Bridging Data Science and Information Literacy
Tableau Integration and Assessment Variability in a University-Wide First-Year Course
Abstract
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.
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