The Impact of Intrinsic Motivation as Predictors of Academic Achievement: The Mediating Role of Deep Learning and Surface Learning in Learning Mathematics

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.