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ERIC Number: EJ1007863
Record Type: Journal
Publication Date: 2013-Apr
Pages: 14
Abstractor: As Provided
Reference Count: 0
ISBN: N/A
ISSN: ISSN-0360-1315
Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning
Firdausiah Mansur, Andi Besse; Yusof, Norazah
Computers & Education, v63 p73-86 Apr 2013
Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need some centroid for the cluster initialization. Furthermore, the other researcher tried to apply ontology for the cluster on social network domain. This paper tries to reveal the behavior of students from all activities in Moodle e-learning system by putting ontology on domain social learning network (Moodle) which is not explored in the prior study. The activities such as forum, quiz, assignment, etc. are placed as clustering parameter according to the ontology model. The ontology of Moodle e-learning system is created to capture the activities of the student inside Moodle e-learning. Five meaningful attributes are used as group cluster for classifying the students' behaviour. According to the result, most of the students belong to the intentional group while some of the students belong to the constructive and active group. The constructed cluster is calculated based on the e-learning hits during the learning process inside Moodle e-learning. The result on the classification of students' behaviour using ontology cluster is comparable to their final achievement grade. It is believed that this study can bring immense benefit to the development of e-learning system in the future. (Contains 8 figures and 7 tables.)
Elsevier. 3251 Riverport Lane, Maryland Heights, MO 63043. Tel: 800-325-4177; Tel: 314-447-8000; Fax: 314-447-8033; e-mail: JournalCustomerService-usa@elsevier.com; Web site: http://www.elsevier.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A