TY - JOUR
T1 - Toward a fuzzy domain ontology extraction method for adaptive e-learning
AU - Lau, Raymond Y.K.
AU - Song, Dawei
AU - Li, Yuefeng
AU - Cheung, Terence C.H.
AU - Hao, Jin-Xing
PY - 2009/6
Y1 - 2009/6
N2 - 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.
AB - 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.
KW - Concept map
KW - Domain ontology
KW - E-Learning
KW - Fuzzy sets
KW - Knowledge management applications
KW - Linguistic processing
KW - Modeling structured
KW - Ontology extraction
KW - Text mining
KW - Textual and multimedia data
UR - http://www.scopus.com/inward/record.url?scp=66149180701&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-66149180701&origin=recordpage
U2 - 10.1109/TKDE.2008.137
DO - 10.1109/TKDE.2008.137
M3 - RGC 21 - Publication in refereed journal
SN - 1041-4347
VL - 21
SP - 800
EP - 813
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 6
M1 - 4564463
ER -
