Predictors of Self-Regulated Learning in Secondary Smart Schools and The Effectiveness Of Self management Tool in Improving Self-Regulated Learning

The Smart School Project was implemented in 1999. It aims to systematically reinvent the teaching and learning processes in schools to produce not only knowledgeable and IT-literate students but also self-regulated learners. However, many teachers may not realize the factors related to self-regulated learning. There are needs to uncover these factors as this information may assists teachers in promoting self-regulation in smart schools. In addition, students may not be able to self-regulate their studies efficiently as they are accustomed to the conventional teacher-centered way of learning. Therefore, they need a Self-Management Tool that can guide them to employ self-regulated learning strategies constantly and practically. This tool may improve students' self-regulated learning skills and enables them to manage their studies more efficiently in smart schools. The objective of this study, thus, was twofold. It aimed to identify the predictors of self-regulated learning in secondary smart schools and also to examine the effectiveness of the Self-Management Tool in improving self-regulated learning.A quantitative correlational research design was used to determine the predictors of self-regulated learning. The sample consisted of 409 students, from six randomly chosen smart schools. The data were collected through survey method. Multiple regression analysis showed that levels of IT-integration, student-teacher interactions, motivational beliefs, and self-regulative knowledge were significant predictors of self-regulated learning [A R~ = .51, F (5,403) = 84.48, p < .01]. A quasi-experimental design was employed to test the effectiveness of the Self- Management Tool in improving self-regulated learning. The subjects were taken from a randomly chosen secondary smart school. A total of 61 students were involved; 30 students in the experimental group and 31 students in the control group. After three months of treatment, Analysis of Covariance (ANCOVA) revealed that there seemed to be no true difference in self-regulated learning between the two groups, [F (1, 56) = 2.39, p > .05]. However, eight weeks after that, the experimental group's self-regulated learning was found to be significantly higher than the control group, [F (1, 55) = 31.04, p < .01]. This suggests that the Self-Management Tool was effective in improving students' self-regulated learning

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