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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. 

Tailoring learning materials and activities to the learners is crucial for enhancing their engagement and interest. With a student-centered approach and iterative design, we developed a new interest-driven API-based data science curriculum for high school students. We revised our pilot curriculum based on feedback from our pilot teacher, student performance and course evaluations, and class observations. Key modifications included incorporating real-world examples of data science applications, expanding coding activities, and redefining class discussions to improve student involvement. Here, we summarize some of these changes made to support the development of data scientist identities and increased student engagement. This work highlights the significance of research-practice partnerships and recommends leveraging feedback from both educators and students to enhance curriculum delivery in K-12 settings. It contributes to the evolving field of data science education in K-12 classrooms and emphasizes the value of collaborative curriculum development based on practical classroom experience and feedback. 

In today's data-driven world, students must be able to explore and analyze the data surrounding them. A crucial aspect of this process is formulating meaningful research questions that can be addressed with the available data. This study investigates the data science inquiry process of high school students. We analyzed 213 student-generated questions from the final project of an innovative interest-driven data science curriculum. Through a qualitative analytic approach, we examined changes in question types, complexity, and scope across four stages of data collection. The findings shed light on a shift from descriptive to more complex, evaluative, and exploratory questions. It also highlights the importance of providing scaffolding, culturally relevant content, and adaptive instructional strategies in data science education. These elements are essential for empowering students from marginalized backgrounds and fostering their engagement and success in the field.