Year: 2026
Type: article
Abstract: The sudden growth in digital learning environments has aided the rise of student datasets that provide a great opportunity to predict and explain complex student behaviour and their performance.However, the goal of predicting academic performance remains very difficult because educational interactions are inherently very complex, multimodal, nonlinear, and imbalanced.To overcome limitations of current state-of-the-art (SOTA) models, this study proposes a Graph-KAN, a paradigm that integrates Kolmogorov-Arnold Networks (KANs) within a heterogeneous graph framework.Instead of relying on fixed activation functions, like in traditional Multi-Layer Perceptrons (MLPs), Graph-KAN takes advantage of the Kolmogorov-Arnold representation theorem to learn polynomial splines along the graph edges, allowing the model to dynamically approximate complex nonlinear education functions.We trained and validated Graph-KAN against standard baselines (GraphSAGE, GATv2, and Transformer-Conv) on the Open University Learning Analytics Dataset (OULAD).Our experimental results show that the hybrid Graph-KAN achieves a Macro F1-score of 0.536, outperforming Transformer-based architectures by
DOI: 10.47974/jdmsc-2825
Authors: Jitendra Gupta, Abhinav Shukla, Aryan Yadav, Vanita Jain
Institutions: Dr. C. V. Raman University, University of Delhi
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W7203728301