We analyzed data from student use of the Learning Management System (LMS) in five undergraduate courses and examined the relationships among social and emotional (SE) skills, as measured by ACT® Tessera®, course features, and course grades. Our goal was to understand how SE skills would impact students’ online behaviors and course outcomes and to explore a principled design approach for feature extraction using individual activities and sequential data mining techniques. In the initial course with the largest amount of LMS use (Introduction to Chemistry), we found low to moderate associations (r = 0.10 – 0.34) between these features and SE skills (grit, teamwork, resilience, curiosity, and leadership) and between these features and course grade (r = 0.18 – 0.36). These behavioral features (i.e., individual activities and learning tactics) were used to create predictive models that accurately predicted SE skills (RMSE = 0.87) and course grade (RMSE = 0.67). These models had higher predictive accuracies than baseline models created from only student background information (including college GPA). ACT has published a full research report and abbreviated Tech Bytes research summary with these results.
These finding suggests a new source of insights that can be used to create accurate and actionable predictions to improve student course grade and SE skills. Furthermore, a mediation analysis revealed a significant partial mediation effect of student online behaviors on the influence of SE skills in final course grade. This indicates that the constructs measured by ACT Tessera influence the daily behaviors of students, which in turn influence class outcomes and student success. No bias based on student demographic background was observed in our predictive models. This research is currently being replicated with the remaining four courses and findings will be presented at educational technology practitioner conferences and in peer-reviewed scientific journals.