Ensuring the Effectiveness of e-Learning based on Online Technology

Analysis of Factors Inﬂuencing the Cognitive Independence of Students

Evgeniy A. Lavrov

1 a

, Viktoriya G. Logvinenko

2 b

, Viacheslav V. Osadchyi

3 c

Olga Ye. Siryk

4 d

and Yana I. Chybiriak

4 e

Sumy State University, 2 Rimsky-Korsakov Str., Sumy, 40007, Ukraine

Sumy National Agrarian University, 160 Herasyma Kondratieva Str., Sumy, 40000, Ukraine

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

Taras Shevchenko National University of Kyiv, 60 Volodymyrska Str., Kyiv, 01033, Ukraine

Keywords:

Cognitive Independence, E-Learning, Human Factor, Lifelong Learning, Distance Learning.

Abstract:

The problem of improvement of educational technologies in connection with the revealed problems, aggra-

vated in the conditions of COVID-19, is considered. A new approach to building a system of ﬂexible learn-

ing, based on “tuning” the technologies of student interaction with the educational environment, is proposed.

Such adaptation is carried out using the mathematical model of educational process control developed by the

authors, the parameters of which are characteristic of the factors that form the cognitive independence of stu-

dents.The presented information technology for assessing the factors that shape the cognitive independence of

students can be integrated into any educational system due to the universal capabilities that Google services

provide. The developed technology is very useful for studying the real picture of individual factors of cognitive

independence in the educational process, organized with the help of electronic educational technology. The

main functional capabilities and advantages of the developed information technology are: the ability to orga-

nize adaptive learning, the ability to organize questionnaires in any electronic educational system, simplicity

and ease of use, modular structure, and others.

1 INTRODUCTION

The current educational revolution (Reid, 2006;

Bersin, 2004; Blaschke, 2012; Cochrane et al.,

2013), the rapid technology of e-learning (caused by

COVID-19) (Joshua et al., 2016; Pereira and Ro-

drigues, 2013) and the concept of lifelong learning

(Al-Qahtani and Higgins, 2013; Voloshinov et al.,

2020) exacerbate the following problems:

• quality of e-learning environment (Verkhova and

Akimov, 2017; Lavrentieva et al., 2021),

• adaptive learning (Kotova and Pisarev, 2017;

Atto and Kotova, 2020; Haranin and Moiseienko,

2018),

https://orcid.org/0000-0001-9117-5727

https://orcid.org/0000-0003-0993-0821

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

https://orcid.org/0000-0001-9360-4388

https://orcid.org/0000-0002-0634-7609

• ergonomic support of the educational system

(Lavrov et al., 2017),

• formation of cognitive independence (Burov,

2017; Pinchuk et al., 2020).

The analysis of these scientiﬁc articles shows that

the main task of improving and ensuring the effec-

tiveness of e-learning is to enhance the cognitive in-

dependence of students. In modern literature, cog-

nitive independence is deﬁned as follows (Lavrov

et al., 2021): ”Cognitive independence is an integra-

tive property of a student who learns using a com-

puter, associated with the student’s initiative and the

search for various alternative ways to solve problems

without the participation of tutors”.

2 PROBLEM STATEMENT

A problem arises: “How to ensure the cognitive in-

dependence of students in the conditions of electronic

Lavrov, E., Logvinenko, V., Osadchyi, V., Siryk, O. and Chybiriak, Y.

Ensuring the Effectiveness of e-Learning based on Online Technology Analysis of Factors Inﬂuencing the Cognitive Independence of Students.

DOI: 10.5220/0011009500003364

In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020) - Volume 2, pages 569-577

ISBN: 978-989-758-558-6

569

education?”.

Lavrov et al. (Lavrov et al., 2021) outlines ap-

proaches to the analysis of factors that affect the cog-

nitive independence of students. However, the ques-

tion remains: “How to implement such a study in

practice?”.

Consider the most well-known digital content

management systems designed to organize learning

processes using Internet technologies (table 1). For

almost all of these platforms, the main goal is to orga-

nize access to teaching materials, ensure interaction,

testing and reporting between teachers and students.

One of the most common distance learning sys-

tems in universities is the Moodle system, an educa-

tional platform that aims to connect teachers, admin-

istrators and students in a reliable, secure and inte-

grated system to create a personalized learning envi-

ronment (Moodle Docs, 2020; Abdula et al., 2020).

The technical aspects of the system can be de-

scribed as follows:

• Moodle is written in PHP using a SQL database,

• Moodle is installation packages and detailed in-

stallation ﬁles,

• Moodle represents different categories of users:

administrators, teachers-developers, teachers, stu-

dents, and guests.

Moodle has a wide range of features that are spe-

ciﬁc to e-learning platforms (Moodle Docs, 2020).

This allows you to organize all stages of the learning

process: diagnosis, planning, training, management

of educational activities, evaluation of results.

To diagnose and assess certain learning phenom-

ena in the Moodle system, the following technologies

are provided:

1. Questionnaire module is an activity that provides

many proven survey tools used to assess and

encourage learning in the Internet environment.

Teachers can use them to collect data from stu-

dents to help them understand the class and think

about their own teaching (Moodle Docs, 2020).

There are three types of questionnaires:

• ATTLS – Attitudes toThinkingandLearning

Survey a questionnaire containing 20 questions,

designed to determine the level of students’ at-

titudes to distance learning,

• COLLES – Constructivist On-Line Learning

Environment Survey (questionnaire “Learn-

ing Environment withEelements of Construc-

tivism”) a questionnaire containing 20 ques-

tions, designed to determine the level of stu-

dents’ attitudes to distance learning,

• Critical Incidents (questionnaire “Critical Inci-

dents”) is a questionnaire in which students are

given the opportunity to assess certain events

and their attitude to what is happening.

2. Survey module (choice) – in the classroom, you

can ask questions and set switches, and students

can press these switches to choose from a number

of possible answers. They can choose one or more

options, and if pre-settings allow, they can update

their selection. The options can be used as a quick

survey to stimulate reﬂection on the topic, to allow

the class to vote for the direction of the course, or

to assess progress (Moodle Docs, 2020).

3. Module test is an activity that allows teachers to

develop and build tests of knowledge, consisting

of many types of questions, including multiple

choice questions, right or wrong questions, short

answers and correspondence, and numerical ques-

tions (Moodle Docs, 2020).

4. The provided technologies can diversify distance

learning courses and make them “alive”.

5. Although these technologies have certain diag-

nostic capabilities, we believe that the main dis-

advantage of the questionnaire module is its static

nature:

• you cannot edit the questionnaire,

• other questions cannot be entered (they can

only be used in the same form as speciﬁed by

the developer).

Therefore, when teachers want to create their own

questionnaires to diagnose certain aspects of assess-

ment of learning phenomena, problems arise because

this is not provided in the system.

As the analysis of the literature shows, modern e-

learning systems do not allow to investigate the im-

portance of factors that affect cognitive independence

(in order to increase the effectiveness of learning).

Modern e-learning technologies have a wide range

of organizational capabilities at all stages of learning,

including diagnostic tools, but they are static in na-

ture, and the scope of assessment of learning phenom-

ena in them is limited.

Thus, despite the large number of studies in the

ﬁeld of adaptive learning, including (Osadchyi et al.,

2020), the practice of most universities has shown a

lack of effective online learning in a pandemic. The

following facts have been recorded: decreased moti-

vation, decrease in the quality of the educational pro-

cess, refusal to study, stressful situations both among

students and teachers.

The main issues are related to the following dis-

abilities: operational research of motivational param-

AET 2020 - Symposium on Advances in Educational Technology

570

Table 1: The most popular educational platforms.

Platform Description

Moodle (https://moodle.org/) The platform integrates teachers, administrators and students (students) into a

reliable, secure and integrated system to create a personalized learning envi-

ronment

Google Classroom (https:

//classroom.google.com)

Google’s web service, designed for educational institutions to facilitate the

creation, distribution and classiﬁcation of tasks, making them paperless

edX (https://www.edx.org/) A platform that provides a large number of courses for various purposes from

the best universities and colleges in the world

Coursera (https://www.coursera.

org/)

An educational platform that provides online courses from the world’s leading

universities and organizations

FutureLearn (https://www.

futurelearn.com/)

Online course platform in the UK

Khan Academy (https:

//www.khanacademy.org/)

Free online courses and courses

Schoology (https://www.

schoology.com/)

A virtual learning environment for schools and universities that allows users

to create, manage and share learning content

Classdojo (https://www.

classdojo.com/)

A communication platform for distance learning in school, used by teachers,

students, and parents

Seesaw (https://web.seesaw.me/) A platform for creating digital learning resources

Skooler (https://skooler.com/) Tools for turning Microsoft Ofﬁce software into an educational platform

CenturyTech (https://www.

century.tech/)

A platform that has tools for distance learning

eters and characteristics of students’ cognitive inde-

pendence, and customizing the educational process

for the characteristics of the student.

In this regard, deﬁne the purpose of this study:

a) develop information technology for analytical re-

search of factors inﬂuencing the effectiveness of dis-

tance learning in conditions caused by a pandemic;

b) develop the principle of building a model that pro-

vides “customization of learning technology for a par-

ticular student studying in a particular educational en-

vironment”.

3 RESULTS

3.1 Development of an Approach to

Building a Model of Adaptive

Formation of Cognitive

Independence in the Context of

Pandemic Constraints

We will consider a typical situation typical for the or-

ganization of the educational process at the university

(Lavrov et al., 2017):

1. The working curriculum for the discipline has M

topics.

2. Each topic has a basic conceptual part (these are

the basic provisions of the topic that are stable

for a long time), as well as a variable part (edu-

cational material, the content of which may vary

depending on the technical process of the educa-

tional process, software of the educational pro-

cess, personal experience, own knowledge, scien-

tiﬁc or methodological advantages, etc.).

3. For each i-th topic in the program, the time t

which can be represented as t

= t

+ t

, is allo-

cated, where ti1 is the time allotted to the concep-

tual part and t

is the time allotted to the variable

part.

4. For each i-th variable part, there are N

variants j

of its presentation.

5. With each i-th variant ( j = 1, N

) of the topic

i (i = 1, M), it is possible to connect some func-

tion of usefulness of presentation of the mainte-

nance of the j-th variant for formation of cognitive

independence. Usefulness cannot be measured di-

rectly. Its indirect assessment may be a number –

the rank of R

i jl

– which is attributed by the expert

to the j-th variant in the i-th topic from the stand-

point of the inﬂuence of educational material of

the j-th variant on the formation of the l-th compo-

nent of cognitive independence. Ranks are formed

by the method of rank correlations. According to

this method, the j-th variant is assigned a rank of

Ensuring the Effectiveness of e-Learning based on Online Technology Analysis of Factors Inﬂuencing the Cognitive Independence of

Students

571

1, if in the opinion of the expert, this variant is the

most useful for the formation of the cognitive in-

dependence in the i-th topic; the second most im-

portant variant of presentation is assigned a rank

of 2, etc. Ranking of variants of teaching material

is carried out for each l-th informative component

of the cognitive independence.

6. To implement the selection process, a logical vari-

able x

i j

, is introduced, which takes on the value 1

if the j-th option is selected when presenting the

i-th topic, and the value 0 otherwise.

Given the assumptions made, the task of forming

cognitive independence can be formulated as follows:

Known:

• the number M of topics of educational material of

the discipline,

• the time t

i j

, allocated for each j-th variable part in

each i-th topic,

• the number N

of j variants of the presentation of

each variable part,

• the structure of the properties l (l = 1, k) of the

student’s personality, the list of which is custom-

ary to explicate cognitive independence (in other

words, personality properties that form cognitive

independence),

• R

i jl

ranks assigned by experts to the j-th variant of

presentation of the i-th topic according to the level

of its inﬂuence on the l-th parameter of cognitive

independence.

It is necessary to choose the following options j

for each topic i to maximize the total effect of the ed-

ucational material on the formation of cognitive inde-

pendence.

Thus, it is necessary to maximize the sum of

ranks, which determines this effect:

∑

i=1

∑

j=1

∑

l=1

i j

→ max, (1)

with restrictions:

• on the study of the discipline

∑

i=1

∑

j=1

i j

≤ T, (2)

• on the obligatory presentation of all topics

∑

i=1

i j

= 1, (i = 1, M), (3)

• on the obligatory choice of at least one version of

the presentation in each topic

∑

i=1

i j

= 1, ( j = 1, N

), (4)

• for integer variables

i j

∈ 0, 1, (5)

Explication of the concept of “cognitive indepen-

dence” allows us to identify a list of personality traits

that form a complex quality of personality “cognitive

independence”, which can be called components of

cognitive independence (factors).

Consider an example of a fragment of a set of

such factors (determined by experts of Sumy National

Agrarian University and the Ukrainian Academy of

Engineering and Pedagogy):

• the need and desire to master the knowledge and

methods of activity,

• cognitive motive and interest,

• interest in the results of their independent cogni-

tive activity,

• interest in the future profession,

• initiative,

• basic knowledge (possessed by the individual),

• acquired basic skills and abilities, computer skills

and possession of previously learned software,

• acquired knowledge of the discipline of the com-

puter cycle being studied,

• acquired skills and abilities in the discipline of

computer cycle, computer skills and possession of

learned software,

• use of scientiﬁc and methodological literature,

means of communication, the Internet,

• attentiveness,

• strong-willed efforts,

• purposefulness,

• persistence,

• contact with the teacher during independent cog-

nitive activities in order to obtain information,

• contact with other students during independent

cognitive activities in order to obtain information,

• ability to set and achieve the goals of cognitive

activities,

• ability to plan their cognitive activities,

• ability to assess their potential in performing cog-

nitive activities,

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572

• ability to evaluate the results of their cognitive ac-

tivities.

In the notation of the above model, a list of k prop-

erties of the student’s l personality is formed (l = 1, k).

Such sets of factors will be different:

• for different universities,

• for different groups of students,

• for different age groups,

• for different learning technologies, etc.

Therefore, it is necessary to be able to model them

in each problem situation.

The main problems of this model are:

• Pr1 – how to embed the model in the distance

learning system;

• Pr2 – how to generate source data that really re-

ﬂects the current problem situation.

We solved the Pr1 problem by creating a spe-

cial technology of intelligent agent-manager, which

is built into any system of distance education (Lavrov

and Lavrova, 2019). To solve the Pr2 problem, we

offer a special online survey technology, which is de-

scribed below.

3.2 Information Technology for the

Analytical Study of the Factors

Inﬂuencing the Effectiveness of

Distance Learning in the Context of

Constraints Caused by a Pandemic

Basic principles of technology:

• use of modern Google Script technology familiar

to students and teachers,

• online questionnaire for all categories of partici-

pants in the learning process:

– all teachers,

– all students

• preliminary formation of factors to be considered

(special expert group of teachers and students),

• embedding the questionnaire in the educational

process management system,

• formation of results:

– for each student,

– for all teachers together.

The results are processed in a special way (Lavrov

et al., 2021). Based on expert data, the average value

of the degree of P

avi

manifestation in the structure of

cognitive independence, the variance of S

expert as-

sessments, the conﬁdence interval V

, as well as upper

conﬁdence limit P

upi

values of the informativeness of

cognitive independence parameters, lower conﬁdence

limit P

lowi

values of the informativeness of cognitive

independence parameters, and the R

Pupi

rank of the

values of the upper limits of the conﬁdence intervals

are calculated for each factor. In addition, a line graph

for R

Pupi

and P

lowi

is built.

Figure 1 and ﬁgure 2 show the results that are au-

tomatically generated by the system after

• conducting a survey,

• processing questionnaires and determining the in-

formativeness of the parameters of cognitive inde-

pendence.

It is clear that for each student we receive the indi-

vidual estimations (ﬁgure 1), and it allows system to

adjust educational process.

If we analyze the ranks of the factors obtained as

a result of the analysis of teachers and students of

Sumy National Agrarian University, the most impor-

tant (fragment) for the conditions of the pandemic (a

total of 20 factors were estimated) were identiﬁed:

• rank 1 – contact with other students during the

performance of independent cognitive activities in

order to obtain information,

• rank 2 – the ability to evaluate the results of their

cognitive activity.

• rank 3 – contact with the teacher during the per-

formance of independent cognitive activity in or-

der to obtain information.

Such studies allow us: a) to change the general

technology of training organization; b) to adjust the

learning process for each student.

The main functional capabilities and advantages

of the developed information technology are:

• the possibility of organizing adaptive learning,

• the possibility of organizing a questionnaire in

any electronic educational system to determine

the level of factors in the study of any discipline,

• an opportunity, for example for educational insti-

tutions, to introduce disciplines in solving prob-

lems related to the quality of teaching,

• simplicity and ease of use,

• modular structure,

• the ability to reach a wide audience of test takers,

with access to the Internet,

• the ability to store answers in Google spread-

sheets,

Ensuring the Effectiveness of e-Learning based on Online Technology Analysis of Factors Inﬂuencing the Cognitive Independence of

Students

573

Figure 1: Results (fragment) of data processing by three experts (students) – the degree of manifestation of the components

of cognitive independence.

• survey results are stored on Google Drive,

• allows you to set a deadline for receiving answers

to questions,

• has sufﬁciently reliable protection, this applies to

both the content of the surveys and the results of

the surveys.

3.3 Use of Technology for the Formation

of Individualized Training Focused

on the Conditions of the Pandemic:

Experimental Studies

The technology of revealing individual features of

students and the model of individual customization of

the educational process “for the student” during the

spring semester of 2019–2020 academic year and the

autumn semester of 2020–2021 academic year were

studied, implemented and tested at the Department of

Cybernetics and Informatics of Sumy National Agrar-

ian University (SNAU).

The content of the questions (20 questions in to-

tal) that were asked to students is described in clause

3.1 and complies with the recommendations (Lavrov

et al., 2021) (however, it is possible to formulate ar-

bitrary questions that are relevant for a particular uni-

versity).

The main advantage of the proposed method is the

ability to ﬂexibly adjust the training to the characteris-

tics of the student and the recommendations of teach-

ers (see table 2).

Satisfaction with the forms of educational pro-

cess (percentage of positive assessments of the qual-

ity of technology, according to materials of the De-

partment of Cybernetics and Informatics of Sumy Na-

tional Agrarian University) in the pandemic is pre-

sented in ﬁgure 3.

4 CONCLUSIONS

Existing e-learning technologies do not offer the pos-

sibility of ﬂexible operational analysis of factors that

determine the quality of the educational process from

the point of view of teachers and students, in particu-

lar the factors that shape the cognitive independence

of students.

In today’s e-learning environment, including due

AET 2020 - Symposium on Advances in Educational Technology

574

Figure 2: Results (fragment) of data processing: R

Pupi

and P

lowi

– upper and lower conﬁdence limits for the informativeness

values of the factors of cognitive independence (obtained during the survey of teachers for the conditions of studying the

discipline “Information Technology”, Faculty of Management, Sumy National Agrarian University).

Figure 3: Satisfaction with the forms of educational pro-

cess (percentage of positive assessments of the quality of

technology, according to the Department of Cybernetics and

Informatics of Sumy National Agrarian University) in the

pandemic.

to COVID-19 restrictions, this is a critical limitation.

In this regard, a modern management system of the

educational process requires a fundamentally new in-

formation technology developed as a result of this

study, which includes models and software:

• online surveys of students and teachers,

• prompt processing of survey results with the pos-

sibility of ranking the factors inﬂuencing cogni-

tive independence in different learning conditions

(including pandemics),

• adjustment of learning technologies to the param-

eters of students identiﬁed as a result of online

surveys.

The scientiﬁc novelty of the result lies in the fact

that in contrast to the existing models of adaptive

management of the learning process, focused on ex-

pert (or selective) assessment of student parameters

and learning technologies, built adaptation models

use online assessment technologies that allow you to

quickly conﬁgure the system to a “problem situation”.

Testing under COVID-19 constraints has proven

Ensuring the Effectiveness of e-Learning based on Online Technology Analysis of Factors Inﬂuencing the Cognitive Independence of

Students

575

Table 2: Development of approaches to learning technologies (example based on materials of computer cycle disciplines,

teacher V. G. Logvinenko, SNAU, Ukraine).

Traditional

learning

(F1 –

Form 1)

Distance

learning

(F2 –

Form 2)

Flexible online learning in a pandemic (adaptive technology) (F3 – Form

Lectures –

18 hours

Study of

lecture

materials

(on the

website) –

18 hours

The volume and forms are adjusted individually according to the results of the

online research:

• online lecture of the teacher,

• video lecture (record),

• study of materials for the lecture (text, presentation),

• discussion of problematic issues of the lecture with the teacher,

• discussion of problematic issues of the lecture in microgroups of students,

• games and debates based on lecture materials

Laboratory

work – 36

hours

Virtual

laboratory

work – 36

hours

The volume and forms are adjusted individually according to the results of the

online research:

• online preparation for laboratory work,

• video to study the technology of laboratory work (record),

• modeling problem situations “what will happen if”,

• discussion of problematic issues of laboratory work with the teacher,

• discussion of problematic issues of laboratory work in micro-groups of stu-

dents,

• games,

• passing a laboratory course

the effectiveness of the approach. The practical sig-

niﬁcance of the results lies in the possibility (thanks

to the use of Google services) of embedding into any

learning process management system.

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