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Liu Ming-Tsung & Yu Pao-Ta (2011). « Aberrant Learning Achievement Detection Based on Person-fit Statistics in Personalized e-Learning Systems ». Educational Technology & Society, vol. 14, n° 1, p. 107–120. En ligne : <http://www.google.fr/ur ... ls%2F14_1%2F10.pdf&>. 
Added by: Marie Gaussel (21 Feb 2012 15:49:12 Europe/Paris)   Last edited by: Marie Gaussel (23 Feb 2012 10:52:09 Europe/Paris)
Resource type: Journal Article
BibTeX citation key: Liu2011a
Categories: General
Keywords: style d'apprentissage
Creators: Liu, Yu
Collection: Educational Technology & Society
Views: 1242/1452
Views index: 15%
Popularity index: 3.75%
URLs     http://www.google. ... s%2F14_1%2F10.pdf&
Abstract     
A personalized e-learning service provides learning content to fit learners’ individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy behind the LCI is that if any non-cognitive factor influences a learner, the effect will eventually be reflected in his/her learning achievement. Therefore, it’s our explicit attempt to build a prototype system aimed at assessing aspects of learning other than cognitive factors. This study proposes a personalized e- learning system based on Item Response Theory which considers both course difficulty and learner’s ability to provide adaptive learning paths. The LCI, which originates from the person-fit statistics in psychometric theory, statistically judges whether the observed learning achievement is significantly different from the achievement predicted by the Item Response Theory (IRT) models. If such an aberrant learning pattern is detected, a computer tutoring agent appears to notify and encourage that learner. Furthermore, human tutors may get involved periodically to offer further guidance to support learners with aberrant patterns. Experimental results show that such diagnostics could enhance the learning efficiency and smooth the learning experience
Added by: Marie Gaussel  Last edited by: Marie Gaussel
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