Generative adversarial networks for students' structure prediction : preliminary research

• Autorzy: Kozina Agata , Nowosielski Krzysztof , Kes Zdzisław , Sidor Olena , Hernes Marcin , Golec Paweł , Zhanat Korlan

Identyfikatory

DOI

10.15439/2022F271

• Konferencja: 17th Conference on Computer Science and Intelligence Systems
• Języki publikacji: EN

Abstrakty

EN

The effectiveness of the university's functioning and its organizational culture can be improved thanks to the use of machine learning. At Universities, the context of student anticipation is very important from the point of view of the fundamental planning and control functions associated with this specific form of management. The purpose of this study is to present the results of an experiment involving the prediction of student structure based on the use of a machine learning solution (GANs) and comparing them against real data obtained from a registry system of a European public institution of higher education in economic sciences. At universities, there is a clear need to support various components of system management. The experiments revealed that - for 11 out of the 48 examined datasets - the PSI index was in excess of 75\% but was decidedly lower for the remaining sets (with 18 sets assessed below the margin of 50\%).

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 31
• Strony: 113--120
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Kozina Agata

• Wroclaw University of Economics and Business ul. Komandorska 118/120, 53-345 Wrocław, Poland

autor

Nowosielski Krzysztof

• Wroclaw University of Economics and Business ul. Komandorska 118/120, 53-345 Wrocław, Poland

autor

Kes Zdzisław

• Wroclaw University of Economics and Business ul. Komandorska 118/120, 53-345 Wrocław, Poland

autor

Sidor Olena

• Wroclaw University of Economics and Business ul. Komandorska 118/120, 53-345 Wrocław, Poland

autor

Hernes Marcin

• Wroclaw University of Economics and Business ul. Komandorska 118/120, 53-345 Wrocław, Poland

autor

Golec Paweł

• Wroclaw University of Economics and Business ul. Komandorska 118/120, 53-345 Wrocław, Poland

autor

Zhanat Korlan

• Almaty Management University Rozybakiev Street 227, Almaty 050060, Kazachstan

Bibliografia

• 1. D. Hossler and B. Bontrager, Handbook of strategic enrollment management. San Francisco, CA: Jossey-Bass, A Wiley Brand, 2014.
• 2. M. J. Denniss, ‘Anticipatory Enrollment Management: Another Level of Enrollment Management’, vol. 88, no. 1, pp. 10-16, 2012.
• 3. D. Trusheim and C. Rylee, ‘Predictive modeling: linking enrollment and budgeting’, Planning for Higher Education, vol. 40, 2011.
• 4. D. M. West, ‘Big data for education: Data mining, data analytics, and web dashboards’, Brookings, 2012. https://www.brookings.edu/research/big-data-for-education-data-mining-data-analytics-and-web-dashboards/
• 5. X. Shacklock, ‘From bricks to clicks: the potential of data and analytics in higher education’, VOCEDplus, 2016. http://hdl.voced.edu.au/10707/411226
• 6. E. N. Saribekyan, ‘Organizational culture and organizational culture’, Culture: management, economics, law, no. 4, p. 37, 2014.
• 7. V. N. Fedoseev and S. N. Kapustin, ‘Methods of personnel management’, in Analysis of the crop industry, Almaty, 2014.
• 8. A. V. Shelyakina, ‘Corporate culture of the organization’, Young scientist, no. 14, pp. 206-209, 2018.
• 9. M. V. Shumeiko, ‘Typology of corporate culture’, Cyberleninka, p. 8.
• 10. J.A. Gray and M. DiLoreto. "The effects of student engagement, student satisfaction, and perceived learning in online learning environments." International Journal of Educational Leadership Preparation 11.1 (2016): n1.
• 11. S. Z. Gurbuz, Ed., ‘Machine learning techniques for SAR data augmentation’, in Deep Neural Network Design for Radar Applications, Institution of Engineering and Technology, 2020, pp. 163-206. http://dx.doi.org/10.1049/SBRA529E_ch6.
• 12. J. Brownlee, ‘Gentle Introduction to the Adam Optimization Algorithm for Deep Learning’, Machine Learning Mastery, 2017. https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).