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All Journal Jurnal Educative: Journal of Educational Studies Educational Psychology Journal
Bora Phan
National University of Cheasim Kamchaymear
Education Languange, Linguistic, Communication & Media Other

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The Conversation Hub: Explaining Students’ Transition from Surface to Deep Mathematics Learning Bora Phan; Gema Hista Medika
Jurnal Educative: Journal of Educational Studies Vol. 11 No. 1 (2026): June 2026
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/educative.v11i1.11090

Abstract

The transition from surface-oriented learning to deep learning remains a significant challenge in mathematics education, particularly in understanding how students develop deeper conceptual engagement through authentic classroom experiences. This study aimed to develop a substantive theoretical explanation of how Grade 12 students transition from surface-oriented learning toward deep learning in mathematics classrooms. A Constructivist Grounded Theory approach was employed, using semi-structured interviews to collect data from Grade 12 students. Data were analyzed through iterative coding procedures, including initial, focused, and theoretical coding, to identify recurring categories and generate an emerging theoretical model. The findings revealed that students' progression toward deep learning was facilitated through continuous mathematical conversations, enabling them to connect prior knowledge, collaboratively construct conceptual understanding, reflect on mathematical reasoning, and apply knowledge in meaningful contexts. These interconnected learning experiences culminated in the emergence of the Conversation Hub, identified as the core category explaining students' transition from surface-oriented learning to deep learning. This study contributes to mathematics education by providing a substantive theoretical explanation of students' learning processes and offers a practical framework for designing mathematics classrooms that foster collaborative dialogue, conceptual understanding, and meaningful learning..
The Impact of Intrinsic Motivation as Predictors of Academic Achievement: The Mediating Role of Deep Learning and Surface Learning in Learning Mathematics Bora Phan
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.
Co-Authors Gema Hista Medika