Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, f...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2009-06, Vol.21 (6), p.800-813
Main Authors: Lau, R.Y.K., Dawei Song, Yuefeng Li, Cheung, T.C.H., Jin-Xing Hao
Format: Article
Language:English
Subjects:
Computer programs
concept map
Concept mapping
Data mining
Discussion forums
Distance education
Distance learning
Domain ontology
e-Learning
Education
Educational technology
Electronic learning
Extraction
Fuzzy
fuzzy sets
Humans
Internet
Messages
On-line systems
Online
Online instruction
Ontologies
Ontology
ontology extraction
Studies
Taxonomy
Text mining
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Summary:With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2008.137