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Problem-Based Learning (PBL) is a popular approach to instruction thatsupports students to get hands-on training by solving problems. Question Poolwebsites (QPs) such as LeetCode, Code Chef, and Math Playground help PBL bysupplying authentic, diverse, and contextualized questions to students.Nonetheless, empirical findings suggest that 40% to 80% of students registeredin QPs drop out in less than two months. This research is the first attempt tounderstand and predict student dropouts from QPs via exploiting students'engagement moods. Adopting a data-driven approach, we identify five differentengagement moods for QP students, which are namely challenge-seeker,subject-seeker, interest-seeker, joy-seeker, and non-seeker. We find thatstudents have collective preferences for answering questions in each engagementmood, and deviation from those preferences increases their probability ofdropping out significantly. Last but not least, this paper contributes byintroducing a new hybrid machine learning model (we call Dropout-Plus) forpredicting student dropouts in QPs. The test results on a popular QP in China,with nearly 10K students, show that Dropout-Plus can exceed the rivalalgorithms' dropout prediction performance in terms of accuracy, F1-measure,and AUC. We wrap up our work by giving some design suggestions to QP managersand online learning professionals to reduce their student dropouts.

Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites