Participatory Design as a Mechanism for Informing an Interest-Driven Data Science Curriculum

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 article 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 article 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 Participatory Design (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.