Unveiling University Students' Data Literacy: A Case Study on Modeling Reasoning in Data Mining Projects

Abstract

In the era of big data, cultivating students' data literacy is of paramount importance. Data literacy encompasses the abilities to collect, manage, analyze, and apply data. As data science is a superset composed of mathematics and statistics, computer science, and specific application fields, it is crucial to investigate data literacy development through the lens of statistical education. An essential component of statistical reasoning is modeling reasoning, which is also fundamental to data literacy. However, limited research exists on the manifestation of data literacy among university students in real-world tasks. Therefore, this study explores the forms of modeling reasoning exhibited by university students in a data mining project. The findings reveal that, in a real teaching setting, university students' modeling reasoning ability follows a spiral progression. Considering these insights, modeling reasoning, as the core of data mining activities, plays a pivotal role in fostering students' data literacy. To cultivate data literacy among university students in the age of big data, we recommend implementing project-based learning, incorporating ill-structured problems and real, complex, massive datasets as project backgrounds.