Theory-based learning analytics to explore student engagement patterns in a peer review activity

Date

2021-04-12

Author

Villa-Torrano, Cristina
Dimitriadis, Yannis
Gasevic, Dragan
Bote-Lorenzo, Miguel L.
Asensio-Pérez, Juan I.
Gómez-Sánchez, Eduardo
Martínez Monés, Alejandra

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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© 2021 ACM.Peer reviews offer many learning benefits. Understanding students' engagement in them can help design effective practices. Although learning analytics can be effective in generating such insights, its application in peer reviews is scarce. Theory can provide the necessary foundations to inform the design of learning analytics research and the interpretation of its results. In this paper, we followed a theory-based learning analytics approach to identifying students' engagement patterns in a peer review activity facilitated via a web-based tool called Synergy. Process mining was applied on temporal learning data, traced by Synergy. The theory about peer review helped determine relevant data points and guided the top-down approach employed for their analysis: moving from the global phases to regulation of learning, and then to micro-level actions. The results suggest that theory and learning analytics should mutually relate with each other. Mainly, theory played a critical role in identifying a priori engagement patterns, which provided an informed perspective when interpreting the results. In return, the results of the learning analytics offered critical insights about student behavior that was not expected by the theory (i.e., low levels of co-regulation). The findings provided important implications for refining the grounding theory and its operationalization in Synergy.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103879673&origin=inward
https://hdl.handle.net/11511/92824

DOI

https://doi.org/10.1145/3448139.3448158

Conference Name

11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021

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Department of Computer Education and Instructional Technology, Conference / Seminar

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Citation Formats

C. Villa-Torrano et al., “Theory-based learning analytics to explore student engagement patterns in a peer review activity,” presented at the 11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021, Virtual, Online, Amerika Birleşik Devletleri, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103879673&origin=inward.