Abstract

This study aims to examine the impact of instruction using multiple representations on student learning outcomes and to investigate patterns related to learning styles. This research is motivated by the limited number of studies that combine various types of mathematical representation with the use of technology, especially the use of Python as an exploratory and contextual learning tool. A quantitative method with a one-group pretest-posttest design was applied in this study, which involved 72 students. Data were collected through pretest and posttest scores, learning style categorization, and information on dominant representations, which were subsequently analyzed using R-Studio. The results indicate a significant improvement in student academic performance following the implementation of instruction based on diverse representations. The mean score increased from 64.79 in the pre-test to 80.37 in the post-test (mean difference = 15.58), with a paired t-test showing significance (p < 0.001). Additionally, an ANOVA analysis revealed no significant differences in learning outcomes based on learning styles. Network analysis suggests that students utilize various types of representations in a flexible manner, without being confined to a specific learning style. Visual representations appeared to be more dominant, but all learning styles were interrelated with various forms of representation. Overall, the results of this study indicate that multiple-representation-based instruction is effective in improving learning outcomes and supporting a more flexible learning process. This approach creates a more adaptive learning environment compared to methods that rely solely on learning style preferences.

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