MODELLING CURRICULA WITH GNN AND LSTM FOR LINK AND SEQUENCE PREDICTION
Kozhanov M. Varkonyi-Koczy A.
2025L.N. Gumilyov Eurasian National University
Eurasian Journal of Mathematical and Computer Applications
2025#13Issue 4159 - 167 pp.
A mathematical model of a university curriculum is proposed. A prerequisite structure is represented as a directed graph, while course order is represented as a sequence. Courses are represented by embeddings that are obtained from syllabus texts and metadata. These vectors are refined by a two layer graph convolutional network (GCN), which is used for link prediction of missing or potentially incorrect prerequisite relations. Course order is represented by a Long Short Term Memory (LSTM) network, which predicts the next course from a fixed window of previously completed courses. An experiment on two bachelor programmes, which are Information Systems and Electric Power Engineering, is reported for 92 courses and 90 explicit prerequisite links. Acceptable quality is obtained for link prediction and sequence reconstruction. Compact tables of recommended new prerequisites are produced for both programmes, which are suitable for curriculum revision decisions.
68Q32 , 68R10 , 68T07 , 68T50 , artificial intelligence , curriculum , data science , educational trajectory , graph neural network , link prediction , machine learning , mathematical modelling , recurrent neural network
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Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
John von Neumann University, Kecskemet, Hungary
J.Selye University, Komarno, Slovakia
Doctoral School of Applied Informatics and Applied Mathematics
Abylkas Saginov Karaganda Technical University
John von Neumann University
J.Selye University
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