RT info:eu-repo/semantics/conferenceObject
T1 Predicting Peer-Review Participation at Large Scale Using an Ensemble Learning Method
A1 Er, Erkan
A1 Gómez Sánchez, Eduardo
A1 Bote Lorenzo, Miguel Luis
A1 Dimitriadis Damoulis, Ioannis
A1 Asensio Pérez, Juan Ignacio
K1 MOOC
K1 Revisión por pares
AB 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.
PB CEUR Workshop Proceedings
YR 2017
FD 2017
LK http://uvadoc.uva.es/handle/10324/24845
UL http://uvadoc.uva.es/handle/10324/24845
LA eng
NO 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.
NO Producción Científica
DS UVaDOC
RD 24-abr-2024