The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study

Herodotou, Christothea; Rienties, Bart; Hlosta, Martin; Boroowa, Avinash; Mangafa, Chrysoula and Zdrahal, Zdenek (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. Internet and Higher Education, 45, article no. 100725.

DOI: https://doi.org/10.1016/j.iheduc.2020.100725

Abstract

A vast number of studies reported exciting innovations and practices in the field of Learning Analytics (LA). Whilst these studies provide substantial insights, most of these studies were implemented in single-course or small-scale settings. There are only few studies that are large-scale and institutional-wide adaptations of LA that have explored the stakeholders' (i.e., teachers, students, researchers, management) perspectives and involvement with LA. This study reports on one such large-scale and long-term implementation of Predictive Learning Analytics (PLA) spanning a period of four years at a distance learning university. OU Analyse (OUA) is the PLA system used in this study, providing predictive insights to teachers about students and their chance of passing a course. Over the last four years, OUA has been accessed by 1,159 unique teachers and reached 23,180students in 231 undergraduate online courses. The aim of this study is twofold: (a) to reflect on the macro-level of adoption by detailing usage, challenges and factors facilitating adoption at an organisational level, and (b) to detail the micro-level of adoption, that is the teachers' perspectives about OUA. Amongst the factors critical to the scalable PLA implementation were: the Faculty's engagement with OUA, teachers as "champions", evidence generation and dissemination, digital literacy, and conceptions about teaching online.

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