Using data mining to create innovations in education
Title: |
Using data mining to create innovations in education |
Authors: |
Chala, Nina |
Affiliation: |
National University of Kyiv-Mohyla Academy, Skovoroda Str., 2, Kyiv, 04070, Ukraine National University of Kyiv-Mohyla Academy, Volos'ka Str., 10, Kyiv, 04655, Ukraine National University of Kyiv-Mohyla Academy, Skovoroda Str., 2, Kyiv, 04070, Ukraine |
Bibliographic description (International): |
Chala, N., Voropai, O. & Pichyk, K. (2021) Using data mining to create innovations in education. Sotsialno-ekonomichni problemy i derzhava [Socio-Economic Problems and the State] (electronic journal), Vol. 25, no. 2, pp. 21-28. Available at: http://sepd.tntu.edu.ua/images/stories/pdf/2021/21cndiie.pdf |
Journal/Collection:
|
Scientific Journal "Socio-Economic Problems and the State" |
Issue: |
2(25) |
Issue Date: |
Nov-2021 |
Submitted date: |
Oct-2021 |
Date of entry: |
12-Jul-2022 |
Publisher: |
Ternopil Ivan Puluj National Technical University
Ternopil Ivan Puluj National Technical University |
Country (code): |
UA |
Place of the edition/event: |
Ternopil |
ORCID Id: |
http://orcid.org/0000-0002-0356-9003 |
DOI: UDC: |
https://doi.org/10.33108/sepd2022.02.021 004.8:378 |
JEL: |
С10 |
Keywords: |
Data Mining technology |
Number of pages: |
8 |
Page range: |
21-28 |
Start page: |
21 |
End page: |
28 |
Abstract: |
The article substantiates the need for educational institutions to use Data Mining technology as a key to successful management decisions in modern realities. The study focuses on working with social media data. The authors emphasized the lack of attention to this issue among both foreign and Ukrainian scientists. The article outlines the algorithm for collecting and transmitting primary data obtained as a result of monitoring the activity of educational institutions in social networks to form models of various types of their actions. The model presented by the authors includes four stages. Stages one and two provide the list of factors / metrics that can be included in the model. These factors require an appropriate and high-quality data collection process. At the next stage, the authors propose data clustering as the most important process for the future use of social network data. It is emphasized that the formation of clusters will depend on the tasks facing the management teams of the educational market. The authors give several examples of such clustering but point out that the list is not exhaustive and can be significantly expanded. An important aspect of the availability of such databases is access to information not only for teachers, but also for all interested university staff. At the same time, each user (students, teachers, staff, administration) will receive data relevant to their requests and needs. The developed methodology will help increase the efficiency of management decision-making and implementation and provide an opportunity to justify the parameters of successful innovation in educational institutions in many respects, including the development of educational programs, implementation of new certification programs and disciplines, other services, etc. |
URI: |
http://elartu.tntu.edu.ua/handle/lib/38467 |
ISSN: |
2223-3822 |
URL for reference material: |
http://sepd.tntu.edu.ua/images/stories/pdf/2021/21cndiie.pdf Type_Category_and_Posting_Day_on_User_Interaction_Level_on_Facebook |
References (International): |
1. Desai S., Meng H. (2019) Social Media Content Analytics beyond the Text: A Case Study of University Branding in Instagram. 94-101. DOI: 10.1145/3299815.3314441 Category_and_Posting_Day_on_User_Interaction_Level_on_Facebook (accessed 4 September 2021) |
Content type:
|
Article |
Appears in Collections: |
Scientific Journal "Socio-Economic Problems and the State", Vol.25, No.2 |