Relaxed Clustered Hawkes Process for Student Procrastination Modeling in MOOCs

Authors

  • Mengfan Yao University at Albany, SUNY
  • Siqian Zhao University at Albany, SUNY
  • Shaghayegh Sahebi University at Albany - SUNY
  • Reza Feyzi Behnagh University at Albany, SUNY

Keywords:

Mining of Spatial, Temporal or Spatio-Temporal Da, Education

Abstract

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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Published

2021-05-18

How to Cite

Yao, M., Zhao, S., Sahebi, S., & Feyzi Behnagh, R. (2021). Relaxed Clustered Hawkes Process for Student Procrastination Modeling in MOOCs. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4599-4607. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16589

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management