Self-Regulated Learning in Data Science Education: Designing and Refining a Data Storytelling-Driven Approach through Students’ Perspectives

This study explores how undergraduate students from diverse academic backgrounds engage with and perceive the integration of data storytelling (DS) and self-regulated learning (SRL) in the context of introductory data science education. Building on established DS frameworks and extending previously developed data stories, this study focuses on two key aspects: (1) students’ perceptions and perspectives regarding the integration of DS and SRL, and (2) based on the existing four stories, potential improvements that can be made to address students’ challenges and needs to inform similar future narrative designs. Participants engaged with these data stories accompanied by comprehension questions to support active learning, followed by group discussion interviews to gain deeper insight into their experiences. A thematic analysis approach was employed to evaluate students’ perceptions and perspectives. Findings reveal that students appreciated the clarity and effectiveness of the current data story design, while also identifying areas for improvement. Overall, the results suggest that integrating SRL with DS can serve as a promising and inclusive pedagogical strategy, fostering SRL engagement and supporting diverse learners in data science education.