Mostrar el registro sencillo del ítem

Peer assessment in MOOCs using preference learning via matrix factorization

dc.contributor.authorDíez Peláez, Jorge 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorAlonso-Betanzos, Amparo
dc.contributor.authorTroncoso, Alicia
dc.contributor.authorBahamonde Rionda, Antonio 
dc.date.accessioned2015-06-16T10:13:31Z
dc.date.available2015-06-16T10:13:31Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10651/31249
dc.description.abstractEvaluating in Massive Open Online Courses (MOOCs) is a difficult task because of the huge number of students involved in the courses. Peer grading is an effective method to cope with this problem, but something must be done to lessen the effect of the subjective evaluation. In this paper we present a matrix factorization approach able to learn from the order of the subset of exams evaluated by each grader. We tested this method on a data set provided by a real peer review process. By using a tailored graphical representation, the induced model could also allow the detection of peculiarities in the peer review processspa
dc.language.isoengspa
dc.relation.ispartofNIPS Workshop on Data Driven Educationspa
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titlePeer assessment in MOOCs using preference learning via matrix factorizationspa
dc.typeconference outputspa
dc.rights.accessRightsopen accessspa


Ficheros en el ítem

untranslated

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

CC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
Este ítem está sujeto a una licencia Creative Commons