Article: Data-driven teaching quality monitoring with a transformer-GNN hybrid Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.35 pp.21 - 36 Abstract: Universities increasingly log dense streams of student interactions, yet most dashboards act only after learning issues have become visible. To enable earlier and cleaner detection, this study introduces a Transformer-GNN hybrid model for data-driven monitoring of teaching quality. A temporal encoder first learns long-range dependencies from sequential event data, while a graph encoder integrates cohort and course context. A reliability-aware gate and per-family calibration then refine alert stability and accountability. Across three academic terms covering six course families, the model improved the area under ROC by 2.4 points and the area under precision-recall by 5.7 points over a strong fusion baseline, reduced calibration error by 45%, and extended mean warning lead time by 1.3 days. The framework remains robust under pacing shifts and provides interpretable, actionable explanations for instructors. Inderscience Publishers - linking academia, business and industry through research

Title: Data-driven teaching quality monitoring with a transformer-GNN hybrid

Authors: Ping Du

Addresses: School of Media and Design, Xi'an PeiHua University, Xi'an, 710000, China

Abstract: Universities increasingly log dense streams of student interactions, yet most dashboards act only after learning issues have become visible. To enable earlier and cleaner detection, this study introduces a Transformer-GNN hybrid model for data-driven monitoring of teaching quality. A temporal encoder first learns long-range dependencies from sequential event data, while a graph encoder integrates cohort and course context. A reliability-aware gate and per-family calibration then refine alert stability and accountability. Across three academic terms covering six course families, the model improved the area under ROC by 2.4 points and the area under precision-recall by 5.7 points over a strong fusion baseline, reduced calibration error by 45%, and extended mean warning lead time by 1.3 days. The framework remains robust under pacing shifts and provides interpretable, actionable explanations for instructors.

Keywords: transformer; graph neural network; teaching quality monitoring; early warning; educational analytics; calibration; interpretability.

DOI: 10.1504/IJICT.2026.153006

International Journal of Information and Communication Technology, 2026 Vol.27 No.35, pp.21 - 36

Received: 21 Oct 2025
Accepted: 17 Nov 2025
Published online: 17 Apr 2026 *

Title: Data-driven teaching quality monitoring with a transformer-GNN hybrid

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