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Position Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 31

Generative Adversarial Networks for students' structure prediction. Preliminary research

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DOI: http://dx.doi.org/10.15439/2022F271

Citation: Position Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 31, pages 113–120 ()

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Abstract. 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\%).

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