An update to the design and delivery of an introductory statistics subject for a large student cohort with diverse educational backgrounds and chosen qualifications

This is a case study update on the design, delivery and performance of a first-year undergraduate statistics subject (or course/unit/module), entitled Making Sense of Data, at La Trobe University. This subject is taught both face-to-face and online to a large cohort of students across a number of domestic and international campuses. In order to support the diverse educational backgrounds and chosen qualifications of students, Making Sense of Data contains both a solid foundation of core statistics content, and a stream-specific component. All students cover the core content, and the stream content that best aligns with their chosen qualification(s). This flexible, accessible structure helps ensure that the subject’s intended learning outcomes are met whilst highlighting the relevance of statistics for each student’s field of study and allowing for stream-specific lectures, assessments and statistical software to be used. In this paper we discuss various feedback-based refinements implemented iteratively in Making Sense of Data in recent years, including adjustments to classes, learning content, assessments, and student support, as well as adjustments made in response to the emergence of Generative AI. We evaluate how these changes to the subject have impacted student outcomes in terms of engagement, satisfaction and performance.