https://iaiai.org/journals/index.php/IJIRM/issue/feed 2026-02-11T08:14:20+00:00 Tokuro Matsuo editorial-office@iaiai.org Open Journal Systems <p align="justify"><strong>International Journal of Institutional Research and Management (IJIRM)</strong> is a peer-reviewed/refereed international journal that is dedicated to the theory and practice in Institutional Research on higher education and research management. IJIRM strives to cover all aspects of working out new technologies and theories, and also case study for evidence-based institutional management, big data in universities, research administration, educational technology, and multidisciplinary topics on institutional research.</p> https://iaiai.org/journals/index.php/IJIRM/article/view/911 2026-02-11T08:14:20+00:00 Shintaro Tajiri shintajiri@gmail.com Kunihiko Takamatsu ktakamatu@irds.titech.ac.jp Naruhiko Shiratori nshirato@tcu.ac.jp Shotaro Imai imai.s.aj@m.titech.ac.jp Sayaka Matsumoto matsumoto.s.as@m.titech.ac.jp Tetsuya Oishi oishi@ltc.kyutech.ac.jp Masao Mori mori@irds.titech.ac.jp Masao Murota murota@ila.titech.ac.jp

<p>This paper presents an analysis of implementing a university-wide data science education initiative at Hokuriku University, focusing on the integration of Tableau visualization platform and multi-instructor assessment management. The study examines data from 1,004 first-year students across four faculties over two academic years (2022-2023), analyzing both learning outcomes and assessment patterns among different instructors. Results indicate that while Tableau integration enhanced student engagement through hands-on analysis of real-world campus data, with completion rates exceeding 90% across most departments, significant variations emerged in grade distributions among instructors despite standardized assessment criteria. Statistical analysis using Kruskal-Wallis tests and Dunn's test with Bonferroni Correction revealed specific patterns in these variations, suggesting the influence of both instructor assessment styles and student population characteristics. These findings provide valuable insights for institutions implementing similar university-wide data science education programs, particularly regarding assessment standardization in multi-instructor environments.</p>

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