A Quantitative Investigation of Student Engagement in Mathematics: Predictors and Group Variations Across Gender and Program
Authors
Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA (UiTM) Terengganu Branch, 23000 Dungun, Terengganu (Malaysia)
Department of Computer & Mathematical Sciences, Universiti Teknologi MARA, Pulau Pinang Branch, 13500 Permatang Pauh, Pulau Pinang (Malaysia)
Nur Solihah Khadiah binti Abdullah
Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kuala Terengganu Branch, 21080 Kuala Terengganu, Terengganu (Malaysia)
Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA (UiTM) Terengganu Branch, 23000 Dungun, Terengganu (Malaysia)
Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kuala Terengganu Branch, 21080 Kuala Terengganu, Terengganu (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.100700110
Subject Category: Education
Volume/Issue: 10/7 | Page No: 1508-1520
Publication Timeline
Submitted: 2026-07-08
Accepted: 2026-07-13
Published: 2026-07-27
Abstract
This study investigates the influence of cognitive, affective, behavioural, and learning approach factors on student engagement in mathematics among higher education students. A total of 342 students enrolled in the Business Mathematics (MAT112) course at Universiti Teknologi MARA Cawangan Terengganu participated in the study. Data were collected using a 32-item questionnaire adapted from previous studies, measured on a five-point Likert scale. The instrument demonstrated good to very good internal consistency with Cronbach's alpha values ranging from 0.767 to 0.852. Data were analysed using Pearson correlation, independent samples t-test, one-way ANOVA, and multiple linear regression. The results revealed significant positive relationships between cognitive, affective, behavioural, learning approach, and student engagement. Female students demonstrated significantly higher engagement than male students, and engagement differed significantly across academic programmes. Multiple linear regression analysis identified affective and behavioural factors as the most influential predictors, followed by cognitive and learning approach factors. The regression model explained 64.1% of the variance in student engagement, indicating good explanatory power. These findings suggest that fostering positive attitudes, active participation, and effective learning approaches may enhance student engagement in mathematics across diverse student groups.
Keywords
Engagement, Cognitive, Affective, Behavioural, Learning Approach, Mathematics
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References
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