Year: 2025
Type: article
Abstract: The difficulty faced by students in understanding statistical concepts taught in the Basic Statistics course in the Mathematics Education Program at Universitas Timor is primarily due to the limitations of conventional teaching methods. Additionally, challenges in data analysis for completing final projects arise from the lack of adequate analytical tools. This study aims to develop and evaluate an interactive learning medium based on RStudio to improve students' learning outcomes in the course and to develop an analytical tool utilizing MARS (Multivariate Adaptive Regression Splines). The objectives of this research are to create more interactive and effective learning media and analytical tools. The method employed in this study involves developing RStudio-based media equipped with interactive features and conducting effectiveness tests through pretest and posttest evaluations to measure improvements in learning outcomes related to the development of the learning media. For the MARS feature, the success of the application is evaluated based on the analysis output after data input. Furthermore, alpha and beta testing are conducted to identify technical issues and difficulties in using the learning media and MARS features, as well as to gather feedback on the interface and learning content. The results of the study indicate that the interactive learning media significantly improved students' learning outcomes, with posttest scores significantly higher than pretest scores. Alpha and beta testing revealed that the media is user-friendly and effective in supporting students' understanding and analytical skills. Therefore, this RStudio-based application has proven to be effective in enhancing the quality of learning and can serve as an alternative for addressing final project requirements that involve technology-based statistical approaches.
Source: Journal of Dynamics and Games
DOI: 10.3934/jdg.2025036
Institutions: University of Timor, Poznań University of Technology
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4414196640