DocumentCode :
2125251
Title :
The Adaptive Learning System Based on Learning Style and Cognitive State
Author :
Chen, Shipin ; Zhang, Jianping
Author_Institution :
Coll. of Educ. Sci. & Technol., China West Normal Univ., Nanchong
fYear :
2008
fDate :
21-22 Dec. 2008
Firstpage :
302
Lastpage :
306
Abstract :
Adaptive learning system, in essence, is a kind of online learning environment that supports the individual learning. It changes the traditional "just put it on the web" approach, and provides the customized learning according to the individual differences. In order to reducing the "cognition overload" and "disoriented", an architecture of adaptive learning system based on learning style and cognitive state (ALS-LSCS) is put forward in this paper. Referencing AHAM model, the architecture of ALS-LSCS is mainly composed of the media space, domain model, instruction model, learner model, adaptive model and the user interface. To record the cognitive state and learning style of learners, learner model combines the stereotype with the multi-layered overlay model. According to the Felder-Silverman categories, learning style is represented in stereotype. Cognitive state is recorded in Multi-layered overlay model. ALS-LSCS selects the learning content based on learnerpsilas cognitive state, and presents learning content through selecting teaching media based on learnerpsilas learning style.
Keywords :
computer aided instruction; inference mechanisms; software architecture; Felder-Silverman categories; adaptive learning system; adaptive model; cognition overload; domain model; instruction model; learner model; learning style and cognitive state; media space; multilayered overlay model; online learning environment; user interface; Adaptive systems; Artificial intelligence; Cognition; Education; Educational institutions; Educational technology; Electronic learning; Intelligent systems; Knowledge acquisition; Learning systems; Adaptive Learning System; Cognitive State; Learning Style;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3488-6
Type :
conf
DOI :
10.1109/KAM.2008.60
Filename :
4732834
Link To Document :
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