Exploring online students’ self-regulated learning with self-reported surveys and log files: a data mining approach

Many researchers who are interested in studying students’ online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students’ SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students’ online SRL patterns with the use of data mining techniques. We examined both self-reported self-regulation surveys and log files to predict online students’ achievements and found using log files was more powerful in predicting students’ achievements in an online course than self-reported survey data. Discussions to enhance teaching and learning practices with the use of data mining are provided.