Examining Current State of Data Science Education in High Schools and Higher Education Institutions
DOI:
https://doi.org/10.52731/lir.v005.469Keywords:
curriculum management, data science education, MDASH, non-STEM, STEMAbstract
Data science education (DSE) has become a global trend, and in Japan, it is gaining increasing emphasis, as evidenced by the introduction of accredited programs for higher education institutions. However, despite the proactive establishment of institutional frameworks, there has been limited discussion regarding the educational effectiveness within individual institutions, such as high schools and universities, as well as the continuity between these educational stages. This study administered a data science comprehension assessment to 406 high school and 1,652 first-year university students in early April 2025, prior to receiving formal DSE at their respective institutions. While university students scored slightly higher than high school students, the overall comprehension levels were low, and differences across departments, particularly between STEM and non-STEM students, were negligible. These results suggest the need to reassess the respective roles of individual educational institutions, strengthen curricular continuity, and refine the specific content delivered at each stage.
References
D. Andrea, L. Paul, C. Lillian and S. Christian, “ACM Task Force on Data Science Education: Draft Report and Opportunity for Feedback, ” Proc. the 50th ACM Technical Symposium on Computer Science Education, 2019, pp. 496-497.
A. Takemura, S. Izumi, K. Saito, T. Himeno, H. Matsui and H. Date, “Shiga Uni-versity Model of Data Science Education (in Japanese),” Proc. the Institute of Sta-tistical Mathematics, 2018, pp.63-78.
D. Conway, “The Data Science Venn Diagram. Retrieved from Drew Conway Data,” Data Consulting, http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram (accessed 15-Apr-2025)
J. Ullman, “What Is Data Science? A Turing Award Winner Shares His View,” BIGDATAWIRE, https://www.bigdatawire.com/2021/08/18/what-is-data-science-a-turing-award-
winner-shares-his-view/ (accessed 15-Apr-2025)
Ministry of Education, Culture, Sports, Science and Technology (MEXT), “Ap-proved Program for Mathematics, Data Science and AI Smart Higher Education (in Japanese),” https://www.mext.go.jp/a_menu/koutou/suuri_datascience_ai/00001.htm (accessed 15-Apr-
Director General for Science, Technology and Innovation Policy, Cabinet Office, “Considerations for Developing the ' Approved Program for Mathematics, Data Science and AI Smart Higher Education’ (in Japanese),” National Diet Library, https://warp.ndl.go.jp/info:
ndljp/pid/11451087/www.kantei.go.jp/jp/singi/ai_senryaku/suuri_datascience_ai/dai1/siryou1-2.pdf (accessed 15-Apr-2025)
Ministry of Education, Culture, Sports, Science and Technology (MEXT), “List of Approved and Selected Institutions (in Japanese),” https://www.mext.go.jp/a_menu/koutou/suuri_
datascience_ai/mext_00005.html (accessed 15-Apr-2025)
K. Mike, B. Kimelfeld, and O. Hazzan, “The Birth of a New Discipline: Data Science Education,” Harvard Data Science Review, vol.5(4), 2023, pp.1-26.
L. Barboza and E. Teixeira, “Effect of data science teaching for non-STEM students: A systematic Literature Review ,” Proc. the 15th International Conference on Software Engineering Advances, 2020, pp.118-122.
M. Foster and Z. Tasnim. “Data science and graduate nursing education: a critical literature review,” Clinical Nurse Specialist, vol.34(3), 2020, pp.124-131.
H. Wu, “Systematic Study of Big Data Science and Analytics Programs,” Proc. 2017 ASEE Annual Conference & Exposition, 2017; doi:10.18260/1-2—28900.
D. Preiss, J. Sperling, R. M. Huang, K. Bradbury, T. Nechyba, R. Calderbank, G. Herschlag and J. S. Borg, “Where Data Science and the Disciplines Meet: Innova-tions in Linking Doctoral Students with Masters-Level Data Science Education,” Harvard Data Science Review 6 (4), 2024, pp.1-64.
J. Ebbeler, C.L. Poortman, K. Schildkamp and J. M. Pieters, “The effects of a data use intervention on educators’ satisfaction and data literacy,” Educational Assess-ment, Evaluation and Accountability, vol.29, 2017, pp.83-105.
N. Kayaki, C. Hayashi, Y. Higuchi, H. Nishioka and M. Kobayashi, “Educational Effect Measurement and Assessment of Data Literacy Education for Students Ma-joring in Business Administration (in Japanese),” The Journal of Business Studies Ryukoku University, vol.63(3･4), 2024, pp.1-14.
A. Khalemsky, R. Gelbard, and Y. Stukalin, “Constructing a Course on Classifi-cation Methods for Undergraduate Non-STEM Students: Striving to Reach Knowledge Discovery,” Journal of Statistics and Data Science Education, vol.33(1), 2025, pp.68-76.
E. Tanaka and T. Ohkawauchi, “Analysis of Discrepancies in Learning Awareness of Data Science Across Disciplines – a Case Study of Nihon University, College of Humanities and Sciences –,” IIAI Letters on Institutional Research, vol.5, 2024, pp.1-12.
Mathematical, Data Science, and AI Education Enhancement Hub Consortium, “Literacy Level Model Curriculum Corresponding Materials (in Japanese),” http://www.mi.u-tokyo.
ac.jp/consortium/e-learning.html (accessed 15-Apr-2025)
G. Kitagawa and A. Takemura, ed., Data science as the liberal arts (in Japanese),
Kodansha, 2024.
N. Akazawa, H. Akaike, Y. Shibata, H. Kakuda and Y. Nakayama, “Changes in Terms which Appear in Informatics Textbooks at High Schools in Japan (in Japa-nese),” IPSJ Transactions on Computers and Education, vol.10(1), 2024, pp. 13-24.
researchmap, “Terminology from All Information Subject Textbooks (in Japa-nese),” https://researchmap.jp/n-akazawa/works/43305921?lang=en (accessed 15-Apr-2025)
S. Ohashi, “Connectivity Between High School and University Data Science Ed-ucation(in Japanese),” RIMS Kokyuroku, vol.2236, 2022, pp.1-8. https://repository.kulib.kyoto-u.ac.jp/dspace/handle/2433/282945?mode=full&locale=en (accessed 15-Apr-2025)
