Enhancing statistics education through student-generated data and authentic assessment

The proliferation of data sources and the growth of data science have increased statistics course enrolments, creating challenges for educators teaching diverse student populations with varying mathematical backgrounds. This study examines an innovative, authentic assessment approach using student-generated data in a large first-year Business Analytics course at The University of Sydney. Traditional individual assignments were replaced with a multi-component collaborative project utilising data from a student survey on plastic recycling behaviours, including individual presentations, progress reports, and group final reports. The innovation was evaluated using pre- and post-implementation surveys, focus groups, and Unit of Study Survey scores, analysed against an authentic assessment framework examining realism, cognitive challenge, and evaluative judgement dimensions. Results showed significant improvements in student engagement and satisfaction, with marked gains in feedback provision, sense of learning community, and critical thinking skills. Focus groups revealed that continuous use of familiar data reduced cognitive load while enhancing understanding of statistical concepts as an integrated whole. The study demonstrates how student-generated data can be used to effectively implement authentic assessment principles in large-scale statistics education and outline its relevance to broader educational contexts, providing a practical model for enhancing learning and engagement.