The Development And Evaluation Of Personalized Learning Material Based On A Profiling Algorithm For Polytechnic Students In Learning Algebra
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Date
2016-07
Authors
Mohamed Mokmin, Nur Azlina
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Abstract
Mathematics is the foundation for engineering studies, especially for Malaysian polytechnics engineering students. Algebra is an important topic in mathematics, especially in engineering programs. Previous research shows that personalization techniques can increase student understanding. Thus, the aim of this study was to design and develop an application that utilized Intelligent Tutoring System (ITS) technology for the personalization of mathematics learning. This technology has the ability to help with the personalization of student learning by recommending the most suitable learning materials. The recommendation is computed using a Case-based Reasoning (CBR) algorithm by finding the similarity between the new submitted profile and the stored profiles in the database. The solution given by the most similar cases is used as a reference. Prior learning and mathematics learning style are the two parameters of a student's profile. The ITS formed two versions of treatments: Personalized Learning Material (PLM) and Non-personalized Learning Material (NPLM). The PLM presented a learning material by referring to a solution from the most similar case to the newly submitted case and the Non-personalized Learning Material (NPLM) referred to a solution from the least similar case. The four learning materials developed for this study were Mastery Learning Material (MLM), Understanding Learning Material (ULM), Self-Expressive Learning Material (SLM) and Interpersonal Learning Material (ILM). The accuracy of the recommendation was measured using the CBR Similarity Score
(CSS) and the learning performance was measured using the Learning Gain Score (LGS). The data from 309 first semester engineering students was analyzed using the Mann-Whitney U test and ANOVA. The results show that the recommendations were generated based on the calculations by the CBR algorithm and the PLM groups have greater LGS than the NPLM groups. The ILM group obtained higher LGS than those working with other groups of learning materials. Guided by the cognitive theory of multimedia learning and instructional design model, the CBR algorithm was successfully integrated with the ITS components to produce an effective personalized application. This study has thus successfully developed a learning application that effectively increases student performance in algebra.
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Developed a learning application that effectively , increases student performance in algebra.