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Procrastination, as an act of voluntarily delaying tasks, is particularly pronounced among students. Recent research has proposed several solutions to modeling student behaviors with the goal of procrastination modeling. Particularly, temporal and sequential models, such as Hawkes processes, have proven to be successful in capturing students’ behavioral dynamics as a representation of procrastination. However, these discovered dynamics are yet to be validated with psychological measures of procrastination through student self-reports and surveys. In this work, we fill this gap by discovering associations between temporal procrastination modeling in students with students’ chronic and academic procrastination levels and their goal achievement. Our analysis reveals meaningful relationships between the learning dynamics discovered by Hawkes processes with student procrastination and goal achievement based on student self-reported data. Most importantly, it shows that students who exhibit inconsistent and less regular learning activities, driven by the goal to outperform or perform not worse than other students, also reported a higher degree of procrastination.
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Relaxed clustered Hawkes process for procrastination modeling in MOOCs
Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis. Traditionally, these models parameterize each process independently and assume that the history of each point process can be fully observed. Such models could however be inefficient or even prohibited in certain real-world applications, such as in the field of education, where such assumptions are violated. Motivated by the problem of detecting and predicting student procrastination in students Massive Open Online Courses (MOOCs) with missing and partially observed data, in this work, we propose a novel personalized Hawkes process model (RCHawkes-Gamma) that discovers meaningful student behavior clusters by jointly learning all partially observed processes simultaneously, without relying on auxiliary features. Our experiments on both synthetic and real-world education datasets show that RCHawkes-Gamma can effectively recover student clusters and their temporal procrastination dynamics, resulting in better predictive performance of future student activities. Our further analyses of the learned parameters and their association with student delays show that the discovered student clusters unveil meaningful representations of various procrastination behaviors in students.
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- Award ID(s):
- 1917949
- PAR ID:
- 10334656
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Issue:
- 35
- ISSN:
- 2374-3468
- Page Range / eLocation ID:
- 4599-4607
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
