The Open UniversitySkip to content
 

A large-scale implementation of Predictive Learning Analytics in Higher Education: the teachers' role and perspective

Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek and Hlosta, Martin (2019). A large-scale implementation of Predictive Learning Analytics in Higher Education: the teachers' role and perspective. Educational Technology Research and Development (Early Access).

Full text available as:
[img]
Preview
PDF (Version of Record) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/s11423-019-09685-0
Google Scholar: Look up in Google Scholar

Abstract

By collecting longitudinal learner and learning data from a range of resources, Predictive Learning Analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction. The aim of this study is to evaluate whether providing teachers in a distance learning higher education institution with PLA data predicts students’ performance and empowers teachers to identify and assist students at risk. Using principles of Technology Acceptance and Academic Resistance models, a university-wide, multi- methods study with 59 teachers, nine courses, and 1,325 students revealed that teachers can positively affect students' performance when engaged with PLA. Follow- up semi-structured interviews illuminated teachers' actual uses of the predictive data and revealed its impact on teaching practices and intervention strategies to support students at risk.

Item Type: Journal Item
Copyright Holders: 2019 The Authors
ISSN: 1556-6501
Keywords: predictive learning analytics; teachers; student performance; student retention; higher education
Academic Unit/School: Faculty of Wellbeing, Education and Language Studies (WELS)
Other Departments > Other Departments
Other Departments
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Item ID: 62191
Depositing User: Martin Hlosta
Date Deposited: 08 Jul 2019 08:12
Last Modified: 26 Jul 2019 20:49
URI: http://oro.open.ac.uk/id/eprint/62191
Share this page:

Metrics

Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU