There are several steps to the learning analytics (LA) data cycle. These include:
- Collection and Acquisition: data is collected and acquired from one, or several, sources.
- Storage: data is stored so it can be worked on. This storage may be located within the system which is used to produce the data, or the data may need to be exported and stored elsewhere.
- Cleaning: there will usually be a need for some cleaning of the data so that it is in a format which can be used by the analysis software. This will be especially true if the data has been collected from a range of different sources, as each source will have its own data format.
- Integration: if data is collected from multiple sources, it needs to be integrated into a single file so that it can be analysed.
- Analysis: a software package is used to analyse the data to produce statistics about it.
- Representation and Visualisation: in order to make the results of the data analysis easier to understand, they need to be represented and visualised in some way e.g. as a graph or chart, or a network diagram.
- Action: finally, some action should be taken on the basis of the results of the data analysis. There is no point in initiating this LA data cycle if there is not going to be an action at the end of it.
Although LA have traditionally been used by departments other than the library, there are library systems which could produce data which could be analysed using this cycle. We can collect data about loans (from our catalogue), database access from proxy server logs), and website usage. Librarians are very good at collecting data and statistics about our patrons and collections, but often there is no particular reason for collecting them. LA ties nicely into the philosophy of Evidence Based Library and Information Practice (EBLIP), which is defined as:
Evidence based librarianship (EBL) is an approach to information science that promotes the collection, interpretation, and integration of valid, important and applicable user reported, librarian observed, and research derived evidence. The best available evidence moderated by user needs and preferences is applied to improve the quality of professional judgments.
Booth, A. (2002). From EBM to EBL: Two steps forward or one step back? Medical Reference Services Quarterly, 21(3), 51-64. doi: 10.1300/J115v21n03_04
By using an approach similar to the LA data cycle, it’s possible for librarians to collect the evidence that they can use to improve existing services or develop new ones.
Before LA are used at an institution, there needs to be consideration of policies and planning around it. There should be policies dealing with the ethical collection and use of the data, as well as a clear outline of how the results of the data analysis will be used to improve the learning experience. LA is nicely suited to be part of the quality and evaluation system within an institution, and the LA cycle could be incorporated into a continual improvement process.
As LA can potentially use data from a range of units from across the university, there needs to be some strategic planning around how it will be implemented and used. The results of LA data analysis could be used to inform changes to teaching practice, and these changes need to have a sound planning framework associated with them. Strategic planning could also help mitigate the “bright and shiny syndrome”, where institutions rush to embrace the latest new technology without a plan for how it will be used. LA is a powerful tool for providing insight into the learner experience, but it should not be relied on as the sole driver for change.