A conversational intelligent tutoring system to automatically predict learning styles

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• 作者：Annabel Latham^a ; ^{a.latham@mmu.ac.uk} ; Keeley Crockett^a ; ^{k.crockett@mmu.ac.uk} ; David McLean^a ; ^{d.mclean@mmu.ac.uk} ; Bruce Edmonds^b ; ^{b.edmonds@mmu.ac.uk}
• 关键词：Architectures for educational technology system ; Human&ndash ; computer interface ; Intelligent tutoring systems ; Interactive learning environments ; Teaching/learning strategies
• 刊名：Computers and Education
• 出版年：2012
• 期刊代码：34_03601315
• 类别：cp
• 出版时间：August, 2012
• 卷：59
• 期：1
• 页码：95-109
• 文件大小：587 K

摘要

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student鈥檚 learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model () to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar鈥檚 tutoring helpful and achieved an average learning gain of 13%.