Curriculum Reform in Data Science Education: Enhancing Learning Outcomes With Scaffolding Learning Through Data Storytelling (SLDS)

Abstract

Data storytelling (DS) employs narrative and visualization techniques to communicate insights from data, offering potential benefits for educational settings. This study introduces the framework of “Scaffolding Learning through Data Storytelling (SLDS)” as an explanatory approach to enhance students’ learning outcomes in an undergraduate general education course on data literacy. Building on the key DS principles from Ryan (2016) and Knaflic (2015), we created a series of data stories to address students’ diverse challenges in learning data science, taking into account their varied academic backgrounds, including STEM and non-STEM disciplines and differences in academic years. Incorporating Hadwin and Winnie’s (2001) concept of “tacit scaffolds,” SLDS integrates these stories into the curriculum stage by stage, aiming to enhance student engagement and understanding by encouraging them to read and think without explicitly directing or instructing specific studying activities. The effectiveness of SLDS was assessed through students’ self-reported metrics of learning attention, relevance, confidence, and satisfaction, as well as multiple-choice questions measuring content comprehension. We anticipate that SLDS will improve learning outcomes more effectively than traditional methods, providing insights into easy-to-approach data narrative structuring and visualization design and its educational benefits for students from all backgrounds. This study aims to offer evidence on the application of DS in teaching and learning, laying a foundation for incorporating DS techniques into curricula and informing future educational practices for various educational levels and disciplines.

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Author Information
Yiyun Fan, National University of Singapore, Singapore
Kah Loon Ng, National University of Singapore, Singapore
Wan Mei Amanda Soon, National University of Singapore, Singapore