Predictors of Self-Regulated Learning in Malaysian Smart Schools

Abstract

This study sought to uncover the predictors of self-regulated learning in Malaysian smart schools. The sample consisted of 409 students, from six randomly chosen smart schools. A quantitative correlational research design was employed and the data were collected through survey method. Six factors were examined in relation to the predictors of self-regulated learning. These factors were levels of IT-integration, student-teacher interactions, motivational beliefs, self-regulative knowledge, information literacy, and attitudes towards IT. Multiple regression analysis showed that levels of IT-integration, student-teacher interactions, motivational beliefs, and self-regulative knowledge significantly predict self-regulated learning in Malaysian smart schools. Self-regulated learning, smart schools, levels of IT-integration, motivational beliefs, student-teacher interactions