Unsupervised reconstruction mechanism to recover the hypermedia structure of instructional materials on the web based on the association lattice of keywords

Authors:
Chang-Kai Hsu;Jyh-Cheng Chang;Maiga Chang;Jia-Sheng Heh
Affiliations:
Dept. of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;Dept. of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;Program Office of National Science and Technology Program for e-Learning in Taiwan;Dept. of Information and Computer Engineering, Chung Yuan Christian University, Taiwan
Venue:
DIWEB'06 Proceedings of the 5th WSEAS International Conference on Distance Learning and Web Engineering
Year:
2005

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Abstract

When a learner is reading an instructional material on the web, sometimes he/she may not understand the meaning of a specific keyword clearly. Therefore, the learner will need more references for that keyword at that moment. However, unfortunately, in the most of time, the learner will not be able to find out. It is because of the instruction designers of learning materials who had never thought that will be a question mark in the learners' mind. Therefore, if the appropriate dependent documents that are associated with the keyword that the learner is looking for could be retrieved automatically and the original document structure could be reconstructed to more suitable for learning and reading, that will be perfect. In this paper, the data mining technique - association rule methodology (ARM) is applying to analyze and using to reconstruct the necessary instructional materials on the web automatically.