Reconceptualizing analytics in education: A quest for a common ground

Kam Cheong Li, Beryl Y Y Wong and Esther W S Chok
The Open University of Hong Kong

Hong Kong SAR, China

Maturing big data techniques have made it feasible to transform massive volume of unstructured data into meaningful patterns that capture, model and predict the behaviours of diverse target groups. In educational settings, analytics has been increasingly used during the past decade. Terms such as 'learning analytics' (LA) and 'academic analytics' (AA) have been adopted to describe various levels and functions of analytics in academia. A number of frameworks, not necessarily conflicting, have also been proposed to classify the wide range of analytics in the education sector. Nevertheless, there is little consensus on how these types of analytics are to be defined and categorized. This paper in search of a common ground that can bridge the different ideas of scholars proposes an integrated framework that reorganizes and elaborates on the existing classifications of analytics in education that have been dominated by two rather confusing terms: LA and AA. The proposed framework reconceptualizes AA and LA, and elaborates on their three operational levels (micro-, meso- and macro-level) and six functioning scopes (learner, course, departmental, institutional, regional and national, and international levels). The findings at each level can be used to generate descriptive and predictive reports that can facilitate effective decision-making in institutions. The proposed framework will be useful for understanding this new domain, deciding on levels of application, and carrying out further studies.