RT info:eu-repo/semantics/conferenceObject
T1 Theory-based learning analytics to explore student engagement patterns in a peer review activity
T2 LAK21: 11th International Conference on Learning Analytics & Knowledge
A1 Er, Erkan
A1 Villa Torrano, Cristina
A1 Dimitriadis Damoulis, Ioannis
A1 Gašević, Dragan
A1 Bote Lorenzo, Miguel Luis
A1 Asensio Pérez, Juan Ignacio
A1 Gómez Sánchez, Eduardo
A1 Martínez Monés, Alejandra
K1 Peer reviews
K1 Revisiones por pares
K1 Learning analytics
K1 Analítica de aprendizaje
K1 Student engagement
K1 Estudiante - Participación
K1 Process mining
K1 Minería de procesos
AB 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.
PB Society for Learning Analytics Research (SoLAR)
SN 978-1-4503-8935-8
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/49207
UL https://uvadoc.uva.es/handle/10324/49207
LA eng
NO Scheffel, M.; Dowell, N.; Joksimovic, S.; Siemens, G. Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK21). Online, 2021, p. 196–206
NO Producción Científica
DS UVaDOC
RD 27-nov-2024