Digital Learning Lab Wins Best Paper Award at International Conference on Educational Data Mining (EDM 2018)
PhD students Jihyun Park (Computer Science), Renzhe Yu (Education), postdoctoral scholar Fernando Rodriguez (Education), and professors Rachel Baker (Education), Padhraic Smyth (Computer Science), and Mark Warschauer (Education) have won the Best Paper Award at the 11th International Conference on Educational Data Mining (EDM) in Buffalo, New York, July 15-18. The title of their paper is "Understanding Student Procrastination via Mixture Models". This paper is supported by the NSF-funded project “Investigating Virtual Learning Environments” in Professor Warschauer’s Digital Learning Lab.
Educational Data Mining is a leading international forum for high-quality research that mines data sets to illuminate on learning processes. The overarching goal of the Educational Data Mining research community is to better support diverse learners by developing data-driven understandings of the learning process in a wide variety of contexts.
Time management is crucial to success in online courses in which students can schedule their learning on a flexible basis. Procrastination is largely viewed as a failure of time management and has been linked to poorer outcomes for students. Past research has quantified the extent of students’ procrastination by defining single measures directly from raw logs of student activity. In this work, we use a probabilistic mixture model to allow different types of behavioral patterns to naturally emerge from clickstream data and analyze the resulting patterns in the context of procrastination. Moreover, we extend our analysis to include measures of student regularity – how consistent the procrastinating behaviors are – and construct a composite Time Management Score (TM). Our results show that mixture modeling is able to unveil latent types of behavior, each of which is associated with a level of procrastination and its regularity. Overall, students identified as non-procrastinators tend to perform significantly better. Within non-procrastinators, higher levels of regularity signify better performance, while this may be the opposite for procrastinators.