Identifying Learner Profiles and Predicting Performance in a Digital Textbook: A Case Study of K-8 Schools in South Korea

• In Event: TIL- Learning Analytics for Performance Improvement

Short Description

This study explores K-8 students' learning behaviors on the Digital Textbook platform. We found 4 learner clusters, and they demonstrated different levels of the 21st learning competencies. The divergent exploration group showed significant improvements, while the peripheral acceptance group experienced a decrease in overall competencies. Random forest showed the community features of DT as a significant predictor of performance. We suggest personalized scaffoldings for DT to support diverse learner profiles and enhance student performance.

Authors

• Contributor: Ji Hyun Yu, University of North Texas

• Contributor: Yeonjeong Park, Ewha Womans University