Predictive Learning Analytics 'At Scale': Guidelines to Successful Implementation in Higher Education
DOI:
https://doi.org/10.18608/jla.2019.61.5Keywords:
Predictive learning analytics, higher education, distance learning, management, guidelines, adoption.Abstract
Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders (i.e., managers, teachers, students) about PLA. This paper presents an “at scale” implementation of PLA at a distance learning higher education institution and details, in particular, the perspectives of 20 educational managers involved in the implementation. It concludes with a set of recommendations about how best to adopt and apply large-scale PLA initiatives in higher education.
Downloads
Published
2019-04-14
How to Cite
Herodotou, C., Rienties, B., Verdin, B., & Boroowa, A. (2019). Predictive Learning Analytics ’At Scale’: Guidelines to Successful Implementation in Higher Education. Journal of Learning Analytics, 6(1), 85—95. https://doi.org/10.18608/jla.2019.61.5
Issue
Section
Practical Reports
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
