Applying Learning Analytics Approaches to Detect and Track Students' Cognitive States During Virtual Problem-Solving Activities

Abstract

A virtual problem-based learning (PBL) environment can generate large amounts of textual or time-series usage data, providing instructors with opportunities to track and facilitate students' problem-solving progress. However, instructors face the challenge of making sense of a large amount of data and translating it into interpretable information during PBL activities. This study proposes a learning analytics approach guided by flow theory to provide teachers with information about middle schoolers' real-time problem-solving cognitive states. The results indicate that the hidden Markov model (HMM) can identify students' specific cognitive states including flow, anxiety, and boredom state. Based on the findings, a teacher dashboard prototype was created. This study has demonstrated the promising potential of incorporating the HMM into learning analytics dashboards to translate a large amount of usage data into interpretable formats, thus, assisting teachers in tracking and facilitating PBL.