  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.
