学术文献库

Many studies in educational data mining address specific learner groups, such as first-in-family to attend Higher Education, or focus on differences in characteristics such as gender or ethnicity, with the aim of predicting performance and designing interventions to improve outcomes. For Higher Education, this is reflected in significant interest in institutional-level analysis of student cohorts and in tools being promoted to Higher Education Institutions to support collection, integration and analysis of data. For those leading modules/units on degree programmes, however, the reality can be far removed from the seemingly well-supported and increasingly sophisticated approaches advocated in centrally-led data analysis. Module leaders often find themselves working with a number of student-data systems that are not integrated, may contain conflicting data and where significant effort is required to extract, clean and meaningfully analyse the data. This paper suggests that important lessons may be learned from experiences at module level in this context and from subsequent analysis of related data collected across multiple years. The changes made each year are described and a range of data analysis methods are applied, post hoc, to identify findings in relation to the four areas of focus. The key findings are that non-engagement with the Virtual Learning Environment in the first three weeks was the strongest predictor of failure and that early engagement correlated most strongly with final grade. General recommendations are drawn from the findings which should be valuable to module leaders in environments where access to integrated, up-to-date student information remains a day-to-day challenge, and insights will be presented into how such bottom-up activities might inform institutional/top-down planning in the use of relevant technologies.