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Many studies in educational data mining address specific learner groups, suchas first-in-family to attend Higher Education, or focus on differences incharacteristics such as gender or ethnicity, with the aim of predictingperformance and designing interventions to improve outcomes. For HigherEducation, this is reflected in significant interest in institutional-levelanalysis of student cohorts and in tools being promoted to Higher EducationInstitutions to support collection, integration and analysis of data. For thoseleading modules/units on degree programmes, however, the reality can be farremoved from the seemingly well-supported and increasingly sophisticatedapproaches advocated in centrally-led data analysis. Module leaders often findthemselves working with a number of student-data systems that are notintegrated, may contain conflicting data and where significant effort isrequired to extract, clean and meaningfully analyse the data. This papersuggests that important lessons may be learned from experiences at module levelin this context and from subsequent analysis of related data collected acrossmultiple years. The changes made each year are described and a range of dataanalysis methods are applied, post hoc, to identify findings in relation to thefour areas of focus. The key findings are that non-engagement with the VirtualLearning Environment in the first three weeks was the strongest predictor offailure and that early engagement correlated most strongly with final grade.General recommendations are drawn from the findings which should be valuable tomodule leaders in environments where access to integrated, up-to-date studentinformation remains a day-to-day challenge, and insights will be presented intohow such bottom-up activities might inform institutional/top-down planning inthe use of relevant technologies.

Exploring Student Engagement and Outcomes: Experiences from Three Cycles of an Undergraduate Module