Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Al-Jabar : Jurnal Pendidikan Matematika Educational Psychology Journal
Phan, Bora
Unknown Affiliation
Education Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
The Impact of Intrinsic Motivation as Predictors of Academic Achievement: The Mediating Role of Deep Learning and Surface Learning in Learning Mathematics Phan, Bora
Educational Psychology Journal Vol. 14 No. 2 (2025): December 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/epj.v14i2.34789

Abstract

This study examines the relationship between academic inspiration in mathematics and internal motivation, the chis of extrinsic incentives, subjective task value with a specific focus on the moderate roles played by deep learning and shallow is tape. A cross-sectional research design and validated questionnaires were used to collect data from high school students at the various and total number of educational institutions in one metropolitan area included 571 high school students. Non-urban (32.0​% male and 43.8% female, Urban, 8.9% male and 15.2% female, mean age = 17.20, SD = 0.294, Cronbach's α = 0.720) from Kampong Cham Province, Cambodia. What the findings of this study make clear is that intrinsic motivation quite significantly predicts academic achievement; compared with servant motivation, it even has a big edge. Surface learning tactics negatively affected academic success, while deep learning strategies promoted it. It also found out that subjective task value increased the predictive validity of intrinsic motivation for success. Such findings demonstrate just how complex the relationships are between a great many motivating factors and learning processes, all aspects of which teachers need to nurture in order for their students 'success in math to succeed.
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.
Co-Authors Long, Ratha Ngorntheng, Thol Vuthy, Chem