Predicting students' achievement during COVID-19 mitigation through self-regulated learning profiles: Indonesian context

Abstract

The study of the various impacts of the spread of COVID-19 in multiple fields is significant now, including in education. This study aims to predict the success of online learning conducted during the COVID-19 mitigation period. Predictions were made using data from the self-regulated learning profile of students in grades 1 through 12. Data was taken using an online questionnaire on aspects of SRL (Panning, Monitoring, Controlling, and Reflecting). The scale used is 1 (Strongly Disagree) to 5 (Strongly Agree). The analysis used is cluster analysis. The results show that three clusters can be identified as clusters that have the possibility of low, medium, and high learning achievement by being characterized in terms of SRL. By comparing SRL profiles, school management can prepare policies to anticipate students’ performance and to improve the processes that are running in online learning.