E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems
Researchers have developed E2Vec, a new feature representation method for analyzing student actions in digital textbook systems. This method utilizes word embedding techniques, specifically fastText, to create a student vector that incorporates temporal information from operation logs and time intervals. The approach was tested on a dataset of 305 students from computer science courses, demonstrating its potential for at-risk detection and generalizability. AI