基于刺激敏感Hawkes过程的个性化学生拖延建模
Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling
摘要：
学生的拖延和死记硬背是在线学习环境中的主要挑战，对教育和健康产生负面影响。在连续时间内对学生活动进行建模并预测他们的下一次学习时间是重要的问题，可以帮助创建个性化的及时干预措施来缓解这些挑战。然而，以前的尝试动态建模的学生拖延遭受的主要问题：他们无法预测下一个活动时间，不能处理缺失的活动历史，不是个性化的，并忽视重要的课程属性，如作业的最后期限，这是必不可少的解释填鸭式的行为。为了解决这些问题，我们引入了一个新的个性化的刺激敏感霍克斯过程模型（SSHP），通过联合建模所有的学生分配对，并利用它们的相似性，预测学生的下一个活动时间，即使没有历史观察。不像经常点的过程，假设一个恒定的外部触发效果的环境，我们模型的三种动态类型的外部刺激，根据分配的可用性，分配的最后期限，和每个学生的时间管理习惯。我们在两个合成数据集和两个真实世界数据集上的实验表明，与最先进的模型相比，未来活动预测的性能优越。此外，我们表明，我们的模型实现了灵活和准确的参数化的活动强度的学生。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
