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Título
Predicting Peer-Review Participation at Large Scale Using an Ensemble Learning Method
Autor
Congreso
LASI: Learning Analytics Summer Institute & Summer School (2017. Madrid)
Año del Documento
2017
Editorial
CEUR Workshop Proceedings
Descripción
Producción Científica
Documento Fuente
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.
Palabras Clave
MOOC
Revisión por pares
Patrocinador
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)
Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA082U16)
SNOLA (TIN2015-71669-REDT)
Version del Editor
Idioma
eng
Derechos
openAccess
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