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

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.