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E-Learning personalization based on Dynamic learners' preference

ISSN:0975-3826
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Essaid El Bachari, El Hassan Abelwahed and Mohammed El Adnani, International Journal of Computer Science & Information Technology
Dibuat : 2012-06-26, dengan 1 file

Keyword : Adaptive Learning, MBTI, Learning Style, Teaching Strategy, Personalization.
Subjek : E-Learning personalization based on Dynamic learners' preference
Url : http://airccse.org/journal/jcsit/0611csit14.pdf
Sumber pengambilan dokumen : Internet

Personalized e-learning implementation is recognized one of the most interesting research areas in the


distance web-based education. Since the learning style of each learner is different we must to fit e-


learning to the different needs of learners. This paper discusses teaching strategies matching with


learner’s personality using the Myers-Briggs Type Indicator (MBTI) tools. Based on an innovative


approach, a framework for building an adaptive learning management system by considering learner’s


preference has been developed. The learner’s profile is initialized according to the results obtained by the


student in the index of learning styles questionnaire and then fine-tuned during the course of the


interaction using the Bayesian model. Moreover, an experiment was conducted to evaluate the


performance of our approach. The result reveals the system effectiveness for which it appears that the


proposed approach may be promising.

Deskripsi Alternatif :

Personalized e-learning implementation is recognized one of the most interesting research areas in the


distance web-based education. Since the learning style of each learner is different we must to fit e-


learning to the different needs of learners. This paper discusses teaching strategies matching with


learner’s personality using the Myers-Briggs Type Indicator (MBTI) tools. Based on an innovative


approach, a framework for building an adaptive learning management system by considering learner’s


preference has been developed. The learner’s profile is initialized according to the results obtained by the


student in the index of learning styles questionnaire and then fine-tuned during the course of the


interaction using the Bayesian model. Moreover, an experiment was conducted to evaluate the


performance of our approach. The result reveals the system effectiveness for which it appears that the


proposed approach may be promising.

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