Improving Student Grade Prediction in Imbalanced Multi-Class Contexts: A Subset-Based Stacked Support Vector Machine Approach

Improving Student Grade Prediction in Imbalanced Multi-Class Contexts: A Subset-Based Stacked Support Vector Machine Approach | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 November 2025 V1 Latest version Share on Improving Student Grade Prediction in Imbalanced Multi-Class Contexts: A Subset-Based Stacked Support Vector Machine Approach Author : Mohamed Merabet [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176418576.62400634/v1 136 views 102 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Student grade prediction based on records from past semesters helps educational institutions develop data-driven strategies to improve the learning experience. However, most existing research focuses on binary classification (pass/fail) using balanced datasets, which may not accurately reflect real-world scenarios. This article introduces a Subset-Based Stacked Support Vector Machine Approach for handling imbalanced multi-class classification problems. The proposed approach constructs a stacked Support Vector Machine (SVM) classifier using different subsets of the data, combined with Bayesian hyperparameter optimization. First, we perform Linear Discriminant Analysis (LDA) to select a relevant set of features. Next, we train individual SVM classifiers on distinct subsets of the data. Following this, we employ Bayesian optimization to tune the hyperparameters of each SVM model, enabling the identification of optimal support vectors. Finally, we create a stacked SVM model trained on the entire dataset. The proposed method was evaluated using two imbalanced multi-class datasets and compared with six existing methods. The experimental results, based on 10 independent runs, demonstrate that our method consistently outperforms all other methods, achieving an average improvement of 6.6% to 9.7% across all evaluated metrics. These results underscore the effectiveness of the Subset-Based Stacked Support Vector Machine Approach for predicting student grades in the context of imbalanced multi-class datasets. Supplementary Material File (improving student grade prediction.pdf) Download 687.08 KB Information & Authors Information Version history V1 Version 1 26 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords grade prediction imbalanced multi-class support vector machine Authors Affiliations Mohamed Merabet [email protected] Universite Djillali Liabes de Sidi Bel Abbes View all articles by this author Metrics & Citations Metrics Article Usage 136 views 102 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohamed Merabet. Improving Student Grade Prediction in Imbalanced Multi-Class Contexts: A Subset-Based Stacked Support Vector Machine Approach. Authorea . 26 November 2025. 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