Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors

We study learning outcome prediction for online courses. Whereas prior work has focused on semester-long courses with frequent student assessments, we focus on short-courses that have single outcomes assigned by instructors at the end. The lack of performance data and generally small enrollments makes the behavior of learners, captured as they interact with course content and with one another in Social Learning Networks (SLN), essential for prediction. Our method defines several (machine) learning features based on the processing of behaviors collected on the modes of (human) learning in a course, and uses them in appropriate classifiers. Through evaluation on data captured from three two-week courses hosted through our delivery platforms, we make three key observations: (i) behavioral data contains signals predictive of learning outcomes in short-courses (with classifiers achieving AUCs ≥ 0.8 after the two weeks), (ii) early detection is possible within the first week (AUCs ≥ 0.7 with the first week of data), and (iii) the content features have an 'earliest' detection capability (with higher AUC in the first few days), while the SLN features become the more predictive set over time as the network matures. We also discuss how our method can generate behavioral analytics for instructors.