Supporting statistical literacy skills for prospective teachers: A learning trajectory used South Sumatra local wisdom context through hybrid learning
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Abstract
In an era where data is increasingly prevalent, statistical literacy skills are essential for active citizenship and informed decision-making. For future generations, prospective teachers play a role in developing this skill. However, current instructional approaches often overlook the integration of practical applications and local wisdom, limiting students' ability to connect abstract statistical concepts with real-world experiences. The objective of this research is to design a Learning Trajectory (LT) that supports the statistical literacy skills of prospective teachers by utilizing a hybrid learning strategy that integrates local knowledge from South Sumatra as context. A total of 60 prospective teachers from a mathematics education study program participated in this study. A design research method was employed, specifically utilizing a validation study. The research unfolded in three stages: preparation for the experiment, the experimental design, and the retrospective analysis. Data collection techniques included student activity sheet assessments, classroom observations, and interviews. Data analysis involved comparing the Hypothetical Learning Trajectory (HLT) with the Actual Learning Trajectory (ALT) in the retrospective analysis stage to develop the LT. The results indicate that the designed LT guided students through five activities that support statistical literacy: reading and interpreting data tables using statistical situations with local wisdom from South Sumatra as context, interpreting graphs, analyzing and reflecting, exploring outliers, and making conclusions and presenting findings. These findings highlight the importance of integrating local wisdom contexts into statistical education, as well as the relevance and applicability of mathematical concepts for prospective teachers. This research contributes to the design of a learning trajectory based on a local wisdom context that can be applied in statistical literacy learning.
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