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Objectives:Interest plays a central role in learning by shaping what, how, when, where, and why learning occurs. In data science education, where complex concepts, lived experiences, and practical skills intersect, capturing and cultivating student interest can be especially generative. This work explores approaches for designing and evaluating interest-driven data science instructional materials.Methods:This paper presents a participatory design study that informs the development of a data science curriculum for high school students. To assess how well learner interests and values are reflected in the resulting curriculum, we used the Integrated Interest Development for Computing Education Framework [56], which provides a concrete operationalization of interest that captures its multifaceted nature.Findings:The paper demonstrates and discusses how participatory design can be used to identify students’ interests and how those interests can be used to inform the creation of an interest-driven curriculum. Further, it highlights how different types of participatory design activities yield insight into different facets of students’ interests and identities, which can then be used to design learning experiences. This work shows how the resulting PD reflects and harnesses the multifaceted nature of student interest and how it can be leveraged to design learning experiences that connect with learners’ lived digital experiences.Conclusions:Participatory design is an effective student-centered approach for tailoring computational learning experiences aligned to students’ voices, values, and interests. The use of various participatory design activities revealed different facets of students’ interests that informed the creation of an interest-driven curriculum that could not have been created without the input of the students themselves. 

In an increasingly data-driven society, it is essential that students understand and critically engage with the data that surrounds them. A key aspect of accomplishing this is helping students understand the importance of data and the impact it can have on their lives. This paper examines the role of real-world authenticity in a high school interest-driven data science curriculum. Through student reflections and project outcomes analysis, the study highlights how real-world data use fosters data practices by allowing students to see data science as relevant and applicable to real-life issues. Findings indicate that students perceived the data exploration activities as authentic and valued the meaningfulness of the data, recognizing its relevance to real-life contexts. 

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.