6.1.5. Learning analytics

The Horizon Report: 2019 Higher Education Edition (Alexander et al, 2019), produced by the EDUCAUSE Learning Initiative, identifies learning analytics as one of the digital strategies and technologies expected to enter mainstream use in the near future. Learning analytics is the science of analyzing raw data in order to make conclusions about that information. When learners use a Learning Management System (LMS), social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked.  Learning analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information without the help of analytics. In the case of education for example, knowing – on a learner-to-learner basis – where time is being spent, who is using course materials and how progress falls across a class, offers valuable insights regarding the learner’s experience. Given that educators are already likely to have an idea about which learners require help and which areas of a curriculum need attention, analytics can be used to target evidence that informs those improvements (Wilson et al,2017). For instructors, analytics do not just reveal previously unknown information about learners, they also support qualitative judgements and existing expertise (Tosun and Kurubacak, 2016).

From a different perspective,  learning analytics has been blamed to “entrench and deepen the status quo, disempower and disenfranchise vulnerable groups, and further subjugate public education to the profit-led machinations of the burgeoning ‘data economy’” (Selwyn, 2019, p. 11). The need to be critical  about Learning Analytics touches also upon the ethical considerations associated with the use of educational data. Ethical implications arise as well in how personal data is collected, stored, analysed and presented to different stakeholders. Hence, procedures regulating access and usage of educational data need to be designed and implemented before Learning Analytics frameworks are implemented. As noted by Tosun and Kurubacak (2016, p.315), “this will include transparency of applied algorithms and weighting of educational data for predictive modelling. Storing and processing anonymised personal data is only a small step towards a more comprehensive educational data governance structure for Learning Analytics”.