Exploring the Evolution of High School Students’ Questions in an Interest-Driven Data Science Curriculum

• In Event: Motivation and Creativity in Technology-Based Learning Environments

Abstract

This study investigates the data science inquiry process of high school students from populations historically excluded in computing-related fields. We analyzed 213 student-generated questions from the final project of a newly implemented interest-driven data science curriculum. We used a qualitative analytic approach to identify dominant themes of interest and assess question complexity and scope through four stages of data collection. Findings reveal a shift from descriptive to more complex, evaluative, and exploratory questions. Students asked questions from diverse themes, with music and animals being the most common. These insights highlight the importance of scaffolding, culturally relevant content, and adaptive instructional strategies in data science education to empower students from marginalized backgrounds and foster their engagement and success in the field.

Authors

• David Weintrop, University of Maryland

• Xiaoxue Zhou, University of Maryland

• Rotem Israel-Fishelson, University of Maryland

• Peter Moon, University of Maryland

• Yue Xin, University of Maryland