EduHawkes: A Neural Hawkes Process Approach for Online Study Behavior Modeling

Lu Jiang, Pengyang Wang, Ke Cheng, Kunpeng Liu, Minghao Yin, Bo Jin, Yanjie Fu

Abstract: The COVID-19 pandemic forces schools to move teaching online and stimulates the development of online tutoring platforms. Although online tutoring platforms provide students the access to learning materials and tools anytime and anywhere, the quality of studies is impeded by the fact that students learn by watching videos, which lacks interactions between teachers and students. Such dilemma prevents us from respectively understanding and improving the online learning patterns and efficiency of students. To achieve this goal, we need to solve three challenges: (1) How can we quantify the study quality of online learning? (2) How can we design an appropriate data structure to describe online study behaviors? (3) How can we model the online study behaviors to better mine online study patterns? To address the challenges, we first propose a new measurement to quantify the online study quality from the perspective of study engagement. We then define a study behavior sequence to describe online study behaviors. The study behavior at each timestamp is an event of a video lecture watching behavior type, such as, watching, dragging forward and dragging backward. Moreover, we develop a neural hawkes process framework (namely EduHawkes) for online study behavior modeling. The EduHawkes is a novel hierarchical encode-decode architecture with simultaneously optimizing the study behavior prediction task (event-level) and the study quality prediction task (course-level). In the experiments, we apply EduHawkes to the applications of study quality prediction and flippant student identification in order to demonstrate the improved performances of our proposed method on modeling online study behaviors.