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

Christothea Herodotou*, Bart Rienties, Martin Hlosta, Avinash Boroowa, Chrysoula Mangafa, Zdenek Zdrahal

*Corresponding author for this work

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    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.
    Original languageEnglish
    Article number100725
    JournalThe Internet and Higher Education
    Volume45
    Early online date13 Jan 2020
    DOIs
    Publication statusPublished - Apr 2020

    Keywords

    • Distance learning
    • Higher education
    • OU Analyse
    • Predictive Learning Analytics (PLA)
    • Scalable implementation
    • PERFORMANCE
    • RESISTANCE
    • BELIEFS
    • SUPPORTING TEACHERS
    • STUDENTS

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