Title: Enhancing academic success: a deep dive into students' performance prediction using decision tree classification models

Authors: Tingting Du; Linglanxuan Kong

Addresses: Department of Personnel, Shanghai Customs University, Shanghai, China ' Department of Personnel, Shanghai Customs University, Shanghai, China

Abstract: Education, a fundamental human right, plays a pivotal role in personal and societal advancement, cultivating critical thinking and problem-solving skills, fostering social integration, and contributing to global progress, with a focus on innovative strategies to elevate education standards and prioritise students' performance. Educational data mining (EDM) is a burgeoning field within DM that investigates patterns in education, covering analysis of student knowledge and behaviour, teacher curriculum planning, and course scheduling, all with the primary goal of enhancing student learning performance and achieving efficiency in education systems. This paper addresses the task of predicting and categorising students' performance in the Portuguese language, emphasising decision tree classification (DTC) models, along with two hybrid models optimised using aquila optimiser (AO) and honey badger algorithm (HBA), for a cohort of 649 students. The results underscore the exceptional predictive capabilities of the DTHB model, outperforming the DTAO model in G2 prediction with an impressive F1-score of 0.9428 compared to 0.9381. Additionally, the DTHB model continues to excel in G3 prediction, boasting the best performance at an F1-score of 0.9275.

Keywords: student performance; decision tree; aquila optimiser; AO; honey badger algorithm; HBA; teacher curriculum planning; educational data mining; EDM; course scheduling; decision tree classification; DTC.

DOI: 10.1504/IJRIS.2026.155208

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.4, pp.265 - 278

Received: 06 Sep 2024
Accepted: 09 May 2025
Published online: 29 Jul 2026 *

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