Student Motivation and Learning in Mathematics and Science: A Cluster Analysis

Abstract

The present study focused on an in-depth understanding of student motivation and self-regulated learning in mathematics and science through cluster analysis. It examined the different learning profiles of motivational beliefs and self-regulatory strategies in relation to perceived teacher autonomy support, basic psychological needs (i.e. autonomy, competence, and relatedness), motivational regulations, and academic achievement. Grounded in self-determination theory, this study examined the learning profiles of 782 students from eight secondary schools in Singapore. The cluster analyzes revealed four distinct learning profiles, and they were compared in association with perceived teacher autonomy support, needs satisfaction, motivational regulations, and grades. Cluster profiling enables teachers to have better understanding of their students’ self-regulated learning so that they can apply effective teaching strategies to foster their motivation. The findings offer a perspective to secondary students’ psychological needs along with some insights into their perceived task value and self-efficacy in the contexts of mathematics and science.