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

Herodotou, Christothea and Rienties, Bart and Hlosta, Martin and Boroowa, Avinash and 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. p. 100725. ISSN 1096-7516

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Official URL: 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 they provided substantial insights, most of these studies have been implemented in single-course or small-scale settings. There are only a few studies that are large-scale and institutional-wide adaptations of LA and have explored the stakeholders' perspectives (i.e., teachers, students, researchers, management) 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 4 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 4 years, OUA has been accessed by 1159 unique teachers and reached 23,180 students 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 shown to be critical to the scalable PLA implementation were: Faculty's engagement with OUA, teachers as “champions”, evidence generation and dissemination, digital literacy, and conceptions about teaching online.

Item Type: Journal Article
Keywords: Predictive Learning Analytics (PLA), Higher education, Distance learning, Scalable implementation, OU Analyse
Faculty: Faculty of Health, Education, Medicine & Social Care
Depositing User: Lisa Blanshard
Date Deposited: 11 May 2020 10:37
Last Modified: 11 May 2020 10:37
URI: https://arro.anglia.ac.uk/id/eprint/705506

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