Exploring Interest-Driven Data Science Through Participatory Design

Abstract

Interest is a powerful catalyst for learning. Cultivating student interest is critical in data science education, where complex concepts and practical skills intersect. This paper explores the use of participatory design as a methodology for applying interest development theory to inform the design of an innovative data science curriculum for high school students. This study presents an analysis of participatory design activities using the Integrated Interest Development for Computing Education Framework (Michalis & Weintrop, 2022), highlighting various operationalizations of interest in data science contexts. By examining the multifaceted dimensions of interest, this work demonstrates how diverse activities provide a context for students to voice their interests. The paper’s findings contribute to the discourse on innovative educational approaches that engage students in data science by aligning with their interests.