An Ontology Based Framework for Intelligent Web Based e-Learning

B. Senthilnayaki, K. Venkatalakshmi and A. Kannan
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B. Senthilnayaki: Department of Information Science and Technology, Anna University, Chennai, India
K. Venkatalakshmi: Department of Electronic and Communication Engineering, Anna University, Chennai, India
A. Kannan: Department of Information Science and Technology, Anna University, Chennai, India

International Journal of Intelligent Information Technologies (IJIIT), 2015, vol. 11, issue 2, 23-39

Abstract: E-Learning is a fast, just-in-time, and non-linear learning process, which is now widely applied in distributed and dynamic environments such as the World Wide Web. Ontology plays an important role in capturing and disseminating the real world knowledge for effective human computer interactions. However, engineering of domain ontologies is very labor intensive and time consuming. Some machine learning methods have been explored for automatic or semi-automatic discovery of domain ontologies. Nevertheless, both the accuracy and the computational efficiency of these methods need to be improved. While constructing large scale ontology for real-world applications such as e-learning, the ability to monitor the progress of students' learning performance is a critical issue. In this paper, a system is proposed for analyzing students' knowledge level obtained using Kolb's classification based on the students level of understanding and their learning style using cluster analysis. This system uses fuzzy logic and clustering algorithms to arrange their documents according to the level of their performance. Moreover, a new domain ontology discovery method is proposed uses contextual information of the knowledge sources from the e-Learning domain. This proposed system constructs ontology to provide an effective assistance in e-Learning. The proposed ontology discovery method has been empirically tested in an e-Learning environment for teaching the subject Database Management Systems. The salient contributions of this paper are the use of Jaccard Similarity measure and K-Means clustering algorithm for clustering of learners and the use of ontology for concept understanding and learning style identification. This helps in adaptive e-learning by providing suitable suggestions for decision making and it uses decision rules for providing intelligent e-Learning.

Date: 2015
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