Personalization of Learning Materials for Mathematics Learning Using a Case-Based Reasoning Algorithm

Authors

  • Mona Masood 1Centre for instructional Technology & Multimedia Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
  • Nur Azlina Mohamed Mokmin 2Politeknik Balik Pulau, 11000 Balik Pulau, Penang, Malaysia.

Keywords:

Algebra, Artificial Intelligent, Case-based Reasoning, Multimedia,

Abstract

Personalization is important to ensure that learning can cater to the needs of individual learners. The Intelligent Tutoring System (ITS) is a technology that can ease the personalization process; one of the most widely used algorithms in ITS is case-based reasoning (CBR). This study measures the ability of the CBR algorithm to give suggestions for the most suitable learning material based on specific information supplied by the user of the system. In order to test the ability of the application to recommend learning material, two versions of the application were created. The first version displayed the most suitable learning material, and the second version displayed the least preferable learning material. The results show that the first version of the application successfully assigns students to the most suitable learning material when compared with the second version.

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Published

2017-09-15

How to Cite

Masood, M., & Mohamed Mokmin, N. A. (2017). Personalization of Learning Materials for Mathematics Learning Using a Case-Based Reasoning Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-11), 67–70. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2740

Issue

Vol. 9 No. 2-11: The Challenge of Computer and Engineering in Building Sustainable Service Systems I

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Articles

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