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

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

Zilong Pan, Chenglu Li, Wenting Zou, Min Liu
Copyright: © 2023 |Pages: 29
ISBN13: 9781668495278|ISBN10: 1668495279|ISBN13 Softcover: 9781668495315|EISBN13: 9781668495285
DOI: 10.4018/978-1-6684-9527-8.ch002
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MLA

Pan, Zilong, et al. "Applying Learning Analytics Approaches to Detect and Track Students' Cognitive States During Virtual Problem-Solving Activities." Perspectives on Learning Analytics for Maximizing Student Outcomes, edited by Gürhan Durak and Serkan Cankaya, IGI Global, 2023, pp. 15-43. https://doi.org/10.4018/978-1-6684-9527-8.ch002

APA

Pan, Z., Li, C., Zou, W., & Liu, M. (2023). Applying Learning Analytics Approaches to Detect and Track Students' Cognitive States During Virtual Problem-Solving Activities. In G. Durak & S. Cankaya (Eds.), Perspectives on Learning Analytics for Maximizing Student Outcomes (pp. 15-43). IGI Global. https://doi.org/10.4018/978-1-6684-9527-8.ch002

Chicago

Pan, Zilong, et al. "Applying Learning Analytics Approaches to Detect and Track Students' Cognitive States During Virtual Problem-Solving Activities." In Perspectives on Learning Analytics for Maximizing Student Outcomes, edited by Gürhan Durak and Serkan Cankaya, 15-43. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-9527-8.ch002

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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.

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