Predicting Academic Performance based on Students’ Mathematics Motivation

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Dr. Ariel Peralta Tuazon, Dr. Armando Alonzo Torres


As national development progressively depends on rapid advances in science and technology which in turn require advanced mathematical skills among the country’s populace, enhancing students’ motivation to learn mathematics skilfully gets more importance than ever before. In this context, a descriptive-regression study was conducted to ascertain the predictive ability of the mathematics motivations of fifty-seven students on their academic performance. Results of the study revealed agreement of students manifesting motivational orientations in learning mathematics in terms of intrinsic & extrinsic goal orientation, task value, and control of learning beliefs. However, they manifest less of self-efficacy in learning mathematics and show a slight of anxiety when they were taking tests. Conducted multiple linear regression analysis showed that intrinsic and extrinsic goal orientations, task value, control of learning beliefs, self-efficacy for learning and performance, and test anxiety, taken in combination, were significant predictors of students’ academic performance. Control of learning beliefs and test anxiety, taken singly, could significantly predict academic performance.


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Dr. Ariel Peralta Tuazon, Dr. Armando Alonzo Torres. (2022). Predicting Academic Performance based on Students’ Mathematics Motivation. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(2), 402–409.
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