Presentation Schedule
Designing Inclusive Data Science Literacy for Non-STEM Majors: A 2023 Baseline and Design-Based Extensions Through 2025 (102235)
Session Chair: Elodie Billionniere
Sunday, 4 January 2026 14:35
Session: Session 4 (Parallel)
Room: Hawaii Convention Center: Room 304B
Presentation Type: Oral Presentation
Japan's government has strongly promoted mathematics, data science, and AI literacy across higher education. Yet universities respond differently depending on student demographics and institutional philosophy, leading to unique designs for data science education. Against this backdrop, we asked a common question: whether such education raise students' motivation to engage with data science? In 2023, we delivered a university-wide, on-demand literacy course to students from seven faculties (N≈2,000). Pre/post surveys with eight Likert items (mapped to SDT: Self-Determination Theory, autonomy, competence, relatedness) were analyzed using two-way repeated-measures ANOVA (time × faculty). Open-ended comments and learning-management logs were used to enrich interpretation. Results showed significant pre–post change on most items, but with notable faculty-level interactions. Gains in perceived fairness of AI and enthusiasm were clear in some groups. In contrast, confidence in competence-related skills even declined in others, suggesting that a scalable on-demand format does not support all learners equally. Guided by these findings and informed by national initiatives at comparable universities, we designed instructional improvements during FY2024–2025. This includes exploring the introduction of collaborative learning supported by generative AI, aligning tasks with SDT to strengthen autonomy and competence, and refining documentation of course architecture and engagement metrics. We present the 2023 baseline, explain how it influenced subsequent redesigns, and outline a 2026 evaluation plan that links attitudinal change with behavioral indicators. This work highlights how national policy interacts with local educational diversity, and how design-based improvement can advance scalable yet inclusive data science literacy.
Authors:
Yuko Murakami, Hiroshima University, Japan
Rie Enomoto, Kokushikan University, Japan
Yoshinori Honma, Kokushikan University, Japan
Tomohiro Inagaki, Hiroshima University, Japan
Naoki Itoh, Kokushikan University, Japan
Ryosuke Oyanagi, Kokushikan University, Japan
Motoo Sekiguchi, Kokushikan University, Japan
About the Presenter(s)
Dr Murakami Yuko is currently an Assistant Professor at the Information Media Center at Hiroshima University, Japan.
Connect on Linkedin
https://www.linkedin.com/in/yuko-murakami-098744159/
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