E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems
arXiv:2407.13053v2 Announce Type: replace-cross Abstract: 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,
The proliferation of digital learning platforms and e-books creates vast datasets of student interaction, enabling more sophisticated AI models to be developed for educational analysis.
Improving the granularity and predictive power of student behavior analytics in e-book systems can significantly enhance personalized learning, intervention strategies, and overall educational outcomes.
The ability to integrate temporal information into feature embeddings for student actions moves beyond static statistical analyses, offering a more dynamic and accurate understanding of learning processes.
- · EdTech platforms
- · Educational researchers
- · Students
- · AI/ML developers
- · Traditional statistical educational models
More accurate predictions of student performance and engagement will become possible.
This improved understanding could lead to the development of highly adaptive and personalized e-learning environments.
Long-term, this could reshape pedagogical approaches, making education more data-driven and individualized across various subjects and age groups.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL