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Title: Predicting Peer-Review Participation at Large Scale Using an Ensemble Learning Method
Authors: Er, Erkan
Gómez Sánchez, Eduardo
Bote Lorenzo, Miguel L.
Dimitriadis, Yannis A.
Asensio Pérez, Juan Ignacio
Conference: LASI: Learning Analytics Summer Institute & Summer School (2017. Madrid)
Issue Date: 2017
Publisher: CEUR Workshop Proceedings
Description: Producción Científica
Citation: Er, E., Gómez-Sánchez, E., Bote-Lorenzo, M.L., Dimitriadis, Y., Asensio-Pérez, J.I. Predicting Peer-Review Participation at Large Scale Using an Ensemble Learning Method. Proceedings of the Learning Analytics Summer Institute Spain 2017, Madrid, Spain, July 2017.
Abstract: Peer review has been an effective approach for the assessment of mas-sive numbers of student artefacts in MOOCs. However, low student participation is a barrier that can result in inefficiencies in the implementation of peer reviews, disrupting student learning. In this regard, knowing earlier the estimate number of peer works that students will review may bring numerous pedagogical utilities in MOOCs. Previously, we have attempted to predict student participation in peer review in a MOOC context. Building on our previous work, in this study we pro-pose an ensemble learning approach with a refined set of features. Results show that the prediction performance improves when a preceding classification model is trained to identify students with no peer-review participation and that the re-fined features were effective with more transferability to other contexts.
Classification: MOOC
Revisión por pares
Sponsor: Ministerio de Economía, Industria y Competitividad (Project TIN2014-53199-C3-2-R)
Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA082U16)
SNOLA (TIN2015-71669-REDT)
Publisher Version:
Language: eng
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:DEP71 - Comunicaciones a congresos, conferencias, etc.

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