A novel method of case representation and retrieval in CBR for e-learning

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• 作者：Aditya Khamparia ; Babita Pandey
• 关键词：Artificial neural network ; Data mining ; Decision tree ; Case based reasoning ; Learning performance ; Case representation
• 刊名：Education and Information Technologies
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：22
• 期：1
• 页码：337-354
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computers and Education; Educational Technology; User Interfaces and Human Computer Interaction; Education, general; Information Systems Applications (incl.Internet); Computer Appl. in Social and Beha
• 出版者：Springer US
• ISSN：1573-7608
• 卷排序：22

文摘

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the relationship between student characteristics and learning performance, DM to generate classification rules for learning outcomes which are further used to generate cases for the case base and CBR for reasoning. This adaptive system helps in facilitating the course content of different difficulty level to individuals according to their features. The result shows the above method provides the learning material to student as per their need and helps them to enhance their learning.