Tsukuba, Japan—Data science deepens understanding of natural and social phenomena and informs decision‑making through analysis of diverse data types using mathematical and computational methods. Since the 2010s, data have become increasingly accessible not only in science, engineering, and medicine, but also in fields such as the social sciences, humanities, sports, and the arts. This results in rapid growth in societal demand for data‑science‑related knowledge and skills. However, effective instructional methods for data science remain underexplored.
In this exploratory case study, the research team quantitatively assessed the educational impact of "Data Science," a required first-year Common Foundation Subject at the University of Tsukuba since the 2019 academic year. The analysis demonstrated how introducing examples from application areas relevant to students' interests and engaging them in analyzing their own data can strengthen their motivation and support a more profound understanding. These results suggest that instructional approaches grounded in intrinsic motivation theory may also be effective in teaching an interdisciplinary field such as data science.
Overall, the findings show that applying data‑science methods to evaluate classroom practices can improve the quality of data science education.
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This study is supported by the Ministry of Education, Culture, Sports, and Technology, Japan via the "Program for Developing Top-level Interdisciplinary Data Science and AI Experts to Solve Global Challenges through Advanced Use of Data Science and AI."
Title of original paper:
Targeting students' interests to facilitate their learning of data science
Journal:
Discover Data
DOI:
10.1007/s44248-026-00101-6
Professor HIRATA, Yoshito
Institute of Systems and Information Engineering, University of Tsukuba
Institute of Systems and Information Engineering
Discover Data
Targeting students’ interests to facilitate their learning of data science
26-Feb-2026