E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems
This work addresses the need for better feature representation in educational data mining, specifically for analyzing student behaviors in e-book systems, though it is incremental as it adapts existing embedding techniques to a new domain.
The study tackled the problem of representing student interactions in e-book systems by proposing E2Vec, a feature embedding method that incorporates temporal information, and demonstrated its effectiveness in an at-risk detection task with a dataset of 305 students.
Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data. In the past, researchers extracted meaningful features from EventStream, and utilized them as inputs for downstream tasks such as grade prediction and modeling of student behavior. Previous research evaluated models that mainly used statistical-based features derived from EventStream logs, such as the number of operation types or access frequencies. While these features are useful for providing certain insights, they lack temporal information that captures fine-grained differences in learning behaviors among different students. This study proposes E2Vec, a novel feature representation method based on word embeddings. The proposed method regards operation logs and their time intervals for each student as a string sequence of characters and generates a student vector of learning activity features that incorporates time information. We applied fastText to generate an embedding vector for each of 305 students in a dataset from two years of computer science courses. Then, we investigated the effectiveness of E2Vec in an at-risk detection task, demonstrating potential for generalizability and performance.