Title: Learning boosting: enhancing predictive modelling in blended learning environments

Authors: Zhihong Xu; Chin-Hwa Kuo; Chih-Yung Chang; Jinjun Liu; Chunyan Yu

Addresses: School of Computer and Information Engineering, Chuzhou University, Anhui, China ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei 25137, Taiwan ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei 25137, Taiwan ' School of Computer and Information Engineering, Chuzhou University, Anhui, China ' School of Computer and Information Engineering, Chuzhou University, Anhui, China

Abstract: Accurate prediction of student learning outcomes is critical for early intervention and instructional decision-making in blended learning environments. This study proposes learning boosting, a structure-enhanced predictive framework integrating community-aware Louvain clustering with a gradient boosting classification. Student activity graphs are clustered to detect latent behavioural communities, and the resulting structural labels are embedded as features for final prediction. Experiments on real-world data from a blended learning course with 102 students evaluate the method under multiple classification granularities, data modalities, and clustering strategies. Results show that learning boosting consistently outperforms 11 baseline models, achieving an F1-score of 0.892, AUC of 0.883, and recall of 0.903 in the three-class task. Ablation studies confirm the complementary benefits of structural feature extraction and clustering. The findings demonstrate that combining graph-based structural modelling with boosting classifiers offers a robust and interpretable approach to learning analytics, especially in sparse and multimodal conditions.

Keywords: learning boosting; learning analytics; blended learning; student performance prediction; gradient boosting.

DOI: 10.1504/IJAHUC.2026.152175

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.51 No.2, pp.116 - 126

Received: 08 May 2025
Accepted: 31 Jul 2025
Published online: 10 Mar 2026 *

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