What role for libraries in learning analytics?
Purpose This case study provides an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the inclusion of library data drawn from the Library Cube. Design/methodology/approach The Student Support & Education Analytics (SSEA) team at UOW is tasked with creating policy, frameworks and infrastructure for the systematic capture, mapping and analysis of data from the across the University. The initial dataset includes: logfile data from Moodle sites, Library Cube, student administration data, tutorials and student support service usage data. Using the learning analytics data warehouse UOW is developing new models for analysis and visualisation with a focus on the provision of near real-time data to academic staff and students to optimise learning opportunities. Findings The distinct advantage of the learning analytics model is that the selected datasets are updated weekly, enabling near real time monitoring and intervention where required. Inclusion of Library data with the other often disparate datasets from across the University has enabled development of a comprehensive platform for learning analytics. Future work will include the development of predictive models using the rapidly growing learning analytics data warehouse. Practical implications Data warehousing infrastructure, the systematic capture and exporting of relevant library datasets are requisite for the consideration of library data in Learning Analytics. Originality/value What was not anticipated five years ago when the Value Cube was first realised, was the development of learning analytic services at UOW. The Cube afforded UWL considerable advantage: the framework for data harvesting and analysis was established, ready for inclusion within learning analytics datasets and subsequent reporting to facult
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