Toward a fuzzy domain ontology extraction method for adaptive e-learning
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 4564463 |
Pages (from-to) | 800-813 |
Journal / Publication | IEEE Transactions on Knowledge and Data Engineering |
Volume | 21 |
Issue number | 6 |
Publication status | Published - Jun 2009 |
Link(s)
Abstract
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. © 2006 IEEE.
Research Area(s)
- Concept map, Domain ontology, E-Learning, Fuzzy sets, Knowledge management applications, Linguistic processing, Modeling structured, Ontology extraction, Text mining, Textual and multimedia data
Citation Format(s)
Toward a fuzzy domain ontology extraction method for adaptive e-learning. / Lau, Raymond Y.K.; Song, Dawei; Li, Yuefeng et al.
In: IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 6, 4564463, 06.2009, p. 800-813.
In: IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 6, 4564463, 06.2009, p. 800-813.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review