[0000-0002-1925-3425],
Tetiana Diuzhykova1[0000-0002-8163-3816]
1 Bogdan Khmelnitsky Melitopol State Pedagogical University 20, Hetmanska Street,
Melitopol, Ukraine
(osadchyi, irina_kr, konukhov}@mdpu.org.ua,
dyuzhikova1970@gmail.com
2 University Of Educational Management of NAES of Ukraine 52 A, Sichovykh Striltsiv Street,
Kyiv, Ukraine
oleg.spirin@gmail.com
Abstract. Personalized learning is an up-to-date trend of formal and informal
education development. Its main peculiarity is the maximum consideration of
the person's educational needs. Nowadays, personalized learning involves de-
velopment of student model based on personal characteristics; customized
learning content, as well as intellectual information and communication tech-
nologies. These approach is considered as adaptive learning. Research results in
the field of personalized and adaptive learning are presented in numerous publi-
cations. Thus, it was decided to perform the search in Scopus and Web of Sci-
ence Core Collection, as well as the electronic libraries of the Institute of Elec-
trical and Electronics Engineers and Association for Computing Machinery.
The study consisted of two stages: 1) search by a set of key phrases; 2) search
by a custom search query. The results of the analysis of the generated sample by
years of publication, countries of origin of authors, number of citations are pre-
sented in tables and diagrams. Moreover, the review of some significant publi-
cations is given, and main areas of further studies are detected such as, the ex-
amining of teachers' experience in the field of use adaptive learning systems.
Keywords: personalized learning, adaptive learning, review.
1 Introduction
Increasing attention to a person-centered learning approach, widespread use of infor-
mation and communication technologies in formal and non-formal education become
a factor of the intensive research in the field of learning individualization and personi-
fication. The scientific results are reflected in numerous publications. In particular,
methodological approaches and aspects of using information and communication
technologies for personalized learning are presented in [1; 2; 3; 4; 5; 6].
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
As Turčáni and Balogh mention, personalized learning recognizes learners' diversi-
ty, cognitive and physical differences and the overall individuality. It includes various
learning styles and approaches: from focused on the educational content to focused on
supporting the learners by communication, discussion, cooperation [7, p. 47-48].
So, it is important to study the current state of research of the problem by review-
ing literature sources. It is a common method of analysis, so there are a lot of articles
presenting the reviews of publications on various aspects of personalized and adaptive
learning.
Akbulut and Cardak [8] made a content analysis of studies describing adaptive ed-
ucational hypermedia (AEH) with a focus on learning styles. They searched publica-
tions from 2000 to 2011 in several electronic databases, namely Ulrich’s Periodicals
Directory, ISI Web of Knowledge, EBSCOhost Web, SpringerLink, ERIC, Google
Scholar and others. For research purpose different key words and phrases were used,
eg. "adaptive/adaptable e-learning”, "adaptation", "personalized e-learning", "learning
styles". Authors selected 70 papers of such types as peer-reviewed articles, full-text
proceedings of international conferences, symposia and workshops, and dissertations
in English. These publications were classified under several categories, namely Publi-
cation type, Main focus, Purpose, Study nature, Variables used for adaptivity, Learn-
ing style model, Student modeling, Tool for modeling, Tools for dynamic modeling,
Research settings, Participants, Type of empirical studies, Data collection tools. As a
result, authors identified some expectations of AEH using in education.
We examined some other papers dedicated to literature review on personalized
learning and adaptive learning systems. A systematic literature reviews were conduct-
ed to study individual differences accommodating in adaptive learning systems
(Nakic, Granic, and Glavinic [9]); using competence-based recommender systems
(Yago, Clemente, and Rodriguez [10]); personalized electronic learning models as a
combination of learning theories, techniques and tools (Jando et al. [11]); characteris-
tics, applications, and evaluation methods of intelligent tutoring systems
(Mousavinasab et al. [12]); personal traits in adaptive learning environment and
learners' models (Normadhi et al. [13]); challenges in the online component of blend-
ed learning (Rasheed, Kamsin, and Abdullah [14]).
The main motivation which encourages us to conduct this study is necessity to de-
fine methodological foundations and appropriate means of development of personal-
ized adaptive learning system for professional training at universities, within the re-
search on request of the Ministry of Education and Science of Ukraine, registration
number 0120U101970. To achieve this goal needs to select pool of theoretical and
applied papers.
Furthermore, there are two research questions in our study. First, "are issues of
personalized and adaptive ICT-enhanced learning up-to-date?". Second, "what are the
ICT-means for personalization of learning?". This study was conducted through a
review relating to personalized and adaptive ICT-enhanced learning of papers pub-
lished from 2010 to 2019.
2 Methodology
In the course of our study, we relied on the methodological foundations of the litera-
ture review as a research method outlined in [15; 16; 17], as well as materials of sci-
entific publications Akbulut and Cardak [8], Nakic, Granic, and Glavinic [9], Yago,
Clemente, and Rodriguez [10], Jando, Meyliana, Hidayanto, Prabowo, Warnars, and
Sasmoko [11], Afini Normadhi, Shuib, Md Nasir, Bimba, Idris, and Balakrishnan
[13], Rasheed, Kamsin, and Abdullah [14].
We analyzed the scientific publications in the abstract and citation databases Sco-
pus (https://www.scopus.com) and the Web of Science Core Collection
(www.webofknowledge.com), as well as the libraries of the Institute of Electrical and
Electronics Engineers (IEEE, https://ieeexplore.ieee.org) and the Association for
Computing Machinery (ACM, https://dl.acm.org/). These electronic resources were
selected since they contain international scientific sources of high impact-factor. In
order to select the most up-to-date and thorough research, it was decided to introduce
additional restrictions, namely: articles in periodicals and proceedings of scientific
conferences, as well as books and parts of books published in 2010-2019.
Web services of Scopus and Web of Science Core Collection abstract and citation
databases provide a strong search functionality. In particular, we used filtration by
subject area / category. Since our research was mainly related to the educational pro-
cess, the subject area "Social Sciences" was selected for search in Scopus, and the
category "Education educational research" in Web of Science Core Collection. The
search was performed under the Title, Abstract and Keyword fields.
On the first stage, in order to determine the general level of scientific interest in the
field of adaptive and personalized learning, we conducted a search in three categories,
which can be defined as: "personalization of learning", "adaptation of learning",
"information systems for learning". In the process of keyword selection, we relied on
works [9; 13; 18]. Three key phrases were selected for each area, namely:
• "personalization of learning": "personalized learning", "individual learning", "di-
rect instruction";
• "adaptation of learning": "personalized e-learning", "adaptive learning", "intelli-
gent tutoring";
• "information systems for learning": "personalized learning environment", "adaptive
learning system", "intelligent tutoring system".
On the second stage, for the selecting of publications, which present the results of
experimental studies in the field of adaptive learning systems, we composed a search
query consisting of four parts, combined by the logical operator AND:
1. keywords to select publications that address adaptive and personalized learning:
(adapt* OR personali*);
2. keywords to select education related publications: (education* OR "tutoring" OR
instruction* OR course*);
3. keywords to select publications related to educational information systems:
("learning environment" OR "learning system" OR "tutoring system");
4. keywords to select publications that show the results of surveying, questionnaire,
and empirical studies and in the field of using adaptive learning systems: (evaluat*
OR empiric* OR experiment* OR survey* OR questionnaire).
Given the specifics of the query language of databases, as well as the additional
limitations pointed out, search queries were as follows:
for search in Scopus:
TITLE-ABS-KEY (adapt* OR personali*) AND TITLE-ABS-KEY (education* OR
"tutoring" OR instruction* OR course*) AND TITLE-ABS-KEY ("learning environ-
ment" OR "learning system" OR "tutoring system") AND TITLE-ABS-KEY (evalu-
at* OR empiric* OR experiment* OR survey* OR questionnaire) AND (LIMIT-TO
(DOCTYPE,"ar") OR LIMIT-TO (DOCTYPE,"cp") OR LIMIT-TO
(DOCTYPE,"ch") OR LIMIT-TO (DOCTYPE,"bk")) AND (LIMIT-TO
(SUBJAREA,"SOCI") OR EXCLUDE (SUBJAREA,"MEDI") OR EXCLUDE
(SUBJAREA,"HEAL") ) AND (LIMIT-TO (PUBYEAR,2019) OR LIMIT-TO
(PUBYEAR,2018) OR LIMIT-TO (PUBYEAR,2017) OR LIMIT-TO
(PUBYEAR,2016) OR LIMIT-TO (PUBYEAR,2015) OR LIMIT-TO
(PUBYEAR,2014) OR LIMIT-TO (PUBYEAR,2013) OR LIMIT-TO
(PUBYEAR,2012) OR LIMIT-TO (PUBYEAR,2011) OR LIMIT-TO
(PUBYEAR,2010) )
for search in Web of Science Core Collection:
TS=((adapt* OR personali*) AND (education* OR "tutoring" OR instruction* OR
course*) AND ("learning environment" OR "learning system" OR "tutoring system")
AND (evaluat* OR empiric* OR experiment* OR survey* OR questionnaire) )
Refined by: WEB OF SCIENCE CATEGORIES: (EDUCATION EDUCATIONAL
RESEARCH) AND DOCUMENT TYPES: (ARTICLE OR BOOK CHAPTER OR
PROCEEDINGS PAPER)
Timespan: 2010-2019. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-
SSH, BKCI-S, BKCI-SSH, ESCI
Search results are presented through tables and diagrams.
3 Research Results and Discussion
To answer the first research question, "are issues of personalized and adaptive ICT-
enhanced learning up-to-date?", the search in electronic libraries and databases was
done.
Analysis of data collected through selecting publications in abstract and citation
databases, as well as electronic libraries by the key phrases "personalized learning",
"individual learning", "direct instruction", "personalized e-learning", "adaptive
learning", "intelligent tutoring", "personalized learning environment", "adaptive
learning system", "intelligent tutoring system", leads to the conclusion that over the
last decade, researchers have paid considerable attention to the theoretical and practi-
cal aspects of personalized and adaptive learning, in particular to using of information
and communication technologies for provision of education adaptability (see Table 1).
Table 1. Generalization of search results.
Resource
Web of Sci-
Key phrases IEEE Xplore® ACM Digital
Scopus ence Core
Digital Library Library
Collection
personalized learning 411 550 720 448
individual learning 399 709 1056 596
direct instruction 26 171 505 312
personalized e-learning 70 29 65 28
adaptive learning 1351 972 782 385
intelligent tutoring 860 973 916 523
personalized learning
22 31 75 25
environment
adaptive learning system 83 46 139 45
intelligent tutoring system 291 348 859 235
The data got from the Scopus and Web of Science Core Collection abstract and cita-
tion databases reveal the dynamics of scientists' publication activity in the field of
personalized and adaptive learning by years. The results are given in the Table 2 and
Fig. 1-3. We can state a stable scientific interest for these issues. In particular, the
number of publications on most of the key phrases analyzed significantly increased in
2014.
Table 2. Distribution of publications on problems of personalized and adaptive learning by
years.
Key phrase 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Web of Science Core Collection
personalized learning 19 15 24 27 23 49 88 86 72 45
individual learning 52 43 46 50 40 82 76 82 71 54
direct instruction 15 21 19 20 17 46 35 42 53 44
personalized e-learning 2 2 2 2 4 3 3 2 5 3
adaptive learning 25 20 18 28 24 35 65 68 55 47
intelligent tutoring 45 24 29 42 41 45 99 79 54 65
personalized learning
2 1 1 2 1 2 5 4 4 3
environment
adaptive learning system 0 3 2 4 1 7 8 2 10 8
intelligent tutoring sys-
22 8 13 18 14 27 47 31 24 31
tem
Scopus
personalized learning 46 35 55 50 48 78 75 91 110 132
individual learning 97 99 100 97 96 124 95 108 112 128
direct instruction 40 31 49 46 38 60 44 50 65 82
personalized e-learning 7 5 6 8 6 5 6 3 8 11
adaptive learning 63 55 52 63 57 70 99 93 123 107
Key phrase 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
intelligent tutoring 92 68 81 89 90 89 122 98 94 93
personalized learning
8 5 6 8 5 9 10 7 7 10
environment
adaptive learning system 4 5 9 16 15 11 21 17 22 19
intelligent tutoring sys-
80 63 76 86 83 84 114 90 92 91
tem
100 140
120
80
100
60 80
60
40 40
20 20
0
0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
personalized learning personalized learning
individual learning individual learning
direct instruction direct instruction
Fig. 1. Dynamics of publication activity on the problems of personalized and individual learn-
ing, according to Web of Science Core Collection (a) and Scopus (b) abstract and citation data-
bases (accessed March 25, 2020).
120 140
100 120
100
80
80
60
60
40
40
20 20
0 0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
personalized e-learning personalized e-learning
adaptive learning adaptive learning
intelligent tutoring intelligent tutoring
Fig. 2. Dynamics of publication activity on the problems of personalized e-learning, adaptive
learning and intelligent tutoring, according to Web of Science Core Collection (a) and Scopus
(b) abstract and citation databases (accessed March 25, 2020).
50 120
40 100
80
30
60
20
40
10 20
0 0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
personalized learning environment personalized learning environment
adaptive learning system adaptive learning system
intelligent tutoring system intelligent tutoring system
a) data source: Web of Science Core Collection b) data source: Scopus
Fig. 3. Dynamics of publication activity on the problems of personalized learning environment,
adaptive learning system and intelligent tutoring system, according to Web of Science Core
Collection (a) and Scopus (b) abstract and citation databases (accessed March 25, 2020).
The Web of Science Core Collection and Scopus abstract databases also provide an
opportunity to analyze the distribution of authors by country. Based on the analysis of
relevant data, we can conclude that these problems are relevant for educational sys-
tems of different countries. The most scientists represent the United States of Ameri-
ca. The leaders' list includes researchers from China, Spain, Germany. In the Table 3,
three countries with the highest authors of publications percentage are shown for each
of the key phrases.
Table 3. Distribution of publications on personalized and adaptive learning by country.
Percentage of the total number of authors of publications in-
Key phrase dexed in the abstract database
1st 2nd 3rd
Web of Science Core Collection
personalized learning USA 24.8% China 18.3% Spain 7.8%
individual learning USA 13.4% Germany 9.9% China 6.0%
direct instruction USA 36.9% Indonesia 6.7% Germany 6.4%
personalized e-learning Australia 14.3% Greece 14.3% China 10.7%
adaptive learning USA 19.2% Taiwan 7.5% Spain 7.3%
intelligent tutoring USA 36.1% Taiwan 8.0% Spain 6.5%
personalized learning envi-
USA 20.0% Spain 12.0% Greece 8.0%
ronment
adaptive learning system USA 17.8% Taiwan 17.8% China 13.3%
intelligent tutoring system USA 34.0% Spain 8.5% Canada 8.1%
Scopus
United Kingdom
personalized learning USA 24.2% China 13.6%
8.1%
Percentage of the total number of authors of publications in-
Key phrase dexed in the abstract database
1st 2nd 3rd
United Kingdom
individual learning USA 19.3% Germany 11.1%
9.8%
direct instruction USA 47.7% Australia 6.3% Canada 5.5%
United Kingdom
personalized e-learning Greece 10.8% Spain 9.2%
9.2%
adaptive learning USA 22.6% China 9.3% Taiwan 6.6%
intelligent tutoring USA 39.7% Germany 6.6% China 5.8%
personalized learning envi- United Kingdom
USA 18.7% Germany 9.3%
ronment 6.7%
adaptive learning system USA 19.4% China 15.1% Taiwan 7.9%
intelligent tutoring system USA 39.7% Germany 6.5% China 5.9%
The analysis of publications indexed by Scopus and Web of Science Core Collection
by criterion of authors' affiliations also shows that the United States is the leader
(Scopus – 12.3%; Web of Science Core Collection – 11.0%).
Selecting of papers in electronic libraries and abstract databases within the search
query "(adapt * OR personali *) AND (education * OR "tutoring" OR instruction *
OR course *) AND ("learning environment" OR "learning system "OR" tutoring sys-
tem ") AND (evaluative * OR empiric * OR experiment * OR survey * OR question-
naire)" gave such results: IEEE Xplore® Digital Library – 402; ACM Digital Library
– 3215; Scopus – 1280; Web of Science Core Collection – 573. Most of these materi-
als have been published in influential international scientific journals, including Com-
puters and Education, International Journal of Artificial Intelligence in Education,
International Journal of Emerging Technologies in Learning, Interactive Learning
Environments, British Journal of Educational Technology, Educational Technology &
Society.
The distribution of publications by years according to Scopus and Web of Science
Core Collection is given in Table 4 and shown in Fig. 4. Note, that the number of
publications indexed in Scopus is gradually increasing, and the Web of Science Core
Collection is changing slightly.
Table 4. Distribution of publications on problems of application of adaptive learning systems
by years.
Data source 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Scopus 88 91 109 124 120 142 140 140 153 173
Web of Science
5 3 6 4 3 12 12 6 8 12
Core Collection
200
180
160
140
Scopus
120
100 Web of Scince
80
60
40
20
0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Fig. 4. Dynamics of publication activity on the problems of using adaptive learning systems
personalized and adaptive learning according to Web of Science Core Collection (a) and Sco-
pus (b) abstract and citation databases (accessed March 25, 2020).
One of the indicators of the interest of scientists in the presented materials, as well as
the degree of influence of these publications in the field of research on the problems
of adaptive learning in the educational process is their citation. The distribution of
publications selected by search query by the number of citations is given in Table 5.
The table is based on Scopus and Web of Science Core Collection data, so the number
of citations in materials indexed in Scopus and Web of Science Core Collection is
taken into account.
Table 5. Distribution of publications on problems of using adaptive learning systems by num-
ber of citations.
0 citations 1 – 49 citations 50 – 99 citations 100 or more citations
Data source
number % number % number % number %
Scopus 352 27.5 876 68.4 38 3.0 14 1.1
Web of Science 49.6 14
271 47.3 284 2.4 4 0.7
Core Collection
The findings of the studies implied that scientists are interested in issues of personal-
ized and adaptive ICT-enhanced learning. So, we can state their significance for theo-
ry and practice of education.
To answer the second research question "what are the ICT-means for personaliza-
tion of learning?", we examined some scientific papers devoted to using ICT for per-
sonalization of learning from the list selected on the previous exploring stage.
In [19], Su J-M. develops the a rule‐based self‐regulated learning (SRL) assistance
scheme to intelligently facilitate personalized learning with SRL‐based adaptive scaf-
folding support for learning computer software [19, p. 536]. He defines five adaptive
scaffolding strategies and rule sets which are corresponding to planning, controlling,
monitoring, and reflecting phases [19, p. 540]. Moreover, researcher describes intelli-
gent learning environment built on these strategies, and gives examples of rule sets
use. Through an experimental research, he points out advantages of using approach
offered. In particular, one of them is the scaffolding strategies can be manageable and
extensible to support different learning subjects of computer software courses [19, p.
553].
In [20], Rohloff, Sauer, and Meinel discuss the problem of content and learning
paths personalization in Massive Open Online Courses (MOOC). They state, that
MOOC platforms are oriented on providing knowledge numerous learners. But, this
approach is not very effective, and a lot of learners are not achieve their educational
goals. Researchers offer tools to integrate personalized learning objectives into
MOOC platform and facilitate students activities. Through the special interface,
learners select learning objectives which are subsequently supported by guiding the
learning with respect to the selected objective [20, p. 9].
In [7], Turčáni and Balogh are considering a methodology for creating a personal-
ized e-course with the possibility of adapting to the learner in a special environment.
They offer an AdaptiveBook module for LMS Moodle which collects data about stu-
dents' activities and helps to build personal learning way.
A recommendation module of an adaptive and intelligent web-based programming
tutoring system – Protus is described in [21]. As Klašnja-Milićević et al. state, tutor-
ing systems can contain two categories of adaptivity tools: (1) adaptive hypermedia
for course adaptation to learners' individual learning styles; (2) recommendation tech-
niques to suggest the most appropriate learning activities to learners [21, p. 886].
The recommender framework offered by researchers contains three modules: (1) a
learner-system interaction module, which gathers data of learners activities to build
appropriate models; (2) an off-line module, which recognizes learners’ goals using
learner models; (3) a recommendation engine for producing a recommendation list
[21, p. 888]. To investigate learning styles across four dimensions (Information Pro-
cessing, Information Perception, Information Reception, Information Understanding),
authors use data collection tool - Index of Learning Styles by Felder and Soloman [21,
p. 889]. To evaluate benefits of using this recommendation module, researchers per-
formed an experiment while studying programming. They conclude, that experimental
results show positive effect of using proposed module.
Some other approaches to personalization of learning are considered in [22; 23; 24;
25; 26].
The following conclusions can be drawn from the results of the analysis:
• a person-centered approach is well developed in educational theory and practice.
However, researchers are looking for new ways to implement it in order to achieve
the highest degree of accordance learning content and means to person needs and
opportunities and to provide conditions for lifelong learning;
• personalized and adaptive learning envisages the organization of the educational
process when a comprehensive study of learner is carried out, then a model of one's
possible development is constructed, and subsequent influences and interactions
are built taking into account this non-static editable model;
• development of learner model is based on data about the learning style and other
personal characteristics. Its collecting and further processing are complex process
that requires the involvement of specialists in various scientific fields, as well as
the use of information and communication technologies;
• organizing the distance learning process updates research on the issues of adaptive
learning systems, among which we consider it advisable to highlight areas such as:
improving the functionality of existing learning management systems, including
the extended Moodle platform, to provide them with the means of personalizing
learning (student analysis, personal characteristics) formation of individual educa-
tional routes, adaptive delivery of educational content and assessment, etc.); pro-
fessional training of specialists (psychologists, teachers, tutors) for the application
of these systems in formal and non-formal education institutions.
At the end, it is necessary to emphasize that individualized and adaptive learning
have an important significance for life-long learning development. There are some
reasons of this statement. Firstly, these approaches are based on learning styles mod-
els, and suppose satisfaction of persons' educational needs in different circumstances.
Secondly, using information and communication technologies helps to give access to
learning to widespread strata of the population.
4 Conclusion
The article presents the results of studying the state of research into the problem of
organizing personalized and adaptive learning, as well as the use of adaptive learning
systems. The study was conducted using the method of extensive search in electronic
databases.
The analysis covered scientific publications for the years 2010-2019, presented in
the abstract and citation databases Scopus and Web of Science Core Collection, as
well as the electronic libraries of the Institute of Electrical and Electronics Engineers
and Association for Computing Machinery. The study consisted of two stages: 1)
search for a set of key phrases: "personalized learning", "individual learning", "direct
instruction"; "personalized e-learning", "adaptive learning", "intelligent tutoring";
"personalized learning environment", "adaptive learning system", "intelligent tutor-
ing system"; 2) search on a custom search query (adapt* OR personali*) AND (educa-
tion* OR "tutoring" OR instruction* OR course*) AND ("learning environment" OR
"learning system" OR "tutoring system") AND (evaluat* OR empiric* OR experi-
ment* OR survey* OR questionnaire). The results of the analysis of the generated
sample by years of publication, countries of origin of authors, number of citations are
presented in tables and diagrams.
The resulting sample covers publications in influential scientific publications. The
refinements applied (time period, databases, key queries, search categories, etc.) limit
its scope and facilitate processing, but narrow the analyzed area somewhat.
Taking into account the above perspective areas of research, further intelligence is
aimed at conducting a systematic review of literary sources, which presents the expe-
rience of teachers in the use of adaptive learning systems, as well as studying the level
of preparedness of teachers and higher education students in the field of knowledge
"Education / Pedagogy" in their use in educational process.
5 Funding
This research was funded by a grant from the Ministry of Education and Science of
Ukraine (Nos. g/r 0120U101970).
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