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Online learning and MOOCs have become increasingly popular in recent years,and the trend will continue, given the technology boom. There is a dire need toobserve learners' behavior in these online courses, similar to what instructorsdo in a face-to-face classroom. Learners' strategies and activities becomecrucial to understanding their behavior. One major challenge in online coursesis predicting and preventing dropout behavior. While several studies have triedto perform such analysis, there is still a shortage of studies that employdifferent data streams to understand and predict the drop rates. Moreover,studies rarely use a fully online team-based collaborative environment as theircontext. Thus, the current study employs an online longitudinal problem-basedlearning (PBL) collaborative robotics competition as the testbed. Throughmethodological triangulation, the study aims to predict dropout behavior viathe contributions of Discourse discussion forum 'activities' of participatingteams, along with a self-reported Online Learning Strategies Questionnaire(OSLQ). The study also uses Qualitative interviews to enhance the ground truthand results. The OSLQ data is collected from more than 4000 participants.Furthermore, the study seeks to establish the reliability of OSLQ to advanceresearch within online environments. Various Machine Learning algorithms areapplied to analyze the data. The findings demonstrate the reliability of OSLQwith our substantial sample size and reveal promising results for predictingthe dropout rate in online competition.

Keeping Teams in the Game: Predicting Dropouts in Online Problem-Based Learning Competition