Nur Maisarah Nor Azharludin, Khyrina Airin Fariza Abu Samah, Mohamad Faiz Dzulkalnine, Ahmad Firdaus Ahmad Fadzil, Mohd Nor Hajar Hasrol Jono, Lala Septem Riza
This paper provides a comparative analysis of ensemble learning (EL) algorithms to enhance the confidence-based assessment (CBA) in evaluating student performance. Traditional CBA often suffers from misclassification caused by overconfidence and underconfidence, limiting its accuracy and fairness. To address these challenges, an enhanced CBA-EL model integrating bagging and boosting ensemble algorithms is proposed. Five bagging algorithms, which are random forest (RF), decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), Naïve Bayes (NB), and four boosting algorithms, which are adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were evaluated using a dataset of 276 responses collected from Pre- and Post-Quiz CBA in a discrete structures course. Algorithm performance was evaluated using accuracy, correlation, weighted mean precision (WMP), and weighted mean recall (WMR). RF achieved 73.19% accuracy, 0.725 correlation, 0.751 WMP, and 0.766 WMR, while CatBoost outperformed all with 86.23% accuracy and the highest correlation, WMP, and WMR values, with 0.842, 0.843, and 0.862, respectively. The findings indicate that integrating EL into CBA improves prediction accuracy and supports bias-aware student evaluation. This research advances reliable assessment practices and informs the development of adaptive learning systems. © 2026 Institute of Advanced Engineering and Science. All rights reserved.
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia; Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock