General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
    • E-mail:
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    • SCImago Journal & Country Rank
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2012 Vol.5(2): 371-376 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2013.V5.712

An Automatic Approach for Identifying Triple-Factor in e-Learning Process

Sfenrianto, Zainal A. Hasibuan, and Heru Suhartanto

Abstract—Developing the personalization in an e-Learning environment is a way to provide learning material which matches the students’ characteristics. In order to develop an e-Learning which supports personalization should be designed to facilitate an factors influence the success of students’ learning, such as learning style, motivation, knowledge ability, etc. Considering the existence of those factors for the personalization in e-Learning system can affect students’ performance and makes learning easier for them. Therefore, in this study, we propose a parameter (learning behavior patterns) based on learning activities (learning behavior) students for identifying learning styles, motivation, and knowledge ability (triple-factor) in e-Learning system. Our approach is to identify the triple-factor by simply observing his/her learning behavior recorded in data log, without asking the student to answer any questions of questionnaire. Each data log gives an indication related to identification the triple-factor in e-Learning system. The identification of those factor aims at inferring the learning styles, motivation and knowledge ability states. Then, it uses as the basis for dynamic personalization functionality.

Index Terms—E-Learning, learning behavior, identification, personalization, learning style, motivation, knowledge ability.

The authors are with Research Laboratory of Digital Library and Distance Learning (DL2), Faculty of Computer Science, University of Indonesia (tel.: +62217863415; fax: +62217863415; e-mail:


Cite: Sfenrianto, Zainal A. Hasibuan, and Heru Suhartanto, "An Automatic Approach for Identifying Triple-Factor in e-Learning Process," International Journal of Computer Theory and Engineering vol. 5, no. 2, pp. 371-376, 2013.

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