An Experiment on the Implementation the Methodology of Teaching

Cloud Technologies to Future Computer Science Teachers

Vasyl P. Oleksiuk

1,2 a

, Olesia R. Oleksiuk

3 b

and Tetiana A. Vakaliuk

4,2,5 c

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

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

Str., Kyiv, 04060, Ukraine

Ternopil Regional Municipal Institute of Postgraduate Education, 1 V. Hromnytskogo Str., Ternopil, 46027, Ukraine

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

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

Keywords:

Future Informatics Teachers, Competence, Methodology, Cloud Computing, Model, Experiment.

Abstract:

The article deals with the problem of training future computer science teachers for the use of cloud tech-

nologies. The authors analyzed courses from leading universities to study cloud technologies. On this basis

the model of application and studying of cloud technologies in the process of training of future teachers of

informatics was developed. The basic principles of this model are proposed: systematic, gradual, contin-

uous. It contains target, content, operating and effective component. Therefore, the stages of using cloud

computing technology were proposed: as a means of organizing learning activities, as an object of study, as a

means of development. The article summarizes the experience of designing a cloud-based learning environ-

ment (CBLE). The model is based on such philosophical and pedagogical approaches as systemic, competent,

activity, personality-oriented, synergistic. Hybrid cloud is the most appropriate model for this environment.

It combines public and private cloud platforms. CBLE also requires the integration of cloud and traditional

learning tools. The authors described the most appropriate teaching methods for cloud technologies such as

classroom learning, interactive and e-learning, practical methods. The article contains many examples of how

to apply the pro-posed methodology in a real learning process. The evaluation of the effectiveness of the

author’s methodology was carried out by using diagnostic tools such as analysis of questionnaires, tests, labo-

ratory and competency tasks. The paper contains a justiﬁcation and description of the pedagogical experiment.

The authors performed a quantitative analysis of its results and veriﬁed their reliability using the methods of

mathematical statistics.

1 THE PROBLEM STATEMENT

Today, the trend of ICT development is the digitiza-

tion of all sectors of public life. As a consequence,

there is an intensive integration of information and

communication technologies (ICT) into the learning

process. Nowadays, teachers are often used cloud

computing in training process. This remote comput-

ing model provides greater accessibility and openness

to education (Bykov and Shyshkina, 2018). Cloud

computing enables students to work with educational

materials regardless of their hardware, software and

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

https://orcid.org/0000-0002-1454-0046

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

geographical location. Therefore, the study and use

of these technologies is mandatory in the curricula of

colleges and universities. This problem becomes es-

pecially relevant in the case of preparing bachelors of

computer science and teachers of informatics.

The purpose of the article is to design content and

study methods for cloud computing in the process of

training future computer science teachers.

The following tasks are required to achieve the

goal of the research:

1. To analyze the state of education in cloud tech-

nologies at leading foreign and Ukrainian univer-

sities.

2. To deﬁne the concept and principles of teach-

ing cloud technologies to future computer science

teachers.

590

Oleksiuk, V., Oleksiuk, O. and Vakaliuk, T.

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers.

DOI: 10.5220/0010926400003364

In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020) - Volume 1, pages 590-604

ISBN: 978-989-758-558-6

3. To offer content and training methods for cloud

technologies.

4. To conduct an experimental verifying of sug-

gested methodology.

The object of the study is the computer science

teachers training process.

The subject of the study is a model of study cloud

technology by future computer science teachers.

We used a set of research methods: theoretical –

analysis of scientiﬁc, technical literature, experience;

generalization of experience of using cloud comput-

ing in education, empirical: observation, analysis,

modeling method, methods of mathematical statistics.

2 ANALYSIS OF CLOUD

COMPUTING LEARNING

EXPERIENCE

Cloud technology training is on the list of courses

from leading US and European universities. Some

of them are focused on the study of individual cloud

platforms, while others involve the study of the the-

oretical foundations of cloud technologies. One ma-

jor subject is administration training, while other stu-

dents are learning to develop cloud applications.

For example, at Harvard University, students are

offered a course in Fundamentals of Cloud Comput-

ing with Microsoft Azure. The content of this course

covers the fundamental architecture and design pat-

terns necessary to build highly available and scal-

able solutions using key Microsoft Azure platform as

a service (PaaS) and server less offerings. The stu-

dents learn fundamentals necessary to make a system

ready for users, including always-up architecture and

deployment strategies, rollback strategies, testing in

production, monitoring, alerting, performance tuning,

snapshot debugging in production, and system health

analysis using application insights and analysis ser-

vices (Harvard University, 2020).

Berkeley University offers a Cloud Computing:

Systems course. In this course, teachers describe the

technology trends that are enabling cloud computing,

the architecture and the design of existing deploy-

ments, the services and the applications they offer,

and the challenges that needs to be addressed to help

cloud computing to reach its full potential. The for-

mat of this course will be a mix of lectures, seminar-

style discussions, and student presentations. Students

will be responsible for paper readings, and complet-

ing a hands-on project (CS294, 2011).

Cambridge University invites students to study

cloud computing. This course aims to teach students

the fundamentals of cloud computing covering top-

ics such as virtualization, data centres, cloud resource

management, cloud storage and popular cloud appli-

cations including batch and data stream processing.

Emphasis is given on the different backend technolo-

gies to build and run efﬁcient clouds and the way

clouds are used by applications to realize computing

on demand. The course includes practical tutorials on

different cloud infrastructure technologies. Students

assessed via a Cloud based coursework project (Kaly-

vianak and Madhavapeddy, 2019).

At the University of Helsinki, students take the

Cloud Computing Fundamentals: AWS course. Stu-

dents learn how to use Amazon Web Services as a

cloud computing platform. This course covers topics

required for AWS Developer Associate certiﬁcation.

The course involves the creation and use of a trial ac-

count on AWS (University of Helsinki, 2019).

Yale University offers a Cloud Networking and

Computing course. In this course, students will visit

the critical technology trends and new challenges in

cloud and data center designs for different trade-offs

of performance, scalability, manageability, and cost in

the networking layers and big data analytical frame-

works. This course includes lectures and system pro-

gramming projects (Yu, 2017).

Another approach is to study cloud technology in

research labs and training centers. At MIT there is

a laboratory called “Parallel & Distributed Operating

Systems Group”. Teachers and students have con-

duct research in cloud systems, multi-core scalabil-

ity, security, networking, mobile computing, language

and compiler design, and systems architecture, taking

a pragmatic approach: they build high-performance,

reliable, and working systems (pdos.csail.mit.edu,

2019).

The California State Polytechnic University is im-

plementing a project to create a data center training

facility through a partnership between the university

and leading cloud platform developers (Microsoft,

Avanade, Chef, Juniper). The Center is engaged in

the deployment of a corporate cloud, through which

practitioners will teach students the design, conﬁgu-

ration, implementation and maintenance of cloud ser-

vices and platforms (Hwang et al., 2016).

Another promising way to acquire ICT compe-

tencies is to study with massive open online courses

(MOOCs) (Zinovieva et al., 2021). Students have

the opportunity to acquire knowledge independently

when they study in them. Universities can also inte-

grate these courses into their own subject disciplines.

Leading online platforms offer many cloud technol-

ogy training courses.

For example, there is an Introduction to Cloud

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers

591

Infrastructure Technologies course on the EdX plat-

form. It contains many chapters. These include ba-

sic: Virtualization, Infrastructure as a Service (IaaS),

Platform as a Service (PaaS), Containers and the lat-

est such as Tools for Cloud Infrastructure, Internet of

Things, How to Be Successful in the Cloud (Linux-

FoundationX, 2019).

Coursera offers several courses to study: Essential

Cloud Infrastructure: Foundation, Essential Cloud

Infrastructure: Core Services, Elastic Cloud Infras-

tructure: Scaling and Automation, Google Cloud

Platform Fundamentals: Core Infrastructure. These

courses explore the Google Cloud Platform and AWS

platforms (Coursera, 2019). In addition to high-

quality educational content, the Courser platform pro-

vides access to the Google Cloud Platform and Ama-

zon Web Services with the QuickLabs service. There,

students can not only perform laboratory tasks, but

also check the quality of their performance.

Udacity has developed a Become a Cloud Dev

Ops Engineer nanodegree program. It provides learn

to design and deploy infrastructure as code, build and

monitor pipelines for different deployment strategies,

and deploy scalable microservices using Kubernetes.

At the end of the program, students will combine

new skills by completing a capstone project (Udacity,

2019).

The Computing Curricula 2017 document that is

used in the development of IT education standards in

the IT domain ITS-CCO (Cloud Computing) involves

the study of such chapters (Task Group on Informa-

tion Technology Curricula, 2017):

ITS-CCO-01 Perspectives and impact;

ITS-CCO-02 Concepts and fundamentals;

ITS-CCO-03 Security and data considerations;

ITS-CCO-04 Using cloud computing applications;

ITS-CCO-05 Architecture;

ITS-CCO-06 Development in the cloud;

ITS-CCO-07 Cloud infrastructure and data.

Researchers and teachers from Ukrainian univer-

sities are also developing cloud computing courses.

For example, the standards of the specialty “123

Computer Engineering” deﬁned the ability of a spe-

cialist to analyze and design high-performance com-

puter systems with different structural organization

using the principles of parallel and distributed infor-

mation processing (tntu.edu.ua, 2018). The course

“Cloud Technologies and Services” was developed

in National Technical University of Ukraine “Igor

Sikorsky Kyiv Polytechnic Institute”. This course

covers the following topics: Cloud technologies and

services, Cloud security, Service Models, Google

App Engine for Java platform, RESTful API build

in Java. The Cloud Technologies course is taught

at the Shevchenko National University’s Faculty of

Information Technologies. The course covers ba-

sic information about the emergence, development

and use of cloud computing technologies. Typolo-

gies of cloud deployment (private, public, hybrid,

public, etc.), cloud computing service models (SaaS,

PaaS, IaaS, etc.) are considered. The discipline pro-

vides an overview of the modern solutions of the

leaders of the cloud computing market – Amazon,

Microsoft and Google. The advantages and disad-

vantages of cloud computing models and their so-

lutions are considered. To develop practical skills

in the discipline, it is proposed to deploy transac-

tional web applications in cloud environments, trans-

fer ready-made solutions to them, learn how to admin-

ister them, and work with virtualization technologies

(http://matmod.fmi.org.ua, 2019).

3 DESIGNING A CLOUD

COMPUTING TRAINING

MODEL

Teaching future IT teachers the use of cloud tech-

nologies is also relevant. Usually, the pedagogical

universities of Ukraine study courses focused on the

use of cloud technologies in education. Most of

them focus on the study of public clouds of Google

Suite or Microsoft Ofﬁce 365 (pnpu.edu.ua, 2019;

ﬁt.univ.kiev.ua, 2016).

In general, Ukrainian and European universi-

ties use cloud platforms to create their own cloud-

based learning environment (CBLE). A methodol-

ogy for using cloud computing for train informatics

teachers and postgraduate students was developed in

(Bilousova et al., 2018; Bykov and Shyshkina, 2018;

Markova et al., 2018).

We interpret the concept of “the use of cloud tech-

nology” as an introduction to the practical work of

a computer science teacher. Appropriate training of

bachelors of computer science should be carried out

continuously and in stages throughout the study pe-

riod. Its effectiveness depends on the level of use of

the tools in the learning process. Therefore, it is nec-

essary to develop a model of organization of students’

learning based on cloud technologies. As a result of

the introduction of the proposed model, students de-

velop ICT competencies for using distributed cloud

resources for training and research.

The cloud-based student learning organization

model changes the traditional reproductive approach

AET 2020 - Symposium on Advances in Educational Technology

592

to practically oriented learning. For its design we

have analyzed similar models. They usually contain

motivational, cognitive, activity, productive compo-

nents (Selviandro and Hasibuan, 2013; Paduri et al.,

2013).

They all transform the educational process from

a system that operates on externally set standards to

a self-evolving system. The main components of our

model are shown in ﬁgure 1.

The target component of model provides the cre-

ation of conditions for the organization and support

of joint educational and research work of students. It

provides for the formation of cloud based learning en-

vironment of a university. Based on the previous anal-

ysis, we can claim that there is a social demand for

a teacher who has competencies in the use of cloud

technologies. Such a teacher should be able to or-

ganize the CLBE of school, to form the appropriate

competence in students. In each of these three stages,

we envision students using cloud computing at a dif-

ferent level of awareness. The purpose of this com-

ponent is the goal setting of stage, on which the ef-

fectiveness of the whole process depends. The target

component also determines the creation of conditions

for the formation of personal capacity for future pro-

fessional activity in the conditions of modern techno-

logical changes.

The purpose of training is implemented through

methodological approaches such as:

• the competency approach allows to identify the

content of ICT competencies in the use of cloud

technologies, to improve the practical orientation

of the learning process;

• the system approach allows to consider all compo-

nents of the proposed model as a coherent system.

A system approach requires designing the model

as a set of interrelated elements. Integrative de-

pendencies and interactions of these elements are

also needed;

• the action approach focuses on the prioritization

of active learning methods;

• synergistic approach considers the basic pro-

cesses of student self-organization and interac-

tion. Learning according to this approach is an un-

stable process. This instability complicates adap-

tation, cognitive operations, and overall activity.

The guiding principles of the methodology ac-

cording to our model are the traditional principles

of science, accessibility, continuity, systematicity and

consistency, activity, clarity. Other principles of

learning such as mobility, adaptability, ﬂexibility,

ubiquity are also important.

The content component of the model is aimed at

developing both the key (digital, personal, social, edu-

cational) and subject competences of future computer

science teachers.

At the center of the proposed model is a student.

Accordingly, the competence structure deﬁnes the

components by the stages of implementation. They

correspond to the preparatory, activity, generalization

stages of the use of cloud technologies. The study in

the preparatory and activity stage should be done in

the bachelor’s degree. The generalization stage can

be implemented as a master’s program.

At the preparatory stage, cloud technology is a

means of organizing educational and cognitive activ-

ity. The relevant components of subject competence

are such as:

• ability to be guided by features of modern cloud

technologies, to understand their functionality and

to be used for basic educational tasks;

• ability to distinguish between features and charac-

teristics of “traditional” Internet services, hosting

web resources, running virtual private machines

in cloud infra-structures;

• ability to determine the ways of using cloud tech-

nologies for the organization of training and re-

search activities according to service models;

• ability to behave adequately and responsibly in

a cloud environment, to demon-strate knowledge

and understanding of the legal, ethical aspects of

using cloud services and digital content;

• ability to actively and constantly explore new ser-

vices, implement them in their activities, aware-

ness of the role of cloud computing in the current

stage of IT and education.

In the activity stage, cloud computing is the object

of study. The relevant components of subject compe-

tence are such as:

• knowledge of basic concepts, deployment models

and service models of cloud technologies, princi-

ples of operation and technology of server system

virtualization, architecture and standards of dis-

tributed computing, and features of hard-ware and

software solutions of modern data centers;

• ability to install, conﬁgure and maintain system,

tool and application software of cloud platforms

according to the basic service models;

• ability to evaluate and determine effective CBLE

deployment decisions based on an analysis of the

functional characteristics of cloud services and

the needs of educational institutions;

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers

593

Figure 1: The model for learning cloud computing.

• ability to design, deploy and integrate ready-made

cloud platforms to improve the IT structure of the

educational institution;

• ability to monitor, support and analyze the func-

tioning of the CBLE.

At the generalization stage, cloud computing is a

development tool for creating educational resources

and learning tools. The relevant components of sub-

ject competence are as follows:

• ability to formulate requirements for quality as-

surance of software development for its function-

ing in the cloud applications;

• ability to evaluate and identify effective deploy-

ment solutions for CBLE based on a comparison

of the technical and economic properties of cloud

computing services, as well as for solutions based

on private and hybrid cloud systems;

• ability to formulate ways to increase the efﬁciency

of the use of cloud technologies in solving orga-

nizational educational and scientiﬁc tasks;

• ability to develop software for educational institu-

tions in a cloud computing environment, test and

debug relevant hardware and software;

• ability to project activities, work in a team to

jointly solve educational and scientiﬁc tasks.

The technological component of the model deﬁnes

the system of teaching methods. We consider appro-

priate methods of teaching cloud technologies such

as:

• classrooms training (lectures, storytelling, presen-

tations, group discussions, tutorials etc);

• interactive methods (quizzes, small group discus-

sions, case studies, participant control, demon-

strations etc);

• services, as well as for solutions based on private

and hybrid cloud systems;

• e-learning (web-based training, web meetings,

webinars, collaborative document preparation,

work in CBLE);

• practical training methods (project, training).

AET 2020 - Symposium on Advances in Educational Technology

594

In general, these methods aim at providing a

blended learning methodology. Their application is

possible during lectures, laboratory work, self-study

trainings, individual and group consultations. We in-

clude the traditional means and components of CBLE

in the training tools.

To provide group work and student feedback in

each course, we use tools such as:

• emails and messengers;

• software for remote access to the objects of stu-

dents in CBLE;

• module and ﬁnal tests;

• Likert scale course feedback.

The resultant component of the model involves

providing ubiquitous access to learning resources

through standardized protocols, enhancing students’

ICT competency, improving the quality of educa-

tional process organization and pedagogical research.

We consider it necessary to use public and private

clouds as a teaching tool not only in the ﬁrst stage, but

also throughout the whole time of studying the bach-

elor of computer science. Such public clouds are G

Suite and Microsoft Ofﬁce 365. Their developers of-

fer free subscriptions to educational institutions. Stu-

dents and staff can get corporate accounts of these

cloud platforms. The use of these platforms can be

practiced in almost all courses of professional train-

ing of the future computer science teacher.

For example, a teacher can schedule study assign-

ments, student work, online consultations using Cal-

endar services. For training demonstrations, webinars

can be effective cloud services such as Google Meet

and Skype for Business and more.

Topical issues of using cloud technologies in train-

ing are their integration with each other and with other

learning tools. Such integration should provide single

authentication (Single Sign-On – SSO), content avail-

ability in various cloud services, access from mobile

devices, and ability to monitor student activity.

Great technical and training capabilities are in the

deployment of private academic cloud according to

the IaaS model. We have deployed a similar cloud

based on the Apache CloudStack platform. It com-

bines the system resources of 4 servers. This allows

you to run 20-50 virtual machines at a time. With

Apache CloudStack’s enhanced networking capabili-

ties, we have integrated these computers into a large

number of virtual local area networks (VLANs). To

provide universal access to the virtual labs, 2 virtual

private network (VPN) servers were set up. They

work with different protocols. Therefore, students

are able to work with these labs from any device that

has Internet access. All these services have formed a

cloud infrastructure that is integrated into the univer-

sity’s LAN. Such an academic cloud makes it possi-

ble to create “cloud laboratories”. In our opinion, a

cloud lab is a system where virtual ICT objects are

generated through cloud computing and networking.

Cloud labs are best used to teach basic computer sci-

ence courses, such as computer architecture, operat-

ing systems, programming, computer networks, and

more.

One of these laboratories (CL-OS) was deployed

for training. Its purpose was the development of ICT

competences, the education of the need for system-

atic updating of knowledge, the formation of project

activity skills. To complete with the tasks, the stu-

dents were supposed to have basic knowledge of the

following disciplines: Operating Systems, Computer

Architecture and Software. The main teaching meth-

ods in this training were group and project techniques.

Students’ educational projects were about practically

important tasks, such as: recovery of destroyed data,

increase of operating systems performance, error cor-

rection during loading, virus removal.

Students use G Suite and Microsoft Ofﬁce 365

public clouds to discuss learning problems, create and

edit shared documents (diagram, abstract, brochure,

booklet, infographics). They acquire teamwork skills

such as communication, teamwork and group leader-

ship; formulation of tasks for yourself and colleagues,

perform tasks in a timely manner (Spirin et al., 2018).

Each of the group members was provided with a

separate virtual machine. It had defects of one of the

above types. Students were able to work on solving

problems not only from any university computer, but

also from their home PC. To train one group of stu-

dents, an academic cloud provided 20–30 virtual ma-

chines (VMs).

Another cloud lab (CL-EVE-NET) was organized

to study computer networks. We have integrated the

Apache CloudStack and EVE-NG Community Edi-

tion platforms to deploy it. Nested Virtualization

technology was used for this purpose. The EVE-NG

platform makes it possible to emulate the operation of

different nodes that are integrated in an internetwork.

These nodes can be virtual machines running differ-

ent operating systems. The integration of EVE-NG

and Apache CloudStack platforms enables the use of

full-featured network OS.

The integration of EVE-NG and Apache Cloud-

Stack platforms enables the use of full-featured net-

work OS. They can be accessed via the EVE-NG plat-

form web interface and through Telnet and VNC pro-

tocols. This lab uses both Apache CloudStack virtual

networks and ENE-NG platforms. If the student con-

ﬁgures the network connections correctly, access will

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers

595

also be available through the appropriate protocols.

We used the CL-EVE-NET lab to study basic

computer network topics, such as: switching and

bridging, network monitoring tools, basic and NAT

routing; dynamic routing protocols; load-balancing

Internet channel, policy base routing, data ﬁlter with

ﬁrewall, network protocols and services (DHCP, ARP,

DNS); virtual private network protocols (Spirin et al.,

2019).

This cloud lab allows you to bring together indi-

vidual student networks. As a result, we get a inter-

network of group. This approach ensures student col-

laboration and teamwork. An error with one of them

can causes problems throughout the network. For the

training of one group of students, an academic cloud

provided the functioning of 20 “parent” VMs. They

ran up to 10 nested virtual network devices (bridges,

switches, routers, hosts).

The CL-ADM cloud lab has been deployed for the

network administration course. In this course, we use

both Windows and Linux. So, to study each topic,

we create at least 2 virtual machines as servers and at

least 2 VMs as clients.

The main topics of the course are:

• network administration of Windows and Linux

servers (local users and groups, ﬁlesystems secu-

rity, network shares, remote administration);

• domain administration (Active Directory, Samba,

NIS);

• server application administration (Apache,

ProFTPd, IIS, Postﬁx, Dovecot SQUID).

To train one group of students, an academic cloud

provided 30–40 virtual machines. Training at the ac-

tivity and generalization stages is carried out accord-

ing to the special program “Cloud Technologies Fun-

damentals”.

The course involves the study of: publicly avail-

able cloud platforms by recognized software de-

velopment vendors (Google Inc., Microsoft), and

open source software as the foundation for enterprise

cloud.

The main topics of the special course are:

• public cloud platforms (G Suite and Microsoft Of-

ﬁce 365);

• cloud platforms for private clouds (Apache

CloudStack, Proxmox).

We used to study the G Suite and Microsoft Ofﬁce

365 public platforms in the form of a Cloud Services

to Every School project (Oleksiuk et al., 2017). The

objectives of the project were to design and deploy

cloud services for secondary schools. The basics of

the project concept were: absence of material costs

for deployment and support of cloud services, vol-

untary nature of participation in the project. In col-

laboration with computer science teachers, students

determined which services needed to be conﬁgured

or migrated to the cloud. The problems of mainte-

nance and support required a lot of time. Teachers

had questions about administering, conﬁguring, mon-

itoring cloud services. We solved such problems by

organizing face-to-face and distance seminars, work-

shops, also through the involvement of students in the

support of deployed systems.

The results of the “Cloud Services to Every

School” project is in line with the indicators of a

cloud-based learning environment. They are: qual-

ity and accessibility of learning, adaptability, inter-

activity and mobility of ICT tools, uniﬁcation of the

school’s IT infrastructure, ensuring its security.

We propose to study private clouds on the exam-

ple of open platforms. We suggest exploring private

clouds as an example of open platforms. Their advan-

tages are open source, freeware, English documenta-

tion, the ability to deploy advanced cloud infrastruc-

tures. However, such platforms are usually not sup-

ported by the developer. Therefore, teaching students

with such platforms often requires them to look for

solutions to various problems. This approach requires

modern hardware. Private clouds require servers that

perform different functions. For deployment by stu-

dents of such clouds it is necessary to use the group

method. It is a division of tasks. Students can perform

tasks together or individually such as:

• conﬁguring the database server;

• cloud platform setup;

• installing hypervisors;

• creating virtual computers;

• distribution of system resources.

In the future, students change roles. Since at our

university the special course “Fundamentals of Cloud

Technologies” is studied in the master’s program, we

consider it appropriate to use a research approach. It

is that the teacher formulates detailed technical re-

quirements for the cloud. Students research and cus-

tomize platforms to meet these requirements. The

results of such research can be summarized by the

method of comparative analysis. For example, one

platform may have better performance for the produc-

tion platform and another platform will perform more

effectively as part of the CBLE.

Important in the ICT competency of the future

computer science teacher is the possession of soft-

ware development tools. Cloud services should be at

the forefront of creating students’ own educational in-

formation resources. The third stage of our model is

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596

dedicated to this task.Training can be based on this

platform leader in software and cloud.

Microsoft has developed a Windows Azure Web

Sites product that enables students to create new and

host existing web applications in a secure cloud stor-

age. Windows Azure Web Sites implements a Plat-

form as a Service (PaaS) model. Therefore, students

will be able to fully focus on the programming and

direct development of their cloud projects.

Google also offers a similar Google Cloud Plat-

form (GLP) cloud service. It allows you to cre-

ate, test and deploy your own applications in the

cloud. Students can learn how to create state-of-the-

art web applications and mobile applications on the

open Google App Engine cloud platform. It is a man-

aged platform that completely abstracts the cloud in-

frastructure, which helps to focus training on devel-

opment tasks.

Deployment of cloud laboratories is also appropri-

ate for a full study of these systems. Unfortunately,

Google has not yet provided academic grants to use

GLP for Ukrainian universities. However, students

are free to use their own accounts for one year. A

similar situation with Microsoft products. It is nec-

essary to get a Microsoft Azure Education Grant for

effective learning.

We propose to use a comprehensive approach and

project methodology in the process of studying these

tools. The main requirements of applying the project

methodology at this stage are as follows:

• identifying the main problem that the created

project should solve;

• requirement for student creativity in project devel-

opment;

• no restrictions on the tools and their functionality;

• the value of the expected result, that is, a cloud-

based application must be developed and de-

ployed;

• organization of joint activities of students;

• identiﬁcation of pre-formed competencies for

project creation;

• the project’s focus on modern cloud and web tech-

nologies.

The third (generalization) stage of our method-

ology consists of several logical parts. They com-

bine a relatively small amount of theoretical mate-

rial. It’s a good idea for a teacher to start learning

about the Google Cloud Platform (GCP). The practi-

cal part involves setting up the environment and creat-

ing a project, conﬁguring a cloud database. The next

task is to log in and log in. After that, students should

focus on project architecture and development of core

functionality.

We invite students to develop a contact manager.

Its main functionality is to enable an authorized user

to create, view, edit and delete records. It also has the

option of sending e-mails to selected contacts. This

basic functionality is present in almost every modern

web application. Students can use GCP cloud prod-

ucts such as Google App Engine standard environ-

ment, Google Cloud SQL, Google Cloud Datastore,

Google Cloud Storage and Google Cloud Pub to de-

velop it.

Application development in the Google Cloud

Platform facilitates group form organization. The

teacher can add new project participants and assign

them speciﬁc roles to determine the degree of access.

In this project, the teacher demonstrates GLP capa-

bilities based on such programming tools as PHP and

Node.js. Important issues for cloud-based application

development are understanding:

• basic functionality of PHP and Node.js;

• basics of a modular, ﬁle and batch system;

• ﬁle management;

• use of the postal service;

• work with the MySQL database.

The next step is to introduce students to the

Google Cloud Platform environment, the basics of

App Engine, and the application deployment process.

It is a good idea for the teacher to organize the devel-

opment of the project in a private university cloud and

then deploy it into a public cloud. It is also possible to

develop the project only in a cloud environment. Both

approaches include steps to develop a web application

that will allow users to submit requests to the server.

After completing these tasks, students develop

their own ICT competencies such as:

• creating a GCP project based on App Engine;

• writing a web server on Node.js;

• deploy code on App Engine and view the web ap-

plication in real time;

• adding updates to an already deployed service.

After creating this application, students move on

to expand its functionality through other GLP ser-

vices. Further practical work focuses on develop-

ing students’ own cloud applications. These can

be an online study log, e-library, video hosting ser-

vice, photo gallery etc. Their students perform in

small groups of 2-3 people. They can offer their

own themes for development. Upon completion, stu-

dents present projects and share their experiences and

achievements.

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers

597

4 TESTING THE

EFFECTIVENESS OF THE

AUTHOR’S METHODOLOGY

We conducted a pedagogical experiment to verify the

developed methodology. The study was conducted

during 2016–2020. We investigated the development

of ICT competence under the conditions of imple-

mentation of the proposed model. The aim of the

study was to identify changes in the levels of ICT

competence of students. According to Mazorchuk

et al. (Mazorchuk et al., 2020) this competence con-

tains basic theoretical knowledge, methods of practi-

cal activity, motivational relations and the ability to

apply cloud technologies in the future. They almost

completely correspond to the structure of our model

of application of cloud technologies. Let’s look at

each of these components.

The motivational (target) component contains mo-

tives, goals, needs for professional training, self-

improvement, self-development by means of cloud

technologies. It stimulates creativity in the profes-

sional activities of a computer science teacher. Ac-

cordingly, the student must develop a need for con-

stant updating of his (her) own knowledge. The moti-

vational component contains the motives for teaching,

the focus on the development of students’ personali-

ties.

The content component of ICT competence of fu-

ture computer science teachers provides free mastery

of skills in working with digital objects. The level of

development of the content component is determined

by the completeness, depth, system of knowledge of

computer and related sciences. It requires knowl-

edge of the principles of cloud computing, its use for

the design and development of educational resources.

Knowledge of the security threats and limitations of

these tools is also required.

Activity (operational) component involves the de-

velopment of skills (including soft-skills) for the ap-

plication of cloud technologies in future professional

activities. These include the ability to establish inter-

personal relationships in the educational environment,

to choose the right style of communication in dif-

ferent situations. Basically, this component requires

the skills and experience needed by future computer

science teachers to solve problems using cloud tech-

nology. Advanced development of this component

requires mastering and forming the readiness of fu-

ture computer science teachers to develop and im-

plement cloud computing in the educational process.

The formation of appropriate skills should be deter-

mined by the professional needs of future computer

science teachers.

The reﬂective (effective) component of ICT com-

petence is determined by the attitude of students to

their practical activities. It includes self-control, self-

esteem, understanding of their own role in the team.

Important for this component are the evaluation of the

results of their activities, understanding the respon-

sibility for its results, professional self-realization

through the means of cloud technologies.

The study was conducted during 2016–2020. It

had ascertaining and search stages. The ascertaining

stage corresponded to the ﬁrst and second stages of

the author’s model. The study was conducted in the

bachelor’s course “Computer Networks”. Since most

of the components of the author’s model are imple-

mented at the generalization stage, we decided that

the search stage should be performed in the process of

learning a special course “ Cloud Technologies Fun-

damentals”.

At each stage of the experiment, the following

data were processed:

• results of the questionnaire like course feedback,

as data for studying the target component;

• grades for all course tests as data of the content

component of the model;

• grades received by students for laboratory work as

data of the operational component;

• assessments for a competency task as data of the

effective component.

For statistical processing of these data, we used

the methodology developed by Olena Kuzminska

(Kuzminska, 2020). To ensure a sufﬁcient sample

size, we had to process the data for 4 years. We

studied the changes and tried to identify differences

in the data of each of the components of ICT com-

petence. To ensure the homogeneity of the groups at

both stages, the results of questionnaires and assess-

ments of the same students were processed. There

were a total of 196 students in these study periods. All

data of the ascertainment and search stage are avail-

able by the following link https://drive.google.com/

ﬁle/d/1n-lPQI-eGFMJiuwq jI7BaWoM3aTUNK0.

Assessment in each of the courses was on a 100-

point scale with a distribution such as:

• maximum 40 points for the test tasks of the course

(content component);

• maximum 40 points for laboratory work (opera-

tional component);

• maximum 20 points for the performance of the

competence task (effective component).

In addition to 20 points, the student could receive

for answering the questionnaire, which gave an an-

swer to the feedback about the course. To choose a

AET 2020 - Symposium on Advances in Educational Technology

598

statistical method, we took into account the following

facts:

1. The data are quantitative; therefore, we can use

numerical scales.

2. The data may not correspond to the normal dis-

tribution. Therefore, it is necessary to check this

for each of the components of ICT competence at

each stage of the study.

3. Samples of each year of study are independent.

4. There are 4 groups for comparison.

We performed data processing using the R lan-

guage. First, we checked the data distribution of each

component is normal for the ascertaining stage.

lillie.test(AscertainingStageData$Target)

#Lilliefors (Kolmogorov-Smirnov) normality

# test

#data: AscertainingStageData$Target

#D = 0.074284, p-value = 0.01045

lillie.test(AscertainingStageData$Content)

#Lilliefors (Kolmogorov-Smirnov) normality

# test

#data: AscertainingStageData$Content

#D = 0.056802, p-value = 0.1276

lillie.test(AscertainingStageData$Operational)

#Lilliefors (Kolmogorov-Smirnov) normality

# test

#data: AscertainingStageData$Operational

#D = 0.055232, p-value = 0.1531

lillie.test(AscertainingStageData$Effective)

#Lilliefors (Kolmogorov-Smirnov) normality

# test

#data: AscertainingStageData$Effective

#D = 0.085305, p-value = 0.001434

As can be seen from the code listing above, the

data distributions of the content and the operational

components are normal, and the target and effec-

tive are not. Therefore, a more powerful one-way

ANOVA method for independent groups can be used

to process the ﬁrst two cases. Another pair of com-

ponents should be processed using a non-parametric

Kruskal–Wallis one-way analysis of variance. These

methods allow to check whether the studied groups

are homogeneous.

Additionally, for the ANOVA method, the homo-

geneity of variances in each distribution should be

checked. We performed this using Levene

s test for

homogeneity.

leveneTest(AscertainingStageData$Content\

˜AscertainingStageData$Years,

AscertainingStageData,center=mean)

#Levene’s Test for Homogeneity of Variance

# (center = mean)

# Df F value Pr(>F)

#group 3 0.2084 0.8905

# 192

leveneTest(AscertainingStageData$Operational\

˜AscertainingStageData$Years,

AscertainingStageData,center=mean)

#Levene’s Test for Homogeneity of Variance

# (center = mean)

# Df F value Pr(>F)

#group 3 1.6235 0.1853

# 192

As can be seen from the listing F value = 0.8905

and F value = 1.6235 (for content and operational

components in accordance). These values are smaller

for the critical value F

0.05

(3; 192) = 8.53. The corre-

sponding p-values (Pr = 0.8905 and Pr = 0.1853) are

greater than the signiﬁcance level (α = 0.05). This is

a reason to reject the null hypothesis about the dif-

ference of variances in the samples. Therefore, the

ANOVA method can be used for the content and ac-

tivity components.

Then the null and alternative hypotheses are as fol-

lows:

• H0 – there are differences between the groups at

the ascertaining stage;

• H1 – there are no differences between the groups

at the ascertaining stage;

The following code contains a test of these hy-

potheses.

summary(aov(Content˜Years,

data=AscertainingStageData))

# Df Sum Sq Mean Sq F value Pr(>F)

#Years 3 57 18.96 0.822 0.483

#Residuals 192 4431 23.08

summary(aov(Operational˜Years,

data=AscertainingStageData))

# Df Sum Sq Mean Sq F value Pr(>F)

#Years 3 57 19.04 0.751 0.523

#Residuals 192 4870 25.36

Thus, for both components we can reject the zero

and accept the alternative hypothesis. Similar hy-

potheses can be formulated for the target and effective

components. Here is a test of group homogeneity for

these components using the Kruskal-Wallis one-way

analysis of variance.

kruskal.test(Target˜Years,

data = AscertainingStageData)

#Kruskal-Wallis rank sum test

#data: Target by Years

#Kruskal-Wallis chi-squared = 6.3968,

# df = 3, p-value = 0.09382

kruskal.test(Effective˜Years,

data = AscertainingStageData)

#Kruskal-Wallis rank sum test

#data: Target by Effective

#Kruskal-Wallis chi-squared = 0.55391,

# df = 3, p-value = 0.9069

The test showed that we can accept an alternative

hypothesis about the homogeneity of groups. The task

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers

599

of the search phase of the study was to identify differ-

ences between groups during the 2017-2020 years of

the study. During this period, in each academic year,

we introduced the some technical and methodological

components of the mod-el such as:

• 2016–2017: deployed CL-OS laboratory;

• 2017–2018: the project “Cloud services in each

school” was implemented;

• 2018–2019: deployed CL-EVE-NET and CL-

ADM laboratories;

• 2019–2020: Coursera courses on Google Cloud

Platform are included in the special course “Cloud

Technologies Fundamentals”.

Similar to the ascertainment stage, we analysed

the results of the questionnaire, grades for tests, labo-

ratory works and competence task.

The questionnaire for diagnosing the level of the

motivational component contained 20 questions. For

each positive answer to the questionnaire, the student

received one point. Points for completing the ques-

tionnaire, grades from the course were obtained by

students in a special course “ Cloud Technology Fun-

damentals “ in 2016-2020. Here are the questions.

1. I understand the importance of cloud technologies

for the organization of educational activities.

2. I understand the importance of cloud technologies

for the organization of design and research activi-

ties of students.

3. I understand the importance of cloud technologies

for the organization of extracurricular activities of

students.

4. I am aware that cloud technologies expand the op-

portunities for the development of students’ ICT

competence

5. I follow the emergence of new cloud services for

education.

6. I am watching the emergence of new platforms for

the deployment of private clouds.

7. I have studied cloud platforms in MOOCs.

8. I have the skills to develop cloud applications.

9. I can develop separate cloud services for CBLE

school.

10. I know the beneﬁts of cloud services as a means

of supporting teacher self-development and self-

improvement.

11. I understand that the use of cloud services has a

positive impact on the quality of teaching and di-

versiﬁes forms of learning.

12. I try to monitor the emergence of new resources

and tools for cloud technology to improve their

competence.

13. I realize that it is necessary for teachers to imple-

ment and disseminate new ideas about the use of

cloud technologies.

14. I am aware of the advantages of cloud technolo-

gies and modern means of communication for co-

operation between educational institutions.

15. I am aware of the beneﬁts of cloud technology to

reduce the cost of education.

16. I am interested in using cloud technologies to im-

prove communication and increase the competi-

tiveness of educational institutions.

17. I adhere to legal and ethical standards when using

cloud services and digital content.

18. I participated in joint projects to develop an effec-

tive educational environment.

19. I have deployed cloud services for educational in-

stitutions.

20. I performed support of CBLE of school.

Diagnosis of the level of the analytical component

of ICT competence of future computer science teach-

ers was investigated by testing the ability to use the

acquired knowledge and skills in non-standard situa-

tions. Students had to demonstrate the ability to per-

form reﬂective analysis and correction of their digital

activities. We of-fered undergraduates to perform a

competency task. They had to develop a long-term

plan for the development of CLBE educational in-

stitution. The plan implementation algorithm was to

contain a detailed description of each stage of CLBE

deployment and use in the school.

1. CLBE design:

• analysis of the state of the school’s digital envi-

ronment;

• studying the speciﬁcs of the activities of teach-

ers and students and determining their needs for

the use of cloud services;

• determining the functionality of cloud services;

• identiﬁcation of subjects for which it is not yet

possible to implement the necessary function-

ality;

• technical audit of the digital environment of

damage, including hardware, software, per-

sonal devices, availability of Internet access;

• ﬁnding out the ﬁnancial capabilities of the edu-

cational institution.

2. Recommendations for implementation

AET 2020 - Symposium on Advances in Educational Technology

600

• informing teachers, students, parents about the

structure and possibilities of using CLBE;

• designing a security policy for the use of cloud

services and notifying it to all participants in

the educational process;

• development and implementation of an algo-

rithm for deploying cloud platforms;

• technical and pedagogical support of activities

in CLBE;

• training of school staff, informing the admin-

istration about the development of digital tech-

nologies.

3. Development prospects

• determining the scalability of the CBLE;

• development of an action plan in case of breach

of conﬁdentiality of personal data;

• support for modern standards, protocols, rules

for updating all components of the environ-

ment;

• participation in national and international edu-

cational projects.

Again, let’s check the normality of the distribu-

tion of points obtained by students at the search stage.

Here are the results of the Kolmogorov-Smirnov test:

• target component: D = 0.070342, p-value =

0.01958;

• content component: D = 0.060965, p-value =

0.07329;

• activity component: D = 0.062046, p-value =

0.06374;

• effective component: D = 0.10837, p-value =

0.000007515.

P-values for motivational and effective compo-

nents again do not correspond to the normal distribu-

tion. P-values for the content and activity components

are close to the critical value of 0.05, but still exceed

it. Therefore, we will consider the obtained distribu-

tions to be normal. Let us check the homogeneity of

their dispersions. Here is the result of running lev-

eneTest:

• content component: F value= 0.9305, Pr(>F)=

0.427;

• activity component: F value= 0.5496, Pr(>F)=

0.649

Therefore, we can apply the One-way ANOVA

test for the content and operational components. Here

are the results of calling the corresponding function.

summary(aov(Content˜Years,

data=ResearchingStageData))

# Df Sum Sq Mean Sq F value

# Pr(>F)

#Years 3 378 126.0 3.612

# 0.0143 *

#Residuals 192 6701 34.9

#Signif. codes: 0 ’***’ 0.001 ’**’ 0.01

# ’*’ 0.05 ’.’ 0.1 ’ ’ 1

As can be seen from the listing, we have to ac-

cept the alternative hypothesis in both cases. That is,

there are differences between groups. Figure 2 shows

quantile scale diagrams. The dots on the chart show

the emissions. In our case, such emissions are low

grades of students who have very low grades from the

course.

We can assume that the factor that caused these

changes is the introduction of the author’s methodol-

ogy. To determinate a set of conﬁdence intervals for

the differences between the means of the factor’s lev-

els with the speciﬁed probability of coverage we have

used Tukey’s ‘Honest Signiﬁcant Difference’ method

for both components.

TukeyHSD(aov(Content˜Years,

data=ResearchingStageData))

#$Years diff lwr

# upr p adj

#2017-2018-2016-2017 0.9925994 -2.1827991

# 4.167998 0.8496254

#2018-2019-2016-2017 2.3033885 -0.7508257

# 5.357603 0.2090704

#2019-2020-2016-2017 3.7281806 0.6022937

# 6.854068 0.0121774

#2018-2019-2017-2018 1.3107890 -1.7611233

# 4.382701 0.6863632

#2019-2020-2017-2018 2.7355812 -0.4076003

# 5.878763 0.1122965

#2019-2020-2018-2019 1.4247921 -1.5959128

# 4.445497 0.6133838

For the content component, the differences be-

tween the values of the 2016–2017 and 2019–2020

academic years are statistically signiﬁcant changes.

TukeyHSD(aov(Operational˜Years,

data=ResearchingStageData))

#$Years diff lwr

# upr p adj

#2017-2018-2016-2017 0.06336725 -2.9224371

# 3.049172 0.9999401

#2018-2019-2016-2017 3.45547675 0.5836212

# 6.327332 0.0111944

#2019-2020-2016-2017 4.33000434 1.3907555

# 7.269253 0.0010348

#2018-2019-2017-2018 3.39210950 0.5036125

# 6.280606 0.0140729

#2019-2020-2017-2018 4.26663709 1.3111262

# 7.222148 0.0013706

#2019-2020-2018-2019 0.87452759 -1.9658195

# 3.714875 0.8552859

From the above listing, we can conclude that al-

most all components of the model had the skills to

An Experiment on the Implementation the Methodology of Teaching Cloud Technologies to Future Computer Science Teachers

601

Figure 2: Range diagrams of average values of content and operational components.

create, deploy and use cloud technologies.

To assess the development of the target compo-

nent, we use the Kruskal-Wallis test. Here are its re-

sults:

• Target by Years Kruskal-Wallis chi-squared =

7.0967, df = 3, p-value = 0.06888;

From the obtained test data, we can still accept the

null hypothesis that there are no statistically signif-

icant differences between the groups. Therefore, we

cannot draw a reasonable conclusion about the impact

of our model on the development of the motivational

component of ICT competence.

For the reﬂex component, the results of the

Kruskal-Wallis test are as follows:

• Effective by Years Kruskal-Wallis chi-squared =

18.66, df = 3, p-value = 0.0003213;

In this case, we accept an alternative hypothesis

about the existence of differences between groups of

students. In order to make multiple comparisons be-

tween groups, possibly with a correction to control the

experiment wise error rate we have performed Dunn’s

Kruskal-Wallis test. Here are its results:

PT = dunnTest(Effective˜Years,

data = ResearchingStageData)

# Comparison Z

# P.unadj P.adj

#1 2016-2017 - 2017-2018 0.08638957

# 0.9311567356 0.931156736

#2 2016-2017 - 2018-2019 -1.70307343

# 0.0885543273 0.177108655

#3 2017-2018 - 2018-2019 -1.78256141

# 0.0746577266 0.223973180

#4 2016-2017 - 2019-2020 -3.66052102

# 0.0002517029 0.001258514

#5 2017-2018 - 2019-2020 -3.72765494

# 0.0001932697 0.001159618

#6 2018-2019 - 2019-2020 -2.06601565

# 0.0388270019 0.155308008

The results of this test show that there are dif-

ferences between 2016-2017 – 2019-2020 and 2016-

2017 – 2019-2020 pairs of years. Therefore, we can

conclude that participation in a real project had a pos-

itive impact on students’ integrated under-standing of

the role of cloud technologies in the digitalization of

the school learning process.

5 CONCLUSION

The problem of the use of cloud computing in the pro-

cess of training future computer science teachers is

actual and needs further research. Training for the use

of cloud technologies should be carried out through-

out the student’s study period. The model of applica-

tion and studying of cloud technologies in the process

of training of future teachers of informatics contains

target, content, technological and resultant compo-

nent. The content component realizes during 3 stages

such as.

1. Cloud technology is a means of education.

2. Cloud computing is the object of study.

3. Cloud computing is a development tool.

The study in the ﬁrst and second stage should be

done in the bachelor’s degree. Stage 3 can be imple-

mented as a master’s program.

The current level of cloud computing development

makes the project method demanded and effective.

Participation in the proposed projects contributes to

the development of students’ skills of independent

and responsible work with cloud technologies. They

have opportunity to focus on results. Students can rec-

ognize themselves as successful network administra-

tor, programmer, teacher.

Our model provides combination of face-to-face

and online learning allows teachers to make use of

advantages offered by the cloud base learning envi-

ronment.

AET 2020 - Symposium on Advances in Educational Technology

602

According to the results of the experiment, the hy-

pothesis of a positive impact of the designed CLBE on

the development of ICT competence of future com-

puter science teachers was conﬁrmed. Participation

in the real project had a signiﬁcant impact on stu-

dents’ integrated understanding of the role of cloud

technologies.

Qualitative changes in the dynamics of develop-

ment of components of ICT competence of students

using the proposed model conﬁrmed the effectiveness

of the author’s methodology.

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