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Student procrastination and cramming for deadlines are major challenges in online learning environments, with negative educational and well-being side effects. Modeling student activities in continuous time and predicting their next study time are important problems that can help in creating personalized timely interventions to mitigate these challenges. However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior. To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students’ next activity times even when there are no historical observations. Unlike regular point processes that assume a constant external triggering effect from the environment, we model three dynamic types of external stimuli, according to assignment availabilities, assignment deadlines, and each student’s time management habits. Our experiments on two synthetic datasets and two real-world datasets show a superior performance of future activity prediction, comparing with state-of-the-art models. Moreover, we show that our model achieves a flexible and accurate parameterization of activity intensities in students.
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Starters and Finishers: Predicting Next Assignment Completion From Student Behavior During Math Problem Solving
A substantial amount of research has been conducted by the educational data mining community to track and model learning. Previous work in modeling student knowledge has focused on predicting student performance at the problem level. While informative, problem-to-problem predictions leave little time for interventions within the system and relatively no time for human interventions. As such, modeling student performance at higher levels, such as by assignment, may provide a better opportunity to develop and apply learning interventions preemptively to remedy gaps in student knowledge. We aim to identify assignment-level features that predict whether or a not a student will finish their next homework assignment once started. We employ logistic regression models to test which features best predict whether a student will be a “starter” or a “finisher” on the next assignment.
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- Award ID(s):
- 1724889
- PAR ID:
- 10095367
- Date Published:
- Journal Name:
- Proceedings of the Eleventh International Conference on Educational Data Mining
- Page Range / eLocation ID:
- 525-528
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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