Naive Metalogues: Situated Stories in Data Science Education

Potential Abstract: This research article explores the intersection of data science education and storytelling through the lens of situated learning theory. Building on the notion that learning is inherently situated within real-world contexts, this study investigates how naive metalogues, or simple narratives that illustrate complex data science concepts, can enhance student understanding and engagement. By incorporating storytelling into data science pedagogy, educators can create more meaningful and memorable learning experiences for students. Through a qualitative analysis of student responses and interactions with naive metalogues in the classroom, this study aims to uncover the impact of situated stories on students’ learning outcomes, attitudes towards data science, and ability to apply data analytics skills in practical settings. The findings from this study have implications for curriculum design, instructional strategies, and the integration of data science across disciplines within the educational landscape.

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