Cultural Relevance for Epistemic Practice in High School Computational Data Mining

This research-to-practice full paper examines the integration of culturally responsive computing (CRC) within high school data science education. It presents a theoretical and practical framework that leverages cultural backgrounds to enhance the teaching and learning processes in computational data mining. The main objective of this research is to demonstrate how integrating CRC can improve educational experiences by making them more inclusive and engaging for students from diverse cultural backgrounds. By applying CRC, educators can tailor learning experiences that transmit technical skills and conceptual knowledge and resonate more deeply with students' personal and social identities. This approach has the potential to foster deeper engagement and understanding of the material, thereby enhancing learning outcomes. Our research operates within the broader context of ongoing efforts to integrate data science across K-12 education, advocating for educational practices that acknowledge and utilize students' cultural identities as assets rather than deficits. The theoretical foundations for these practices are rooted in existing literature that emphasizes the importance of culturally relevant pedagogies in computing and data science education. Implementing these frameworks in classroom settings represents a vital intersection of educational research and practice, aiming to address disparities in engagement and achievement among minority students. This work leverages qualitative analysis of how high school students utilize their cultural and social orientations within a structured “coding like a data miner” workshop. The dataset includes students' final project presentations, which showcase their ability to integrate cultural relevance into their epistemic data science practices. We highlight how these integrations influence learners' understanding of and engagement with data science concepts. The results are anticipated to contribute to the discourse on how data science education can be made more accessible and effective through culturally responsive methodologies. This contribution is valuable for preparing a diverse student population to thrive in a data-driven future, ensuring they are equipped with technical skills and can apply them within their cultural contexts.