Selection Cloud-oriented Learning Technologies for the Formation of

Professional Competencies of Bachelors Majoring in Statistics and

General Methodology of Their Use

Tetiana A. Vakaliuk

1,2,3 a

, Olga D. Gavryliuk

2 b

, Valerii V. Kontsedailo

4 c

Vasyl P. Oleksiuk

5,2 d

and Olga O. Kalinichenko

3 e

Zhytomyr Polytechnic State University, 103 Chudnivsyka Str., Zhytomyr, 10005, Ukraine

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

Str., Kyiv, 04060, Ukraine

Kryvyi Rih State Pedagogical University, 54 Gagarin Ave., Kryvyi Rih, 50086, Ukraine

Inner Circle, Nieuwendijk 40, 1012 MB Amsterdam, Netherlands

Ternopil Volodymyr Hnatiuk National Pedagogical University, 2 M. Kryvonosa Str., Ternopil, 46027, Ukraine

Keywords:

Criterion, Selection Criteria, Cloud-based Learning Technologies, Cloud Services, Bachelors Majoring in

Statistics, the Methodology of Use.

Abstract:

This article scientiﬁcally substantiates the criteria for the selection of cloud-oriented learning technologies for

the formation of professional competencies of bachelors majoring in statistics, as well as presents the results

of expert evaluation of existing cloud-oriented learning technologies by deﬁned criteria. The criteria for the

selection of cloud-oriented learning technologies for the formation of professional competencies of bachelors

majoring in statistics were determined: information-didactic, functional, and technological. To implement

the selection of cloud-oriented learning technologies for the formation of professional competencies of bach-

elors majoring in statistics, and effective application in the process of formation of relevant competencies,

the method of expert evaluation was applied. The expert evaluation was carried out in two stages: the ﬁrst

one selected cloud-oriented learning technologies to determine the most appropriate by author’s criteria and

indicators, and the second identiﬁed those cloud-oriented learning technologies that should be used in the ed-

ucational process as a means to develop professional skills Bachelor of Statistics. According to the research,

the most appropriate, convenient, and effective cloud-oriented learning technologies for the formation of pro-

fessional competencies of future bachelors of statistics by the manifestation of all criteria are cloud-oriented

learning technologies CoCalc and Wolfram Alpha. The general structure of the methodology of using cloud

learning technologies for the formation of professional competencies of future bachelors of statistics is de-

scribed.

1 INTRODUCTION

The European integration processes, change, and de-

velopment of the educational system of Ukraine cre-

ates new requirements for the training of specialists

in almost all spheres of human life. The formation

of general competencies is the basis of general educa-

https://orcid.org/0000-0001-6825-4697

https://orcid.org/0000-0001-9761-6511

https://orcid.org/0000-0002-6463-370X

https://orcid.org/0000-0003-2206-8447

https://orcid.org/0000-0002-7057-2675

tion, and the formation of professional competencies

of future specialists is carried out in the process of

education in higher education institutions (HEI). Tra-

ditional learning is out of date and needs updating, re-

plenished with new technologies, forms, means, and

is conﬁrmed in the text of the National Doctrine of

Educational Development that “continuous improve-

ment of the quality of education, updating its content

and forms of organization of educational process; de-

velopment of the system of continuous education and

training throughout life; introduction of educational

innovations, information technologies” (President of

Ukraine, 2002).

132

Vakaliuk, T., Gavryliuk, O., Kontsedailo, V., Oleksiuk, V. and Kalinichenko, O.

Selection Cloud-oriented Lear ning Technologies for the Formation of Professional Competencies of Bachelors Majoring in Statistics and General Methodology of Their Use.

DOI: 10.5220/0010921900003364

In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020) - Volume 1, pages 132-141

ISBN: 978-989-758-558-6

An important achievement in the ﬁeld of educa-

tion has been the creation of open education platforms

based on the implementation of the principle of the

functioning of cloud technologies; comprehensive up-

dating of training technologies, methodological sup-

port, and content of distance and e-learning based on

the introduction of information and communication

technologies (ICT); introduction of new forms and

methods of teaching based on cloud-oriented tech-

nologies, Web 2.0 technologies, services of electronic

social networks (Kremen, 2016).

Formation of professional competencies of spe-

cialists, including the future bachelor of statistics, is

carried out during the training at HEI, and the use of

the latest information and communication technolo-

gies is an important key element in this process. That

is why one of the leading areas of qualitative train-

ing of specialists in the requirements of today is the

application of cloud technologies, and in the educa-

tional process – cloud-oriented learning technologies

(COLT).

Research on evaluating the effectiveness of ICT

learning has largely highlighted the problem of eval-

uating learning outcomes.

The analysis of existing ICTs, criteria, and indica-

tors of their selection were analyzed and highlighted

in (Bykov et al., 2014; Golovnia, 2015; Demyanenko

et al., 2013; Kolos, 2013)

In particular, Bykov et al. (Bykov et al., 2014)

considered open web-oriented systems for monitor-

ing the implementation of scientiﬁc and pedagog-

ical research results. Golovnia (Golovnia, 2015)

in her works investigated the virtualization software

in the training of UNIX-like operating systems and

identiﬁed the criteria and indicators of their selec-

tion. Demyanenko et al. (Demyanenko et al., 2013)

give methodological recommendations on the selec-

tion and use of electronic tools and resources for ed-

ucational purposes. Kolos (Kolos, 2013) has devel-

oped criteria for selecting components of a computer-

oriented educational environment for a postgraduate

teacher education institution. Spirin (Spirin, 2011) of-

fers criteria for external evaluation of the quality of in-

formation and communication training technologies.

The use of cloud technologies in education is

shown in (Shyshkina and Marienko, 2020; Valko

et al., 2020; Lovianova et al., 2019; Lytvynova, 2018;

Merzlykin et al., 2017; Symonenko et al., 2020; Popel

et al., 2017; Velychko et al., 2020; Vlasenko et al.,

2020; Volikova et al., 2019).

In particular, the problem of developing a method-

ological system for the use of a cloud-oriented envi-

ronment in the training of databases of future com-

puter science teachers was investigated by Korotun

(Korotun, 2018). The question of designing a cloud-

oriented educational environment of a comprehen-

sive educational institution was also investigated by

Lytvynova (Lytvynova, 2016). Several teams of au-

thors have considered cloud technologies in learn-

ing at different intervals (Shyshkina and Popel, 2013;

Valko et al., 2020; Lytvynova, 2018; Glazunova et al.,

2017; Seidametova et al., 2012; Markova et al., 2015;

Striuk and Rassovytska, 2015). At the same time, the

question of research into the use of cloud technolo-

gies in training future bachelor of statistics and the

development of appropriate criteria and indicators of

selection have not been sufﬁciently studied.

The purpose of the article is to deﬁne criteria

and establish appropriate indicators for the selection

of cloud-oriented learning technologies to shape the

professional competencies of bachelors majoring in

statistics and to develop a general methodology for

the use of selected cloud-based learning technologies

for the speciﬁed type of activity.

2 METHODS

An expert evaluation method was used to imple-

ment the selection of the COLT for the formation of

the professional competencies of future bachelors of

statistics and for effective application in the process

of forming the corresponding competencies (Zastelo,

2015; Gavryliuk et al., 2020). According to the pur-

pose and objectives of the method, the corresponding

COLT is numbered in ascending or descending order

based on a separate trait, by which further ranking is

made. It should be noted that the peer review was

carried out in two stages.

In the ﬁrst stage, experts were asked to evaluate

8 COLT that could be used in the process of forming

the professional competencies of future bachelors of

statistics.

In the second phase of the study, another group of

specialists was recruited to evaluate the most signiﬁ-

cant COLT according to certain criteria.

3 RESULTS

3.1 Selection of Cloud-based Learning

Technologies for the Formation of

Professional Competencies of

Future Bachelors of Statistics

Research on the implementation of cloud-oriented

learning technologies to shape the professional com-

Selection Cloud-oriented Learning Technologies for the Formation of Professional Competencies of Bachelors Majoring in Statistics and

General Methodology of Their Use

133

petencies of future professionals is being actively pur-

sued by various researchers. As this research is aimed

at COLT to shape the professional competencies of

future Bachelor of Statistics, it is important to iden-

tify, by a certain set of criteria, the most effective,

convenient, and relevant cloud-oriented learning tech-

nologies to be used in the educational process of HEI.

To begin with, we will deﬁne the term “criteria”,

since this deﬁnition is presented differently by differ-

ent researchers.

In encyclopedic reference publications, the con-

cept of “criterion” is deﬁned as “a trait, a basis for

evaluation, taken as a basis for classiﬁcation” (Busel,

2005).

In (Honcharenko, 2000) the criterion is called “the

criterion for evaluating something, a means of verify-

ing the truth or falsehood of a statement”.

Bagrii (Bagrii, 2012) argues that the criterion is

“a standard against which to evaluate, compare a real

pedagogical phenomenon, process, or quality by ref-

erence”.

Torchevsky (Torchevsky, 2012) notes that “in the

most general form, the criterion is an important and

deﬁning feature that characterizes the various qualita-

tive aspects of a particular phenomenon under study,

helps to clarify its essence, helps to specify the main

manifestations. In this regard, the indicator is a quan-

titative characteristic of this phenomenon under study,

which makes it possible to conclude on the state of

statics and dynamics”.

In Dychkivska (Dychkivska, 2004) term “crite-

rion” is deﬁned as “an indicator that characterizes

the property (quality) of an object, the evaluation of

which is possible using one of the measurement meth-

ods or the expert method”.

Under the selection criteria of COLT for the for-

mation of professional competencies of future bach-

elors of statistics, we will understand such features,

qualities, and properties of cloud-oriented technolo-

gies that are required for their effective use in the ed-

ucational process to form the professional competen-

cies of future bachelors of statistics.

We apply the method of expert evaluation (Za-

stelo, 2015; Gavryliuk et al., 2020). In the ﬁrst stage,

experts were asked to evaluate 8 COLT that could be

used in the process of forming the professional com-

petencies of future bachelors of statistics.

20 experts of different proﬁles were invited to the

expert evaluation procedure, among them ofﬁcials of

the State Statistical Service of Ukraine and the State

Treasury in Zhytomyr, employees of banking institu-

tions, employees of commercial ﬁnancial institutions.

A point scoring system was used in the study

(Spirin and Vakaliuk, 2017). According to the afore-

mentioned evaluation system, for the number of N

COLT, the maximum possible estimate of N is given

to the most signiﬁcant in the use of COLT and 1 to

the least signiﬁcant. The results of the assessment are

presented in the form of a table, where the columns

indicate the hotline number and the ﬁelds the expert

number. The COLT name card is presented in alpha-

betical order (A to Z), to prevent psychological clues

that could affect the outcome of the assessment.

To determine whether there is an objective agree-

ment between experts, calculated Kendall’s Concor-

dance Coefﬁcient W by the appropriate formula spec-

iﬁed in (Zastelo, 2015; Gavryliuk et al., 2020).

The results of the peer review are presented in ta-

ble 1.

The result was selected COLT 4: CoCalc, Scilab,

WebMathematica, Wolfram Alpha.

After calculating based on the experimental data

presented (table 1), obtained a coefﬁcient of concor-

dance W = 0.71. Since the value obtained is non-zero,

there is an objective agreement between experts.

In the second phase of the study, another group of

specialists was recruited to evaluate the most signif-

icant COLT according to certain criteria. It is worth

noting that the second stage involved 15 specialists of

different proﬁles, namely: teachers, heads of depart-

ments and deans of faculties of higher education in-

stitutions of Ukraine, having experience and related

to the professional training of future bachelors of

statistics, employers (Main Department of Statistics

in Zhytomyr region, Department of the State Trea-

sury Service of Ukraine in Zhytomyr, Main Depart-

ment of State Tax Service in Zhytomyr region, heads

of state and commercial banks, managers ﬁnancial

companies), which worked directly with the selected

COLT and could objectively evaluate them according

to the degree of manifestation of each criterion.

The manifestation of each of the presented crite-

ria was evaluated for each of this COLT. To this end,

experts have been asked to evaluate its performance

using the scale shown in table 2.

The indicator will be considered positive if the

arithmetic mean of these points is at least 1.5. If more

than half (50%) of the indicators of the relevant cri-

terion are negative, then the criterion is deﬁned as in-

sufﬁciently developed. In the case of:

• when 50–55% of the indicators of the criterion are

positive, the criterion is characterized as critically

manifested;

• if 56–75% of the indicators of the criterion are

positive, then the criterion is characterized as suf-

ﬁciently manifested;

• if 76–100% of the criterion indicators are positive,

AET 2020 - Symposium on Advances in Educational Technology

134

Table 1: Ranking cloud-oriented learning technologies for the formation of the professional competencies of future bachelor

of statistics.

Expert number

COLT

CoCalc Excel GeoGebra Google Maple Scilab Web Wolfram

Online Sheets Cloud Mathematica Alpha

1 6 4 2 1 3 5 7 8

2 6 5 1 2 3 4 8 7

3 8 1 2 3 4 5 7 6

4 5 3 2 1 4 8 7 6

5 5 2 1 4 3 6 7 8

6 6 1 5 2 3 4 8 7

7 8 2 3 1 5 4 7 6

8 5 3 1 2 4 6 7 8

9 6 1 4 3 2 5 8 7

10 7 1 2 3 4 8 5 6

11 7 3 2 4 1 6 5 8

12 5 2 3 6 1 4 8 7

13 8 1 2 3 4 5 6 7

14 6 4 1 3 2 5 8 7

15 7 4 1 3 2 5 6 8

16 5 3 2 4 1 6 8 7

17 8 2 1 3 5 4 7 6

18 7 1 2 3 4 8 5 6

19 4 3 2 1 8 7 5 6

20 7 4 1 2 3 6 5 8

S 126 50 40 54 66 111 134 139

d 36 -40 -50 -36 -24 21 44 49

Table 2: Scale bar for evaluation of the relevant criteria.

Scores Evaluation of the indicator

0 the indicator is missing

1 the indicator is partially available (not available more than available)

2 the indicator is more available than not available

3 the indicator is completely available

then the criterion is characterized as highly mani-

fested (Spirin and Vakaliuk, 2017).

An analysis of existing cloud-oriented learning

technologies to shape the professional competencies

of future bachelors of statistics has made it possible

to identify the criteria and relevant indicators of these

cloud-oriented learning technologies:

• information-didactic: information support; cov-

erage of various sections of mathematics and

statistics; graphical presentation of results; team-

work on the project; ability to apply programming

knowledge;

• functional: user-friendly interface; free of charge;

accessibility; multilingualism;

• technological: cross-platform; integration with

other cloud services; adaptability.

The results of the peer review of each of the

selected criteria and relevant indicators will be dis-

cussed in more detail.

The information-didactic criterion characterizes

the information and didactic component of cloud-

oriented learning technology and is based on the

laws of assimilation of knowledge, skills, and com-

petences, namely:

• the indicator “information support” characterizes

the presence of a description of the use of the tool,

examples, or the presence of a section of assis-

tance;

• the indicator “coverage of various sections of

mathematics and statistics” characterizes the pos-

sibility of using COLT in the process of studying

certain sections of mathematics and statistics;

• the indicator “graphical presentation of results”

Selection Cloud-oriented Learning Technologies for the Formation of Professional Competencies of Bachelors Majoring in Statistics and

General Methodology of Their Use

135

characterizes the ability to interpret the results

in the form of graphs, histograms, or a three-

dimensional model;

• the indicator “teamwork on the project” character-

izes the ability to work with multiple users at the

same time;

• the indicator “ability to apply programming

knowledge” characterizes the ability to take indi-

vidual actions to perform calculations using dif-

ferent programming languages.

Basic data on indicators of information-didactic

criteria for each of the selected COLT contains ta-

ble 3.

The functional criterion characterizes the func-

tional component of cloud-oriented learning tech-

nologies and assumes the following indicators:

• the indicator “user-friendly interface” describes

the convenience and comprehensibility of the in-

terface and the computational component of the

software system;

• the indicator “accessibility” characterizes the pro-

vision of cloud-oriented learning technology to

different categories of users;

• the indicator “free of charge” characterizes the

possibility of free or full use of cloud-oriented

learning technologies;

• the indicator “multilingualism” characterizes the

support of multiple languages (localization) of the

interface.

The basic data on the indicators of the functional

criterion for each of the selected COLT contains in

table 4.

The technological criterion is characterized as fol-

lows:

• “cross-platform” indicates the possibility of using

cloud-oriented learning technologies in different

operating systems;

• the indicator “integration with other cloud ser-

vices” implies the possibility of supporting the

work with calculations in different cloud services,

and the possibility of further integration with

other services;

• “adaptability” indicates the possibility of full use

of cloud-oriented learning technologies on differ-

ent devices (desktop PC, laptop, netbook, tablet,

smartphone, etc.).

The basic data on the indicators of the technolog-

ical criterion for each of the selected COLT contains

table 5.

Let’s summarize the results of the study in table 6.

3.2 The General Structure of the

Methodology of using Cloud-based

Learning Technologies for the

Formation of Professional

Competencies of Future Bachelors

of Statistics

The formation of professional competencies is a

long process that requires, in addition to appropriate

teacher training, the use of appropriate methods of its

implementation.

The methodology of using cloud-based learning

technologies for the formation of professional com-

petencies of future bachelors of statistics includes the

purpose of the application, the content of an applica-

tion, interrelated forms of training, methods, and tools

for achieving a predictable result.

The expected result of the methodology is the

formed professional competencies of future bachelors

of statistics in the specialty 112 “Statistics”.

The purpose of using cloud-based learning tech-

nologies is to form in future bachelors’ statistics of

professional competencies.

The content of the methodology involves improv-

ing the learning process of disciplines of general

training of the variable part of the free choice of stu-

dents using cloud-based learning technologies (on the

example of the content of the variable discipline of

“Computer Statistics”).

Note the features of teaching the discipline “Com-

puter Statistics” for the training of future bachelors of

statistics using cloud-based learning technologies.

To improve and enhance the discipline “Computer

Statistics” carried out:

• selection of cloud-based learning technologies

that are appropriate and reasonable to use in the

learning process of future bachelors of statistics,

to form their professional competencies;

• improving the content of the variable discipline

“Computer Statistics” for the use of cloud-based

learning technologies during the acquaintance and

mastery of relevant topics of the course;

• development of methodical recommendations on

the use of cloud-based learning technologies in

the educational process of the discipline “Com-

puter Statistics”.

The purpose of the discipline is based on the mas-

tery of practical skills of future professional activity

in conditions that are as close as possible to the real

ones; to form professional competencies in applicants

related to a thorough knowledge of the chosen ﬁeld

AET 2020 - Symposium on Advances in Educational Technology

136

Table 3: The information-didactic criterion for selection of cloud-oriented learning technologies and the value of its indicators.

COLT

The indicators

Information

support

Coverage

of var-

ious

sections

of math-

ematics

and

statistics

Graphical

presen-

tation of

results

Teamwork

on the

project

Ability

to apply

pro-

gram-

ming

knowl-

edge

The

mani-

festa-

tion of

the cri-

terion

The level

of mani-

festation

CoCalc 1.93 2.67 2.07 1.80 2.00 100% highly

Scilab 2.13 2.20 0.80 0.80 2.33 60% sufﬁciently

WebMathematica 1.47 2.00 1.33 1.53 2.13 80% highly

Wolfram Alpha 2.33 2.27 2.33 1.53 2.33 100% highly

Table 4: The functional criterion for the selection of cloud-oriented learning technologies and the value of its indicators.

COLT

The indicators

User-

friendly

interface

Free of

charge

Accessibility Multilingualism The man-

ifestation

of the crite-

rion

The level of

manifesta-

tion

CoCalc 1.80 2.00 2.20 1.80 100% highly

Scilab 2.00 1.87 2.13 1.53 100% highly

WebMathematica 1.73 1.87 1.73 1.93 100% highly

Wolfram Alpha 2.13 2.53 2.20 1.60 100% highly

of statistics, the ability to perform a qualitative anal-

ysis of data or calculations, calculations of relevant

processes, the ability to work with statistical informa-

tion, the use of appropriate software and cloud ser-

vices, able to work both independently and in a team.

The study of the discipline “Computer Statistics”

assumes that applicants for the specialty 112 “Statis-

tics” must know the basic concepts of mathematical

statistics; stages of statistical research; specialized

programming languages, in particular, the statistical

programming language R; software for working with

statistical data; specialized cloud services for organiz-

ing work with statistical information; features of the

organization of joint work using cloud services; be

able to perform statistical calculations; perform sta-

tistical calculations using specialized software; per-

form statistical calculations using appropriate cloud

services; transmit and receive statistics; analyze the

obtained data; build and edit schedules; visualize the

received data with the help of specialized cloud ser-

vices; organize joint activities with other specialists

of the relevant activity or clients for whom the statis-

tical survey is carried out.

Consider the modules that form the content of the

advanced program of the discipline “Computer Statis-

tics”:

Module 1. Working with data. Basics of work in R.

Content module 1. Basic concepts, data

types, and elementary functions. Arith-

metic and logical operations. Basic math-

ematical functions. Vectors. Matrices.

Arrays and data frames.

Content module 2. Export and import of

data in R. Export of data, import of data

in internal format. Export and import data

tables.

Content module 3. Programming in R.

Creating your functions. The technique of

vectorization of the function. Conditional

use (if) and multi-conditional (switch) op-

erations. While and repeat loops. Cycle

for.

Module 2. Basic concepts of statistical distribution.

Content module 4. Basic probability dis-

tributions. General concepts of distribu-

tion. The most commonly used distribu-

tions.

Content module 5. Graphic representa-

tion of statistical distributions. Points on

the plane. Charts. Construction of his-

tograms. Elements of three-dimensional

graphics.

Module 3. Statistical evaluation and statistical testing

Selection Cloud-oriented Learning Technologies for the Formation of Professional Competencies of Bachelors Majoring in Statistics and

General Methodology of Their Use

137

Table 5: The technological criterion for the selection of cloud-oriented learning technologies and the value of its indicators.

COLT

The indicators

Cross-

platform

Integration

with other

cloud services

Adaptability The manifes-

tation of the

criterion

The level of

manifestation

CoCalc 1.53 1.53 1.93 100% highly

Scilab 1.53 1.53 1.53 100% highly

WebMathematica 1.73 1.73 1.93 100% highly

Wolfram Alpha 2.60 2.33 2.93 100% highly

Table 6: Generalized results of the selection of cloud-oriented learning technologies by the manifestation of all criteria.

COLT

Criterion

Information-didactic Functional Technological

CoCalc 100% 100% 100%

Scilab 60% 100% 100%

WebMathematica 80% 100% 100%

Wolfram Alpha 100% 100% 100%

of hypotheses.

Content module 6. Evaluation of un-

known parameters. The method of mo-

ments. Quantile method. The method of

the highest probability. Conﬁdence inter-

vals.

Content module 7. Test of statistical hy-

potheses. General concepts of the theory

of hypothesis testing. Algorithm for test-

ing statistical hypotheses. Pearson’s crite-

rion. Kolmogorov’s criterion.

The proposed technique involves the use of the

following teaching methods of selected cloud-based

learning technologies (CoCalc and Wolfram Alpha, as

described above and in (Gavryliuk et al., 2020)):

• Explanatory and illustrative. Statistics as a sci-

ence is quite complex and contains many sec-

tions that contain a signiﬁcant amount of theo-

retical material, theorems and proofs, formulas,

and graphical constructions of relevant processes.

The explanatory-illustrative method as the most

appropriate to use because students receive accu-

rate theoretical material from the teacher, or inde-

pendently from the textbook or textbook with sub-

sequent discussion in class or online, and receive

a visual presentation of the material using selected

cloud-based learning technologies, demonstration

of practical application cloud-based learning tech-

nologies CoCalc and Wolfram Alpha (ﬁgure 1).

Explaining the theoretical aspects of statistics is

a basic factor inﬂuencing students’ further un-

derstanding of the following related topics in the

course, the use of cloud-based learning technolo-

gies to effectively perform professional tasks and

the formation of professional competencies of fu-

ture bachelors of statistics.

• Reproductive. Given the accuracy and complex-

ity of the theoretical material, the course of the

discipline “Computer Statistics” provides for lab-

oratory and practical work, which is planned to

practice tasks of varying complexity according

to the speciﬁed algorithm according to the rele-

vant educational topic, as well as a demonstration

of their cloud-based learning technologies. Co-

Calc and Wolfram Alpha followed by a repeti-

tion of the action scenario by the students. It is

planned to present ready-made solved exercises

and perform exercises in a similar way (two or

three exercises or tasks). Also, it can be pre-

prepared by the teacher sets of statistics provided

to students as a separate ﬁle in the cloud stor-

age or ready-presented statistical sets presented

on the MEI page (Mathematics Education Inno-

vation, http://mei.org.uk/data-sets), or on Google

Public Data, Google Dataset Search services.

• The method of problem statement can be effec-

tively used during practical or independent work,

during which students do not receive samples

of problem-solving or ready-made algorithms for

working with cloud-based learning technologies.

The teacher describes the problems or asks the

formed problem question (one or more), describes

the ways to solve the problem, acts as a men-

tor who guides the work of students. Working in

such circumstances promotes the development of

students’ critical thinking, solving atypical situ-

ations, and forms professional competencies, in

particular, to develop research and analyze the

data obtained; ability to present the results to the

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138

Figure 1: The result of sampling calculations in the Wolfram Alpha service.

Selection Cloud-oriented Learning Technologies for the Formation of Professional Competencies of Bachelors Majoring in Statistics and

General Methodology of Their Use

139

target audience; ability to work in a team.

• Partial search. The study material is presented

by the teacher in part (a certain part of the topic),

and the rest of the students work independently.

However, the teacher directs the work of appli-

cants with questions or pre-selected tasks to pre-

vent errors in their activities or found the wrong

solution.

• Research. The method is quite difﬁcult to use

because it requires additional training from the

teacher and is quite time-consuming. Provides in-

dependence of students in the study of a particular

topic or theoretical aspect, its practical implemen-

tation in cloud-based learning technologies Co-

Calc, Wolfram Alpha, or the study of additional

topics related to the topic of the course, but not

considered due to time constraints on learning dis-

cipline. Researching the problem develops the

ability to conduct research, the ability to use hard-

ware and specialized cloud services, obtain addi-

tional data and interpret them, the ability to work

independently, all together are components of pro-

fessional competencies formed at the appropriate

level of a successful future statistician.

The means of forming the professional competen-

cies of future bachelors of statistics, which are speci-

ﬁed in the presented methodology using cloud-based

learning technologies, include CoCalc and Wolfram

Alpha, textbooks or teaching materials, as well as

computers (laptops, tablets, smartphones) with an ac-

tive connection to the Internet.

The result of the proposed methodology is the

formed professional competencies of future bachelors

of statistics at a high level, as well as the success-

ful application of skills to use CoCalc and Wolfram

Alpha to perform practical work in the professional

ﬁeld.

4 CONCLUSIONS

Therefore, according to the research, the most appro-

priate, convenient, and effective cloud-oriented learn-

ing technologies for the formation of professional

competencies of future bachelors of statistics by the

manifestation of all criteria are cloud-oriented learn-

ing technologies CoCalc and Wolfram Alpha. The

general structure of the methodology of using cloud

learning technologies for the formation of profes-

sional competencies of future bachelors of statistics

is described. In the future, it is planned to describe in

more detail the individual components of the method-

ology of using cloud learning technologies for the for-

mation of professional competencies of future bache-

lors of statistics, in particular the forms of use and

forms of organization of the educational process.

REFERENCES

Bagrii, V. N. (2012). Criteria and levels of future social

educators’ professional skills. Collection of scientiﬁc

works of Khmelnytskyi Institute of Social Technolo-

gies, University of Ukraine, (6). http://nbuv.gov.ua/j-

pdf/Znpkhist

2012 6 4.pdf.

Busel, V. T., editor (2005). The Great Interpretative Dictio-

nary of Modern Ukrainian Language. Perun, Kyiv.

Bykov, V. Y., Spirin, O. M., and Luparenko, L. A. (2014).

Open web-oriented systems for monitoring the imple-

mentation of scientiﬁc and pedagogical research re-

sults. The theory and practice of social systems man-

agement, (1):3–25.

Demyanenko, V. M., Lavrentieva, G. P., and Shyshkina,

M. P. (2013). Guidelines for the selection and use

of electronic tools and resources for educational pur-

poses. The computer at school and family, (1):44–48.

https://lib.iitta.gov.ua/748/.

Dychkivska, I. M. (2004). Innovative pedagogical tech-

nologies. Akademvydav, Kyiv.

Gavryliuk, O., Vakaliuk, T., and Kontsedailo, V. (2020). Se-

lection criteria for cloud-oriented learning technolo-

gies for the formation of professional competencies of

bachelors majoring in statistics. SHS Web of Confer-

ences, 75:04012.

Glazunova, O. G., Kuzminska, O. G., Voloshyna, T. V.,

Sayapina, T. P., and Korolchuk, V. I. (2017). Mi-

crosoft and Google cloud services: organizing group

project work for university students. Open educa-

tional e-environment of Modern University, (3):199–

211. http://openedu.kubg.edu.ua/journal/index.php/

openedu/article/view/84/135.

Golovnia, O. S. (2015). Criteria for selecting virtualization

software to train UNIX-like operating systems. Infor-

mation technology in education, (24):119–133.

Honcharenko, S. U., editor (2000). Vocational education:

vocabulary. Vyshcha shkola, Kyiv.

Kolos, K. R. (2013). Process model and component selec-

tion criteria for a computer-based educational environ-

ment for a postgraduate teacher education institution.

Information technology in education, (17):109–117.

Korotun, O. V. (2018). The use of the cloud-oriented en-

vironment in training databases of future computer

science teachers. PhD thesis, Institute of Informa-

tion Technologies and Learning Tools of the NAES

of Ukraine, Kyiv. https://lib.iitta.gov.ua/711959/.

Kremen, V., editor (2016). National report on

the state and prospects of education develop-

ment in Ukraine. Pedahohichna dumka, Kyiv.

https://mon.gov.ua/storage/app/media/nrk/Analitychni-

materialy/7-natsionalna-dopovid-pro-stan-i-rozvitok-

osviti-v-ukraini.pdf.

AET 2020 - Symposium on Advances in Educational Technology

140

Lovianova, I., Bobyliev, D., and Uchitel, A. (2019). Cloud

calculations within the optional course Optimization

Problems for 10th-11th graders. CEUR Workshop

Proceedings, 2433:459–471.

Lytvynova, S. H. (2016). Design of the cloud-

oriented educational environment of a comprehen-

sive educational institution. Comprynt, Kyiv.

https://lib.iitta.gov.ua/106829/.

Lytvynova, S. H. (2018). Cloud-oriented learning environ-

ment of secondary school. CEUR Workshop Proceed-

ings, 2168:7–12.

Markova, O. M., Semerikov, S. O., and Striuk, A. M.

(2015). The cloud technologies of learning: Ori-

gin. Information Technologies and Learning Tools,

46(2):29–44. https://journal.iitta.gov.ua/index.php/

itlt/article/view/1234.

Merzlykin, P., Popel, M., and Shokaliuk, S. (2017). Ser-

vices of SageMathCloud environment and their di-

dactic potential in learning of informatics and math-

ematical disciplines. CEUR Workshop Proceedings,

2168:13–19.

Popel, M., Shokalyuk, S., and Shyshkina, M. (2017). The

learning technique of the SageMathCloud use for stu-

dents collaboration support. CEUR Workshop Pro-

ceedings, 1844:327–339.

President of Ukraine (2002). On the national doc-

trine of educational development: Presiden-

tial decree no. 347/2002 of april 17, 2002.

https://zakon.rada.gov.ua/laws/card/347/2002.

Seidametova, Z. S., Ablyalimova, E. I., Medzhitova, L. M.,

Seitvelieva, S. N., and Temnenko, V. A. (2012).

Cloud technologies and education. Diaipi, Simfer-

opol. http://eprints.zu.edu.ua/9436/1/REP0000333

Shyshkina, M. and Marienko, M. (2020). The use of the

cloud services to support the math teachers training.

CEUR Workshop Proceedings, 2643:690–704.

Shyshkina, M. P. and Popel, M. V. (2013). Cloud based

learning environment of educational institutions: The

current state and research prospects. Information

Technologies and Learning Tools, 37(5):66–80. https:

//journal.iitta.gov.ua/index.php/itlt/article/view/903.

Spirin, O. M. (2011). Criteria for external evaluation of

the quality of information and communication train-

ing technologies. Scientiﬁc journal of NPU named

after M.P. Drahomanov. Series 2. Computer-Oriented

Learning Systems, (9 (16)):80–85. http://enpuir.npu.

edu.ua/bitstream/123456789/712/1/8.pdf.

Spirin, O. M. and Vakaliuk, T. A. (2017). Criteria of

open web-operated technologies of teaching the fun-

damentals of programs of future teachers of informat-

ics. Information Technologies and Learning Tools,

60(4):275–287. https://journal.iitta.gov.ua/index.php/

itlt/article/view/1815.

Striuk, A. M. and Rassovytska, M. V. (2015). The use

of cloud technologies in the combined teaching of

computer science students of engineering special-

ties. Bulletin of the Alfred Nobel Dnipropetrovsk Uni-

versity. Pedagogy and Psychology, (1 (9)):221–226.

https://lib.iitta.gov.ua/10520/.

Symonenko, S., Osadchyi, V., Sysoieva, S., Osadcha, K.,

and Azaryan, A. (2020). Cloud technologies for en-

hancing communication of IT-professionals. CEUR

Workshop Proceedings, 2643:225–236.

Torchevsky, R. V. (2012). Pedagogical conditions for the

development of management culture of future masters

of military management in the system of postgraduate

education. PhD thesis, Kyiv.

Valko, N. V., Kushnir, N. O., and Osadchyi, V. V. (2020).

Cloud technologies for STEM education. CEUR

Workshop Proceedings, 2643:435–447.

Velychko, V. Y., Fedorenko, E. H., Kaidan, N. V., Soloviev,

V. N., and Bondarenko, O. V. (2020). The support of

the process of training pre-service mathematics teach-

ers by means of cloud services. CEUR Workshop Pro-

ceedings, 2879:318–332.

Vlasenko, K., Chumak, O., Bobyliev, D., Lovianova, I., and

Sitak, I. (2020). Development of an online-course syl-

labus “Operations research oriented to cloud comput-

ing in the CoCalc system”. CEUR Workshop Proceed-

ings, 2740:278–291.

Volikova, M., Armash, T., Yechkalo, Y., and Zaselskiy, V.

(2019). Practical use of cloud services for organiza-

tion of future specialists professional training. CEUR

Workshop Proceedings, 2433:486–498.

Zastelo, O. V. (2015). Analysis of methods for determining

the coherence of opinion of an expert group in assess-

ing the level of formation of the students’ foreign lan-

guage communicative competence. The computer at

school and family, (4(60)).

Selection Cloud-oriented Learning Technologies for the Formation of Professional Competencies of Bachelors Majoring in Statistics and

General Methodology of Their Use

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