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Motivation, learning approaches and mathematics achievement: evidence from cambodian high school students Phan, Bora; Long, Ratha; Ngorntheng, Thol; Vuthy, Chem
Al-Jabar: Jurnal Pendidikan Matematika Vol 17 No 1 (2026): Al-Jabar : Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v17i1.29191

Abstract

Purpose: This study aims to examine the relationship between motivational factors and academic achievement in mathematics, with particular attention to intrinsic motivation, extrinsic motivation, and subjective task value. Method: A cross-sectional survey design was employed involving 571 high school students from several educational institutions in Kampong Cham Province, Cambodia. The participants included students from both non-urban (32.0% male, 43.8% female) and urban areas (8.9% male, 15.2% female), with a mean age of 17.20 years (SD = 0.294). Data were collected using validated questionnaires that measured intrinsic motivation, extrinsic motivation, subjective task value, learning approaches, and mathematics academic achievement. The reliability of the instrument was confirmed with a Cronbach’s alpha coefficient of 0.720. Data analysis was conducted using correlation and mediation analyses to identify relationships among the study variables. Findings: The findings indicate that intrinsic motivation significantly predicts students’ academic achievement in mathematics and shows a stronger influence compared to extrinsic motivation. Deep learning strategies are positively associated with academic achievement and support higher levels of conceptual understanding. In contrast, surface learning approaches demonstrate a negative relationship with academic performance. Additionally, subjective task value strengthens the relationship between intrinsic motivation and academic achievement. Significance: These results highlight the importance of fostering intrinsic motivation and promoting deep learning strategies to enhance students’ engagement and achievement in mathematics education.