Deep neural network-based student performance prediction with hessian-free optimization

Irawan, Andy, Abidin, Zainal ORCID: https://orcid.org/0000-0002-9261-4952 and Jamhuri, Mohammad (2026) Deep neural network-based student performance prediction with hessian-free optimization. Jurnal Riset Mahasiswa Matematika, 5 (4). pp. 267-277. ISSN 2808-4926

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Abstract

Predicting student graduation predicates is important for academic monitoring and timely intervention in higher education. This study investigates graduation predicate prediction using deep neural networks under three feature-group settings: academic-only, non-academic-only, and combined academic–non-academic features. A multilayer perceptron with three hidden layers was trained using SGD with momentum, RMSProp, Adam, and a damped Hessian-free optimization procedure. Two tasks were considered: a four-class graduation predicate classification task and a binary risk-screening task in which Sufficient was treated as the positive risk class. The results show that the combined feature group achieved the best multiclass performance, with an accuracy of 0.8478 and a weighted F1- score of 0.8274. Hessian-free optimization consistently produced the best results across all feature-group scenarios, with the clearest gain appearing in the non-academic-only setting. In the additional risk-screening analysis, non-academic variables provided meaningful but limited predictive signal, and Major emerged as the strongest individual predictor. These findings show that combining academic and non-academic information improves graduation predicate prediction and that Hessian-free optimization is an effective training strategy for deep neural classification in educational data.

Item Type:	Journal Article
Keywords:	deep neural networks; educational data mining; graduation predicate prediction; Hessian-free optimization; risk screening.
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity 08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080299 Computation Theory and Mathematics not elsewhere classified
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Andy Irawan
Date Deposited:	22 Jul 2026 08:51

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