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Título: | Personalised automated assessments |
Autor: | Gutierrez, Patricia; Osman, Nardine CSIC ORCID ; Sierra, Carles CSIC ORCID | Palabras clave: | Large amounts Massive open online courses Peer assessment Peer-review process Program committee User need Uncertainty analysis |
Fecha de publicación: | jul-2015 | Citación: | 1st 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 | Resumen: | Consider 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. | URI: | http://ceur-ws.org/Vol-1407/ http://hdl.handle.net/10261/130755 |
ISSN: | 16130073 |
Aparece en las colecciones: | (IIIA) Comunicaciones congresos |
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Fichero | Descripción | Tamaño | Formato | |
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CEUR-WS_AInF2015,1407pp.40-46.pdf | Artículo principal | 735,35 kB | Adobe PDF | Visualizar/Abrir |
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