Big Data Aanalytics in Higher Education

Irina Vdovychenko

, Valentyna Khotskina

, Serhii Hushko

2 c

, Victoria Solovieva

2 d

and Sergiy Tkalichenko

National University, Vitaly Matusevich str., 11, Kryvyi Rih, 50027, Ukraine

State University of Economics and Technology, Medychna Str., 16, Kryvyi Rih, 50005, Ukraine

tsw1966@ukr.net

Keywords: Big Data Technologies, Higher Education System, Information Arrays, Analytics, Student Subjects

Olympiads.

Abstract: The article offers an overview of the means and methods of one of the most demanded modern Big Data

technologies for the use in the higher education system. The paper presents an analysis of publications on

similar studies. A brief description of Big Data technology is given in it. The article highlights various

information arrays in the archives of universities, which are systematically replenished. The use of Big Data

technology examples in American universities are considered. The research results of correlations and links

with characteristics of student subjects Olympiad’s winners were represented in the work. The purpose of the

article was to substantiate and propose certain directions for researching processes in the higher education

system using big data analytics. The authors propose the tasks, by solving which it would allow improving

significantly the educational process in universities.

1 INTRODUCTION

The system of higher education is constantly forming

and accumulating huge amounts of information.

These are the data about students and materials,

educational literature, curricula, laboratories and

classrooms, educational practices, diplomas and

coursework, and much more. And all this

information, accumulating, changing and

replenishing, is stored for decades.

To process these archives, new technologies are

required, one of which is Big Data technologies.

The volumes of information are steadily growing

and transforming. The replacement of classical

methods of data analysis with technologies such as

Big Data is becoming relevant.

In view of the fact that data arrays, the size of

which exceeds the potential of reference databases for

their research, processing and storage, require new

https://orcid.org/0000-0003-0953-655x

https://orcid.org/0000-0001-8963-4189

https://orcid.org/0000-0002-4833-3694

https://orcid.org/0000-0002-8090-9569

https://orcid.org/0000-0002-1798-8073

tools and methods of analysis, they are increasingly

appealing to Big Data technology.

The need for Big Data arose a few years ago.

Clifford Lynch, editor of Nature, first introduced the

“big data” concept in 2008. Some scientists were

already interested in the problem of the information

rapid growth at the global scale. It should be noted

that big data has existed before. Today, the Big Data

category includes significant data flows over 100 GB

per day. In our time, the concept of Big Data has

already thoroughly entered the list of the technologies

in demand around the world. It penetrates and is

actively used in various spheres of life: technology,

business, video, sociology, medicine, education,

space, finance, etc.

Big Data is a combination of approaches,

methods, tools applied for the work with structured

and unstructured data of large volumes and diversity.

Most often, the technology is used for solving

intellectual problems. It is necessary in situations of

continuous growth of information. As the analysis

436

Vdovychenko, I., Khotskina, V., Hushko, S., Solovieva, V. and Tkalichenko, S.

Big Data Aanalytics in Higher Education.

DOI: 10.5220/0011364200003350

In Proceedings of the 5th International Scientiﬁc Congress Society of Ambient Intelligence (ISC SAI 2022) - Sustainable Development and Global Climate Change, pages 436-445

ISBN: 978-989-758-600-2

showed, Big Data is an alternative to classical

database management systems and solutions within

Business Intelligence. Big data tools that allow

distributed processing of information are used both

for incredibly large data (all pages of the global

network) and for small ones. To make a more

efficient decision, to identify hidden patterns, it is

necessary to submit a larger amount of input data for

processing, in these conditions Big Data is simply

necessary.

Currently, to process large arrays of "raw" data,

Big Data is used as the ideology of information

processing. At first, conventional computational

methods were used, which made it possible to process

huge amounts of input data, but today, Big Data is a

self-learning algorithm that can independently

expand and improve itself. The technology is making

a leap from simple processing of large information

arrays to a fundamentally new intellectual form. Big

Data technologies open up new approaches and

application possibilities (Vdovychenko, 2018)..

An important aspect that speaks in favor of Big

Data is the statement of analysts that education and

medicine will be promising areas for growth in the

use of the technology. With a small investment in

“big data”, they immediately begin to give tangible

returns. According to a number of experts, the

massive introduction of Big Data in these industries

can improve the life quality of people in the shortest

possible time (Savchuk, 2014).

2 THE ANALYSIS OF RECENT

RESEARCH AND

PUBLICATIONS

A lot of research has been carried out recently in the

field of education using Big Data technology, but the

results are scattered, and yet answers to many

questions have not been received. The trends and

results of some researches are considered as it

follows.

The group of scientists led by J. Okumpau,

analyzing Big Data, come to the conclusion that the

revealed patterns, taken mainly from one

demographic group, do not generalize the results

taken from other demographic groups. For example,

the patterns found for urban students are not similar

to the patterns built for rural students (Ocumpaugh et

al., 2014).

In their work, M. Nasiri, B. Minai, F. Wafai

consider a forecasting model for academic success

through monitoring and support the first-year

students (Nasiri et al., 2012). In the works of O.

Moscozo-Ts ea, M. Fitzkaino, S. Luyan-Mora, the

analysis of Big Data methods was carried out in terms

of two indicators of educational efficiency: student

dropout and student graduation (Moscoso-Zea et al.,

2017).

The work of I. Jugo, B. Kovacic and V. Slavui,

represents an interest, which describes the experience

of creating adaptive e-learning systems that use data

intellectual analysis tools to improve the adaptability

of the learning system (Jugo et al., 2016).

R. Asif, analyzing Big Data, found that the

concentration of pedagogical efforts on analytics and

learning results accounting in a small number of

specialized academic disciplines can contribute to the

effectiveness of learning (Asif et al., 2017).

In the works of K. Khare, H. Lam, A. Khare, the

analytics of students' success using massive online

courses is considered, which allows scientists to

predict the probability of refusal to study the course

(Khare et al., 2017).

In their researches, scientists consider Big Data

aspects related to the interaction of learning subjects.

G. Mobasher, A. Shawish, O. Ibrahim describe the

structure of a large database in education, which,

among other things, contains the demographic data of

students, the psychological characteristics of

students, teachers and parents (Mobasher et al.,

2017). In the works of V. Tem, an approach to the

joint learning organization is described, which makes

it possible to identify educational patterns based on a

varied set of educational online resources (Tam et al.,

2016).

Based on the analysis of many approaches and

models in his statements, I.D. Frumin identifies three

Big Data major areas:

1. Associated with thinking (primarily critical and

creative);

2. Associated with interaction with others

(communication and collaboration);

3. Associated with interaction with oneself (self-

regulation, reflexivity, self-organization).

It is clear that the results of analytics in these areas

are most valuable when they reveal abnormal and

borderline states of the educational system

(Liebowitz, 2016). R. Asif when analyzing Big Data,

found that the concentration of pedagogical efforts on

analytics and accounting the results of learning in a

small number of specialized academic disciplines can

contribute to the effectiveness of learning.

Consequently, the development of Big Data

technology in education is considered by many

scientists, but these are disparate approaches, there is

Big Data Aanalytics in Higher Education

437

no data systematic analysis on the use of Big Data

for the development of the education system. The

research results application for positive changes in the

development of higher education system were not

noted. We consider it important and relevant to

substantiate and propose certain trends of processes

research in higher education system using big data

analytics. In our opinion, these are the tasks having

solved them it is possible to significantly improve the

educational process in universities.

3 PRESENTATIONS OF THE

MAIN MATERIAL

If Big Data technology used, then the information

accumulated in the higher education system is

considered and analyzed would be a colossal source

of a unique new information. With its help, it is

possible to improve, transform the training system,

rethink approaches, reduce the time and efforts for

solving standard problems, and introduce ready-made

experience that gives the best result. We propose to

highlight the following arrays of information in

universities:

 Methodical information.

 Educational literature.

 Archives (diplomas, coursework, practical

works).

 Personal data of students (biographical data, data

on academic performance and attendance.

 Personal data of university employees

(biographical data, scientific, educational and

administrative work).

 Administrative data.

 Current information.

 Results of sessions.

Logical-functional diagram of Big Data processes in

the education system is shown in Fig.1.

Big Data technologies develop analytic indicators on

the base of this information. The following types of

analytic indicators are defined:

 descriptive analytics;

 predictive analytics;

 prescriptive analytics.

The point of descriptive analytics is to compose an

objective and most accurate description of the current

situation using the available data.

Descriptions are usually visualized through

graphs, charts, infographics, according often to

averaged data. The task is to turn huge arrays of

numbers and graphs into accessible, understandable

and easily perceived information.

The purpose of the models used in predictive

analytics is to predict events based on a comparison

of historical and current data. Most often, predictive

analytics is used to identify in advance students who

are inclined to abandon their studies and therefore

need special attention from the teacher.

Figure 1: Logical-functional diagram of Big Data processes

in the education system.

Prescriptive analytics answers the question

"what’s to be done?" Such reports not only indicate

which of students is worth paying attention to and

what exactly he is not coping with, but also give

recommendations for what direction is to change the

educational trajectory. For this, the algorithms use

generalized information about the actions of previous

users with similar characteristics (Leviev, 2021).

Let's look at examples of using Big Data

technology in universities in different countries.

According to statistics, 400,000 students in the

United States are expelled annually. The outflow of

ISC SAI 2022 - V International Scientiﬁc Congress SOCIETY OF AMBIENT INTELLIGENCE

438

students negatively affects the educational

institutions themselves: the greater the outflow, the

less profits and the state’s financial support. The

college's position in national rankings is falling.

To solve the problem, the University of the

Commonwealth of Virginia, together with the

Education Advisory Board, conducted a study that

made it possible to identify students at risk and help

them. At the University a platform was created, which

aggregates all student grades and searches for the

problems. As a result of the use of Big Data, within

one semester the number of students who completed

the course increased by 16%, and the number of

students who were promoted to the next course of

study increased by 8 percent.

Nottingham Trent University of England

implemented an interactive system of descriptive

analytics of student results in the form of a dashboard

that showed data on student engagement in the

educational process. The dashboard was designed to

reduce student dropout rates, improve attendance and

increase a sense of membership of the university

community.

The monitor panel, which is available to students,

teachers and curators (tutors), displays the indicators

of the involvement of each student in comparison

with his classmates:

 frequency of work with at the library,

 information of the courses studied,

 attendance,

 participation in competitions,

 and other educational indicators.

Thus, any student can watch his own activity and

compare himself with fellow students in order to

understand how much he is involved in the

educational process and the life of the university as a

whole, and to what aspect should be paid more

attention. If a student does not show signs of activity

within two weeks, the platform sends notifications to

tutors so that they can quickly contact the student and

support him. 3 years after the implementation of the

system, the results of a university survey showed that

72% of freshmen used this Big Data student

dashboard and it inspired them to increase the amount

of time spent for studying.

At the American Austin Pie University, a referral

system was introduced which helps students choose

and be enrolled in educational courses. The inputs

used are the learning outcomes of previous students

for a specific course, the performance of each student,

and information about students with similar profiles

and interests. Based on the analysis of this Big Data

information, the system using Machine Learning

algorithms, selects training courses that best match

the interests, abilities and curriculum of an individual

student. The accuracy of the recommendations is

estimated at 90% (Leviev, 2021).

Ball State University in Indiana uses Big Data to

analyze student participation in a variety of campus

activities. This parameter is considered to be the key

in terms of academic success. The University

monitors the frequency of campus visits and events.

This approach has contributed to improved learning

results. And there are many similar examples.

At the North Carolina University (USA) in early

2020, a multitasking learning system was presented,

where Big Data models of the system predict the

probability of a student's correct answer based on his

previous behavior in the educational process. This is

useful for informing teachers in case a student may

need additional instructions and it facilitates adaptive

learning functions. Such as changing the storyline, or

prompts, etc., for example (Geden, 2020). And there

are many of such examples.

In recent years, a fundamentally new effect of the

massive application of this approach in data

processing has begun to manifest itself. Scientists are

looking for hidden correlations between the studied

phenomenon (object, process) and thousands of other

factors, where huge statistics accumulated over the

years were used as the initial data. The use of these

empirically discovered patterns promises the progress

in the development of many scientific directions.

Complex modern Big Data models more and more

often reveal some seemingly irrational and fantastic

dependencies, allowing to have a look far beyond the

known scientific picture of the world (Tyndall, 2012).

In this regard, Big Data is sometimes called the "new

astrology of the XXI century." And this is the result

of a smooth transition from the amount of information

to its quality, when machines become capable of

identifying fundamentally new dependencies that

were previously inaccessible to human limited

awareness.

One of the applications of big data is predictive

modeling. By studying the potential university

entrants, it is possible to get important data. Based on

the analysis of this information, the Big Data system

selects a specialty and university that best suits the

interests, experience and personal qualities, abilities,

level of knowledge and financial capabilities of a

particular future student. It is possible to determine

which psycho-type of a student is suitable for

acquiring the specialty, and which one is not. The

accuracy of the recommendations is about 92%.

Those students who want and who are able to get this

University’s specialties, they will come to the

University. This will reduce the percentage of

Big Data Aanalytics in Higher Education

439

dropping out from the university, ensure the quality

of graduates, enterprises will receive good specialists.

Big data analytics will help understand better the

students’ abilities and capabilities. Studying the

psychological peculiarities, behavior, social status,

family customs, national features, cultural level,

emotional personality type and other characteristics,

it is possible to personally select additional

instructions, leading questions, prompts, similar

examples, visualizations, etc., when organizing

distance learning. This will significantly increase its

efficiency, the interest of students in educational

material.

Mikhail Leviev, head of Algo Most, offers his 5

ways of using Big Data in education:

1. Big Data and economy;

2. Personalization of training;

3. Improving the quality of teaching;

4. Choice a future profession;

5. Virtual campus.

These are the most common areas of scientific

research on the use of Big Data in education. We

propose in new directions to find explicit and implicit

reasons, features, coincidences of circumstances and

coincidence of factors that give unique results. These

results must be found, identified, recorded and

repeated for developing the education system,

training highly qualified specialists in various fields,

as well as "heaven-born" teachers who can teach and

transfer knowledge and experience in a qualitative

manner.

In identifying such new directions, we paid

attention to student Olympiads. They are held with

the aim of enhancing the educational and cognitive

activity of students, intensifying and improving the

educational process, stimulating the needs for

creative mastery of knowledge, with the aim of

developing future graduates, identifying and realizing

their creative abilities and scientifically gifted, truly

talented youth. Knowing that the goal of the student

Olympiad is to improve the quality of training

qualified specialists, to search for gifted student

youth, to stimulate their creative work, we understand

that they are the best ones suited for the analysis.

While studying the participants and winners of

student Olympiads, it is necessary to accumulate

explicit and less explicit patterns. This will make it

possible to identify the most effective methods of

preparing for the Olympiads, the most efficient

teaching techniques of certain disciplines, as well as

innovative teachers who are able to convey

knowledge and skills to students in the best possible

way. When studying the results of the Olympiads, it

is necessary to pay attention to the following

questions:

 To identify the universities, the students of

which most often become the winners.

 To identify the universities whose students are

often not included in the top 10.

 Which academic disciplines are the worst

covered by students?

 What topics of learning material are the worst

covered by students?

 To reveal the presence / absence of students’

success growth with re-participation.

 To study the dependence of success at the

Olympiad on the course, gender, social living

conditions, etc.

 To find out if there is a connection between

victories at the Olympiads and the kind of sport the

student is engaged in.

 To take into account gender, height, age,

heredity, psychologic type and other explicit and

implicit features of the winners of student subject

Olympiads.

To conduct a study of this issue, information

about students was collected, winners of the second

round of the All-Ukrainian Student Olympiads. The

student Olympiads on technical disciplines were

under consideration as follows:

1. Life safety;

2. Power engineering, electrical engineering and

electro-mechanics;

3. Energy management and engineering;

4. Fundamentals of labor protection;

5. Energy saving and energy management;

6. Civil protection;

7. Casting production;

8. Materials science;

9. Physical metallurgy;

10. Metals heat treatment;

11. Non-metallic materials;

12. Welding;

13. Programming processing on numerically

controlled machines;

14. Computer-aided design and computer modeling

systems in mechanical engineering;

15. Engineering technology;

16. Interchangeability, standardization and technical

measurements;

17. Theoretical mechanics;

18. Resistance of materials;

19. Applied Mechanics;

20. Mechatronics;

21. Machine parts and design bases;

22.Metrology and information-measuring

technology;

ISC SAI 2022 - V International Scientiﬁc Congress SOCIETY OF AMBIENT INTELLIGENCE

440

23. Metrology, standardization and certification;

24. Radio engineering;

25. Electronic devices;

26. General electrical engineering;

27. Theoretical foundations of electrical engineering;

28. Descriptive geometry and geometric modeling on

a PC;

29. Mathematics;

30. Programming;

31. System programming.

The input information array was based on

information about students of universities in the city

of Krivoy Rog, engineering specialties, such as:

transportation engineer, mechanical engineer,

road transport engineer, heat and power engineer,

electrical engineer, builder, mechanical engineer,

metallurgist, electro-mechanical engineer, process

engineer, programmer, systems engineer, automated

control system engineer, electronics engineer,

electrician, analyst, miner, ore dressing engineer.

The information was collected from 2010 to

2021. The following fields in the database were

obtained after systematization:

sequential number, university, specialty, course,

gender, age, height, weight, hair color, eye color,

nationality, psychological type, type of

temperament, chronic diseases, parents' age (f/ m),

parents' (f/m), living conditions, marital status, sport,

music education, art education, participation in

amateur performances, gastronomic priorities,

hobbies, favorite color, favorite season, favorite

flower, pets available. As an example, partially

collected information is represented in Table 1.

Table 1: Information for analysis.

S/No 1 2

University knu knu

Specialty komп meh

Course 4 3

Gender g m

Age 21 20

Height 168 183

Height 57 78

Hair color sv.r shat

Eye color ser kar

Nationality ukr ukr

Psychological type int ekst

Type of temperament mel hol

Chronic diseases - -

Father’s age 45 47

Mother’s age 42 41

Father’s education vis sr.t

Mother’s education vis vis

Living conditions otl otl

Marital status holl holl

Sport voll box

Music education - -

Art education + -

Participation in amateur + +

Gastronomic priorities sl ostr

Hobbies put bok

Favorite color roz kr

Favorite season leto leto

Favorite flower liliy rom

Pets available + +

The information was processed, qualitative

indicators were converted into quantitative ones,

ranked and received input data for analysis using the

R programming language, as one of the leading tools

for Big Data technology. An example of some data

obtained is presented in Table 2.

Table 2: Modified information for analysis.

S/No 1 2

University 1 2

Specialty 4 8

Course 4 3

Gender 1 2

Age 21 20

Height 168 183

Height 57 78

Hair color 5 3

Eye color 4 1

Nationality 1 1

Psychological type 2 1

Type of temperament 1 3

Chronic diseases 0 0

Father’s age 45 47

Mother’s age 42 41

Father’s education 1 2

Mother’s education 1 1

Living conditions 1 1

Marital status 1 1

Sport 6 4

Music education 0 0

Art education 1 0

Participation in amateur 1 1

Gastronomic priorities 1 4

Hobbies 2 5

Favorite color 7 6

Favorite season 3 3

Favorite flower 4 8

Pets available 1 1

Some information was obtained by questioning

Olympiad’s winners.

Big Data Aanalytics in Higher Education

441

QUESTIONNAIRE FORM

We ask you to take part in the research in order to assess, with your help, the real state of affairs in this area

and to suggest proposals for the future. The profile is anonymous. The research data will be used for scientific

purposes only and in a generalized form. We are sincerely grateful for your cooperation!

А. Please, rate the weight of the indicated factors of the problem of Olympiads organization and holding them

on a scale from 0 - none, 1 - the least, etc., to "5" - the highest.

S/N

Factors Factor

wei

Choic

1. Com

lexit

of or

anizin

iads for the entire education s

ste

0 1 2 3 4 5

2. Com

lexit

of conductin

iads for individual universities 0 1 2 3 4 5

3. Difficulty of conducting Olympiads for teachers 0 1 2 3 4 5

4. Difficulty of holding Olympiads for students 0 1 2 3 4 5

5. To what extent will the holding Olympiads improve the quality of specialists

training?

0 1 2 3 4 5

6. To what extent will the holding Olympiads increase the competitiveness of

Ukrainian s

ecialists in the world?

0 1 2 3 4 5

7. To what extent will the holding Olympiads increase the flow of students to

Ukrainian universities from other countries?

0 1 2 3 4 5

8. To what extent will the holding Olympiads increase the accessibility of higher

education for all segments of the population?

0 1 2 3 4 5

9. o what extent will the holding of Olympiads contribute to the democratic reforming

of the education s

stem?

0 1 2 3 4 5

10. To what extent does Ukrainian le

islation stimulate the holdin

of Ol

iads? 0 1 2 3 4 5

11. What is the degree to which it is necessary to introduce assessment of students'

knowled

e at Ol

iads on a uniform Euro

ean scale?

0 1 2 3 4 5

12. To what extent does the holding of Olympiads activate the atmosphere of novelty

and innovation in the work of teachers?

0 1 2 3 4 5

13. To what extent does the holding of Olympiads activate the atmosphere of novelty

and innovation in the work of students?

0 1 2 3 4 5

14. To what extent will the holding of Olympiads contribute to the implementation of a

creative a

roach to learnin

0 1 2 3 4 5

15. To what extent will Olympiads increase the competitiveness of specialists in the

Ukrainian market?

0 1 2 3 4 5

16. To what extent is the current level of a student's preparation for the Olympiad

determined by knowledge and competence, but not by the time spent for

conventional trainin

and learnin

0 1 2 3 4 5

17. To what extent does Olympiad holding guarantee a coordinated approach to quality

standards for transnational learning?

0 1 2 3 4 5

18. To what extent will a wide European market be accessible for Ukrainian students

mobility after winning Olympiad?

0 1 2 3 4 5

19. To what extent will victories at Olympiad affect the competitiveness of students in

the world?

0 1 2 3 4 5

20. What degree of reforming the higher education system will be achieved by active

and systematic of Olympiads holding in Ukraine?

0 1 2 3 4 5

21. To what extent is it necessary to involve students in the development of a working

concept for holding Olympiads in Ukraine?

0 1 2 3 4 5

22. How profitable is the European higher education system influence on the national

conce

t of Ol

iads for Ukraine?

0 1 2 3 4 5

23. To what extent will the reduction in the volume of Olympiads affect the level of

trainin

of s

ecialists?

0 1 2 3 4 5

24. To what extent will internationally funds and grants for the development of

education stimulate the holding of Olympiads in Ukraine?

0 1 2 3 4 5

25. To what extent should the student community take part in running Olympiads on

the basis of e

ual

artnershi

0 1 2 3 4 5

26. To what extent is the European higher education system a guarantee for intellectual

learnin

0 1 2 3 4 5

27. To what extent will

ou contribute to the

rocess of holdin

iads in Ukraine? 0 1 2 3 4 5

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442

B. Do you agree with the statements presented?

No Difficult

to sa

Yes Choice

1. Innovations in the concept of holding Olympiads in Ukraine is a

key issue, with the adoption of which cardinal changes in the

development of higher education in Ukraine will begin.

0 2 5

2. Will the independent assessment of the Olympiads ensure the

artici

ation of international ex

erts?

0 2 5

3. Is it realistic to form a uniform European space for holding

Olympiads in higher education?

0 2 5

4. Do the disciplines and tasks that are submitted for Olympiads

meet the requirements of the Ukrainian labor market?

0 2 5

5. Do you approve the expression: "Education is a public

ert

0 2 5

6. Is the foreign expert’s participation obligatory in assessing the

ualit

of Ol

iads in Ukraine?

0 2 5

C. If the level of the Olympiads in the European Union is taken as 5 points, then what level

1 Conducting Olympiads in engineering and technical

disciplines in Ukraine

0 1 2 3 4 5 6 7 8 9 10

An example of a part of the obtained results of questioning the Olympiads winners on the problems of holding

student Olympiads in Ukraine are presented in Table 3.

Table 3: Part of the final array with the survey results.

433 1 5 2 3 2 3 3 115214442433333422025553

432 1 1 3 2 4 2 2 034454343222112232525053

252 1 4 0 0 0 3 0 032301105112111210522050

444 3 5 0 0 3 2 3 324502200552245122520000

545 2 4 0 0 0 2 0 023010000221004300022010

544 4 5 2 4 2 3 3 242454453014355330525558

543 1 2 2 0 0 4 0 034400000201005500002024

434 5 5 1 0 2 0 0 123001002100001315002006

555 5 1 0 0 0 3 0 011510003530013000500200

345 2 5 3 5 3 4 5 255315534342245040500555

444 2 4 2 4 2 3 3 254454332123343310522247

333 3 4 3 3 2 5 3 333433333333333335502255

445 3 4 0 3 0 1 0 354054440143044322500525

555 5 5 1 0 2 3 1 120500000012211212002503

543 4 4 2 4 2 3 5 241452443342434532525555

112 1 3 0 1 0 2 0 053351111443251120502206

222 2 4 3 2 2 2 3 003332224112344440502225

222 2 5 4 5 3 3 4 424453341324353142522255

555 5 3 0 0 0 4 0 012503331011005012200008

000 0 5 3 3 5 5 5 354255555435555342255555

334 2 2 2 0 3 1 3 241224453232324325525256

453 5 3 5 5 3 3 4 434235554353334442525503

232 4 1 2 1 1 2 1 225212222322334110522003

433 5 4 3 1 2 0 1 232133322221122322000223

133 4 2 5 3 2 2 1 455355555455555345255553

555 5 3 3 3 1 1 3 042334443235555442522025

334 4 3 5 5 5 2 0 021443432342122220002225

434 5 3 2 2 3 3 0 022224443343223410250528

222 4 2 1 0 0 0 0 032000000200000000000003

444 3 5 2 2 2 0 0 033443543344554132505023

Big Data Aanalytics in Higher Education

443

In the result of our research, a large amount of

data was obtained, among which the following are

the most interesting:

1. At least one of the parents of the Olympiad

winners has a higher education.

2. The age difference between father and mother

of 75% was more than three years.

3. Hair color - predominantly brunette.

4. Psychological type - mostly introverts.

5. Type of temperament - mostly sanguine.

6. The kind of sport is tough single combats

(boxing, karate, etc.)

7. 46% have art, music or choreographic

education.

8. 62% participate in amateur performances.

9. 72% named red as their favorite color.

10.51% have pets.

From this array, you can find overlapping

dependencies, coinciding "chances" and many other

indicators with which you can determine a potential

winner of Olympiad, devote more time to him for

preparation.

Considering that this is the beginning of a large

study, we hope that Big Data technology will

provide an opportunity to identify many important

and interesting connections.

The results of this information analysis can be

used for serious changes in the educational process.

It is very interesting to use Big Data to analyze

teaching staff. To do this, among others, you can pay

attention to such questions: to identify the age

characteristics of teachers.

To determine from which universities:

 the graduates do not change their qualifications;

 the largest percentage of graduates get a job in

their specialty;

 the largest number of graduates become

lecturers (teachers) with scientific degrees (the

highest category).

To study the peculiarities of teaching in these

universities, the experience of their best teachers, the

atmosphere in the educational institution and much

more, which seems less significant, but in the end, it

solves a lot.

It is very important to use Big Data technologies

to identify patterns and characteristics of families

formed by teacher’s dynasties. This technology will

be able to disclose those connections, which are

invisible to man, characteristics, features that form a

true teacher "from God."

By studying the pedagogical universities

applicants, Big Data will make it possible not to

invite random people who cannot be good teachers

in terms of their spiritual qualities, or their outlook,

or professional knowledge.

4 CONCLUSIONS

The considered Big Data technology, with the help of

which the patterns in the development of higher

education system are revealed, in such a small part of

it as student subject Olympiads, gives great

opportunities and hopes.

The presented results of the study of the student

subject Olympiads winners’ characteristics using Big

Data technology, as well as their opinions on the

importance of student Olympiads, obtained through

the questionnaire survey, provide information for

thought and application.

The results of Big Data application to the huge

amount of data accumulated in the higher education

system will give grounds for changing curricula, the

requirements, methods and approaches to teaching

students. Having implemented the proposed research

directions in the work, it allows getting a colossal

source of unique, new and useful data, on the base of

which it is possible to change the educational system

for the better.

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