Author Information
Yuko Murakami, Hiroshima University, JapanRie 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
Abstract
Japan’s government has strongly promoted mathematics, data science, and AI literacy across higher education. However, 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: Does 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 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, competence-related confidence declined among 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 a 2024–2025 expansion plan. 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 meets local educational diversity, and how design-based improvement can advance scalable yet inclusive data science literacy.
Paper Information
Conference: IICE2026Stream: Higher education
This paper is part of the IICE2026 Conference Proceedings (View)
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To cite this article:
Murakami Y., Enomoto R., Honma Y., Inagaki T., Itoh N., Oyanagi R., & Sekiguchi M. (2026) Designing Inclusive Data Science Literacy for Non-STEM Majors: A 2023 Baseline and Design-Based Extensions Through 2025 ISSN: 2189-1036 – The IAFOR International Conference on Education – Hawaii 2026 Official Conference Proceedings (pp. 119-127) https://doi.org/10.22492/issn.2189-1036.2026.12
To link to this article: https://doi.org/10.22492/issn.2189-1036.2026.12
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