Relaxed Clustered Hawkes Process for Student Procrastination Modeling in MOOCs
Relaxed Clustered Hawkes Process for Student Procrastination Modeling in MOOCs
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MOOC 中学生拖延建模的宽松聚类霍克斯过程
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Reza Feyzi
中科院分区：
文献类型：
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作者：
Mengfan Yao;Siqian Zhao;Shaghayegh Sherry Sahebi;Reza Feyzi
Hawkes过程已被证明是有效的建模突发
序列在各种应用中，如金融和社交网络活动分析。
传统上，这些模型独立地对每个过程进行参数化，并假设
可以充分观察到每个点过程的历史。
然而，这种模型在某些现实世界中可能是低效的，甚至是被禁止的。
例如，在教育领域，这些假设被违反。
在大规模开放式在线课程（MOOC）中，由于数据缺失和部分观察到的数据，我们提出了一种新的个性化霍克斯过程模型（RCHawkes-Gamma），该模型通过同时联合学习所有部分观察到的过程来发现有意义的学生行为集群，而不依赖于辅助特征。
我们在合成和真实世界的教育数据集上的实验表明，RCHawkes-Gamma可以有效地恢复学生集群及其时间拖延动态，从而对未来的学生活动有更好的预测性能。
我们对学习参数及其与学生拖延的关联的进一步分析表明，发现的学生集群揭示了学生中各种拖延行为的有意义的表征。
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.