Title: Optimising student performance prediction using DTC enhanced with WOA and ECPO

Authors: Lijuan Gao; Caiyan Chen

Addresses: College of Literature and Media, Xi'an Fanyi University, Xi'an, Shaanxi, China ' College of Education, Xi'an Fanyi University, Xi'an, Shaanxi, China

Abstract: To enhance academic outcomes in today's competitive educational environment, institutions must proactively predict student performance, classify students based on skill levels and provide early guidance. Forecasting student success using past performance can help reduce failure rates and implement targeted interventions. Educational data exploration employs analytical techniques to identify trends in teacher and student data, offering valuable insights for planning and forecasting. This study combined two advanced optimisation techniques: the Waldrus Optimisation Algorithm (WOA) and Electric Charged Particles Optimisation (ECPO), with Decision Tree Classification (DTC) to predict student outcomes. Standard performance metrics, including precision, accuracy, recall and F1-score, were used for evaluation. Results indicated that both optimisers significantly improved the model's effectiveness. DTC+ECPO excelled in the 'Acceptable' performance category, while DTC+WOA was highly effective in identifying 'Poor' performance students. These findings validate WOA and ECPO as robust tools for predictive analytics and early academic intervention.

Keywords: student success; DTC; Walrus optimisation algorithm; electric charged particles optimisation.

DOI: 10.1504/IJIPT.2026.153844

International Journal of Internet Protocol Technology, 2026 Vol.19 No.2, pp.99 - 108

Received: 19 Apr 2025
Accepted: 02 Jun 2025
Published online: 28 May 2026 *

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