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 to observe learners' behavior in these online courses, similar to what instructors do in a face-to-face classroom. Learners' strategies and activities become crucial to understanding their behavior. One major challenge in online courses is predicting and preventing dropout behavior. While several studies have tried to perform such analysis, there is still a shortage of studies that employ different data streams to understand and predict the drop rates. Moreover, studies rarely use a fully online team-based collaborative environment as their context. Thus, the current study employs an online longitudinal problem-based learning (PBL) collaborative robotics competition as the testbed. Through methodological triangulation, the study aims to predict dropout behavior via the contributions of Discourse discussion forum 'activities' of participating teams, along with a self-reported Online Learning Strategies Questionnaire (OSLQ). The study also uses Qualitative interviews to enhance the ground truth and results. The OSLQ data is collected from more than 4000 participants. Furthermore, the study seeks to establish the reliability of OSLQ to advance research within online environments. Various Machine Learning algorithms are applied to analyze the data. The findings demonstrate the reliability of OSLQ with our substantial sample size and reveal promising results for predicting the dropout rate in online competition.

翻译：近年来，随着技术革新，在线学习和MOOC（大规模开放在线课程）日益普及，且这一趋势预计将持续。与线下课堂中教师观察学习者行为类似，如今迫切需要关注在线课程中学习者的行为模式。学习者的策略和活动对于理解其行为至关重要，而在线课程的一大挑战在于预测和防止退出现象。尽管已有研究尝试进行此类分析，但采用多数据流来理解并预测退出率的研究仍显不足。此外，很少有研究将完全在线的团队协作环境作为研究背景。因此，本研究以在线纵向问题导向学习（PBL）协作机器人竞赛为试验平台。通过方法三角互证，本研究旨在通过参与团队的讨论论坛话语活动贡献以及自评式在线学习策略问卷（OSLQ）来预测退出行为。同时，采用定性访谈增强真实数据与结果的可信度。OSLQ数据收集自超过4000名参与者。此外，本研究试图验证OSLQ的信度，以推动在线环境下的研究发展。运用多种机器学习算法进行数据分析。结果表明，在较大样本量下OSLQ具有良好信度，并揭示了预测在线竞赛退出率的有效方法。