Background Measurement of students' self-regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and need careful interpretation. Precise and interpretable evaluation of self-regulation skills in a normal K-12 classroom thus poses a challenge.

Objectives The present study proposes and explores a learning analytics method of combining aptitude and event measures to evaluate student's self-regulation skills.

Methods An explorative learning analytics study was conducted in a junior high school mathematics class (N = 20 students), using a three-lesson intervention with digital learning materials. Students first assessed their self-regulation skills with a self-report questionnaire, after which trace logs and observations of student behaviour were collected. Learning sessions were extracted from trace logs, clustered, and linked to learning strategies. Students were clustered by the self-report results and learning behaviour profiles. Session clusters, student behaviour clusters and assignment grades were also tested for association.

Results and Conclusions The detected session and student behaviour types were linked to learning tactics and strategies found in prior studies. Additionally, association was found between self-reported self-regulation skills and the student behaviour obtained from trace logs.

Implications The results demonstrate the feasibility of concurrently using aptitude and event measures on a classroom scale, providing teachers with a tool to evaluate and support self-regulated learning. Combined with further measures like predictive learning analytics, teachers can obtain an early and highly interpretable picture of at-risk students in their classes.