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The areas of educational data mining and learning analytics focus on the extraction of knowledge and actionable items from data sets containing detailed information about students. However, the potential impact from these techniques is increased when properly contextualized within a learning environment. More studies are needed to explore the connection between student interactions, approaches to learning, and academic performance. Self-regulated learning (SRL) is defined as the extent to which a student is able to motivationally, metacognitively, and cognitively engage in a learning experience. SRL has been the focus of research in traditional classroom learning and is also argued to play a vital role in the online or blended learning contexts. In this paper, we study how SRL affects students' online interactions with various learning activities and its influence in academic performance. The results derived from a naturalistic experiment among a cohort of first year engineering students showed that positive self-regulated strategies (PSRS) and negative self-regulated strategies (NSRS) affected both the interaction with online activities and academic performance. NSRS directly predicted academic outcomes, whereas PSRS only contributed indirectly to academic performance via the interactions with online activities. These results point to concrete avenues to promote self-regulation among students in this type of learning contexts.
Pardo et al. (Fri,) studied this question.