The Unraveling Student Success: The Interplay of Learning Motivation, Learning Approaches, and Academic Achievement
DOI:
https://doi.org/10.30596/eji.v7i2.5029Keywords:
intrinsic motivation, extrinsic motivation, task value, surface learning, deep learning, academic achievementAbstract
This study examines academic inspiration in mathematics and learning motivation, the choice between intrinsic and extrinsic motivation, and task value, with a specific focus on the moderating roles of surface and deep learning, and on how to make students highly committed to duty tasks for their academic achievement. This study employed a cross-sectional design and validated questionnaires to collect data from grade 12 math students at various educational institutions (N=571). Random sampling was used to select participants. The study had 32.0% males and 43.8% females in non-urban areas, and 8.9% males and 15.2% females in urban areas, with a mean age of 17.20 (SD = 0.294) and Cronbach's α = 0.86. The findings of this study make it clear that intrinsic motivation, extrinsic motivation, task value, surface learning, and deep learning predict academic achievement quite significantly. Surface and deep learning tactics positively affected students’ academic success. It also found that intrinsic motivation increased the predictive validity of subjective task value for success, and all affected students’ learning outcomes positively.
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