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Using data mining to create innovations in education

Title: 


Using data mining to create innovations in education


Authors: 



Chala, Nina
Voropai, Olga
Pichyk, Kateryna


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
http://orcid.org/0000-0001-5257-7619
http://orcid.org/0000-0003-1161-270X


DOI:


UDC: 


https://doi.org/10.33108/sepd2022.02.021


004.8:378


JEL: 


С10
С13
С89
I20
I29


Keywords: 


Data Mining technology
social networks
clusters
educational institutions
educational services
innovations in education


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
https://doi.org/10.1145/3299815.3314441
https://doi.org/10.1080/08841241.2015.1083512
https://www.mckinsey.com/industries/public-and-social-sector/our-insights/how-higher-education-institutions-can-transform-themselves-using-advanced-analytics?cid=app
https://doi.org/10.1016/j.jbusres.2016.02.010
https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-strategy-analytics-revolution?cid=other-eml-alt-mip-mck&hdpid=d040c36b-ab38-4318-8f3e-e01884025bba&hctky=3072032&hlkid=3a7d50730d574ff0bbf82bf62bb74d3d
https://www.researchgate.net/publication/220876232_The_Effect_of_Post_

Type_Category_and_Posting_Day_on_User_Interaction_Level_on_Facebook
https://doi.org/10.1007/s10639-016-9501-1
https://doi.org/10.1080/09523987.2017.1324363


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
2. Galan M., Lawley M., Clements, M. (2015) Social media’s use in postgraduate students' decision-making journey: An exploratory study. Journal of Marketing for Higher Education, 25, 287–312 DOI: 10.1080/08841241.2015.1083512
3. Krawitz M., Law J., Litman S. (2018) How higher-education institutions can transform themselves using advanced analytics. McKinsey and Company, 1-6. Available at: https://www.mckinsey.com/industries/public-and-social-sector/our-insights/how-higher-education-institutions-can-transform-themselves-using-advanced-analytics?cid=app (accessed 19 August 2021)
4. Moro S., Rita P., Vala B. (2016) Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach, Journal of Business Research, Volume 69, Issue 9, 2016, Pages 3341-3351, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2016.02.010
5. Mulligan Ch., Northcote N., Röder T., Vesuvala S. (2021) The strategy-analytics revolution. McKinsey and Company, 1-6. Available at: https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-strategy-analytics-revolution?cid=other-eml-alt-mip-mck&hdpid=d040c36b-ab38-4318-8f3e-e01884025bba&hctky=3072032&hlkid=3a7d50730d574ff0bbf82bf62bb74d3d (accessed 4 September 2021)
6. Pletikosa I., Spiegler E., Michahelles F. (2011). The Effect of Post Type, Category and Posting Day on User Interaction Level on Facebook. 810-813. 10.1109/PASSAT/SocialCom.2011.21. Available at: https://www.researchgate.net/publication/220876232_The_Effect_of_Post_Type_

Category_and_Posting_Day_on_User_Interaction_Level_on_Facebook (accessed 4 September 2021)
7. Singh, A. (2017) Mining of Social Media data of University students. Education and Information Technologies 22,1515–1526. https://doi.org/10.1007/s10639-016-9501-1
8. Veletsianos G., Kimmons R., Shaw A., Pasquini L., Woodward S. (2017) Selective openness, branding, broadcasting, and promotion: Twitter use in Canada’s public universities, Educational Media International, 54:1, 1-19, DOI: 10.1080/09523987.2017.1324363
9. Voropai, O., Pichyk, K., Chala, N. (2019). Increasing competitiveness of higher education in Ukraine through value co-creation strategy. Economics and Sociology, 2019, 12(4), стр. 214–22
6. 
https://doi.org/10.14254/2071-789X.2019/12-4/14


Content type:

 

Article


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Scientific Journal "Socio-Economic Problems and the State", Vol.25, No.2

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