Year: 2024
Type: conference-paper
Abstract: With the widespread adoption of digital technologies such as digital textbooks, it has become feasible to collect daily logs of students' learning activities. Accordingly, there has been a growing trend in research using these logs. One of these areas is focusing on predicting grade of each student based on these learning activity logs. However, previous research focused on detecting At-risk students when learning activity logs for all lectures are available. This is not applicable for detection at the first few lectures (i.e. weeks) required in practical usage scenarios. We call this scenario as "early detection" in this paper. However, in early detection, the accuracy of at-risk detection tends to decrease. To solve this problem, we propose a Learning-activity Forecasting Network (LFNet) that improves the accuracy of early detection by aligning the embedding of the first few lectures with that of all lectures. Through experiments on learning activity logs of actual lectures, we confirmed that the proposed method could achieve high At-risk detection accuracy even from the first few lectures of learning activity logs.
Institutions: Nagoya University, Aichi University of Technology
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
Award: G2358816916
OpenAlex ID: W4406621141