English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/130755
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Personalised automated assessments

AutorGutierrez, Patricia; Osman, Nardine ; Sierra, Carles
Palabras claveLarge amounts
Massive open online course
Peer assessment
Peer-review process
Program committee
User need
Uncertainty analysis
Fecha de publicaciónjul-2015
Citación1st International Workshop on AI and Feedback, AInF 2015; Buenos Aires; Argentina; 25 July 2015 through 27 July 2015. CEUR Workshop Proceedings, Volume 1407, 2015, Pages 40-46
ResumenConsider an evaluator, or an assessor, who needs to assess a large amount of information. For instance, think of a tutor in a massive open online course with thousands of enrolled students, a senior program committee member in a large peer review process who needs to decide what are the final marks of reviewed papers, or a user in an e-commerce scenario where the user needs to build up its opinion about products evaluated by others. When assessing a large number of objects, sometimes it is simply unfeasible to evaluate them all and often one may need to rely on the opinions of others. In this paper we provide a model that uses peer assessments to generate expected assessments and tune them for a particular assessor. Furthermore, we are able to provide a measure of the uncertainty of our computed assessments and a ranking of the objects that should be assessed next in order to decrease the overall uncertainty of the calculated assessments.
Aparece en las colecciones: (IIIA) Comunicaciones congresos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
CEUR-WS_AInF2015,1407pp.40-46.pdfArtículo principal735,35 kBAdobe PDFVista previa
Mostrar el registro completo

NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.