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Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek and Hlosta, Martin
(2019).
DOI: https://doi.org/10.1007/s11423-019-09685-0
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.
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About
- Item ORO ID
- 62191
- Item Type
- Journal Item
- ISSN
- 1556-6501
- Keywords
- predictive learning analytics; teachers; student performance; student retention; higher education
- Academic Unit or School
-
Institute of Educational Technology (IET)
Other Departments > Business Development Unit
Other Departments
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2019 The Authors
- Depositing User
- Martin Hlosta