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

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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.

Original languageEnglish
Title of host publicationLAK21 Conference Proceedings - The Impact we Make: The contributions of learning analytics to learning
Subtitle of host publicationThe Eleventh International Conference on Learning Analytics & Knowledge
EditorsMaren Scheffel, Nia Dowell, Srecko Joksimovic, George Siemens
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)9781450389358
Publication statusPublished - 2021
EventInternational Learning Analytics & Knowledge Conference 2021 - Online, Irvine, United States of America
Duration: 12 Apr 202116 Apr 2021
Conference number: 11th (Proceedings)


ConferenceInternational Learning Analytics & Knowledge Conference 2021
Abbreviated titleLAK 2021
CountryUnited States of America
Internet address


  • Learning analytics
  • Peer reviews
  • Process mining
  • Student engagement

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