Abstract
e-Learning has become a trend in the world nowadays. However, most researches neglect a fundamental issue - the e-Learner 's prior knowledge on which the useful intelligent systems are based. This research employs the e-Learner's prior knowledge and mines his/her interior desire on appropriate target courses or materials as a part of a personalization process to construct the overall e-Learning strategy for education. This paper illustrates a novel web usage mining approach, based on the sequence mining technique applied to e-Learner's navigation behaviour, to discover patterns in the navigation of e-Learning websites. Three critical contributions are made in this paper: (1) using the footstep graph to visualize the e-Learner's click-stream data so any interesting pattern can be detected more easily and quickly; (2) illustrating a novel sequence mining approach to identify pre-designated e-Learner navigation patterns automatically and integrating a back-propagation network (BPN) model smoothly; and (3) applying the empirical research to indicate that the proposed approach can predict and categorize the e-Learners ' navigation behaviour with high accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 295-318 |
| Number of pages | 24 |
| Journal | Journal of Research and Practice in Information Technology |
| Volume | 41 |
| Issue number | 4 |
| Publication status | Published - 2009 Nov 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Management Information Systems
- Information Systems
- Hardware and Architecture
- Computer Networks and Communications
Cite this
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Accessing e-Learners' knowledge for personalization in e-Learning environment. / Chou, Pao Hua; Wu, Menq-Jiun; Li, Pi Hsiang; Chen, Kuang Ku.
In: Journal of Research and Practice in Information Technology, Vol. 41, No. 4, 01.11.2009, p. 295-318.Research output: Contribution to journal › Article
TY - JOUR
T1 - Accessing e-Learners' knowledge for personalization in e-Learning environment
AU - Chou, Pao Hua
AU - Wu, Menq-Jiun
AU - Li, Pi Hsiang
AU - Chen, Kuang Ku
PY - 2009/11/1
Y1 - 2009/11/1
N2 - e-Learning has become a trend in the world nowadays. However, most researches neglect a fundamental issue - the e-Learner 's prior knowledge on which the useful intelligent systems are based. This research employs the e-Learner's prior knowledge and mines his/her interior desire on appropriate target courses or materials as a part of a personalization process to construct the overall e-Learning strategy for education. This paper illustrates a novel web usage mining approach, based on the sequence mining technique applied to e-Learner's navigation behaviour, to discover patterns in the navigation of e-Learning websites. Three critical contributions are made in this paper: (1) using the footstep graph to visualize the e-Learner's click-stream data so any interesting pattern can be detected more easily and quickly; (2) illustrating a novel sequence mining approach to identify pre-designated e-Learner navigation patterns automatically and integrating a back-propagation network (BPN) model smoothly; and (3) applying the empirical research to indicate that the proposed approach can predict and categorize the e-Learners ' navigation behaviour with high accuracy.
AB - e-Learning has become a trend in the world nowadays. However, most researches neglect a fundamental issue - the e-Learner 's prior knowledge on which the useful intelligent systems are based. This research employs the e-Learner's prior knowledge and mines his/her interior desire on appropriate target courses or materials as a part of a personalization process to construct the overall e-Learning strategy for education. This paper illustrates a novel web usage mining approach, based on the sequence mining technique applied to e-Learner's navigation behaviour, to discover patterns in the navigation of e-Learning websites. Three critical contributions are made in this paper: (1) using the footstep graph to visualize the e-Learner's click-stream data so any interesting pattern can be detected more easily and quickly; (2) illustrating a novel sequence mining approach to identify pre-designated e-Learner navigation patterns automatically and integrating a back-propagation network (BPN) model smoothly; and (3) applying the empirical research to indicate that the proposed approach can predict and categorize the e-Learners ' navigation behaviour with high accuracy.
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UR - http://www.scopus.com/inward/citedby.url?scp=78649556275&partnerID=8YFLogxK
M3 - Article
VL - 41
SP - 295
EP - 318
JO - Australian Computer Journal
JF - Australian Computer Journal
SN - 1443-458X
IS - 4
ER -