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Empowering online teachers through predictive learning analytics

Herodotou, Christothea; Hlosta, Martin; Boroowa, Avinash; Rienties, Bart; Zdrahal, Zdenek and Mangafa, Chrysoula (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50(6) pp. 3064–3079.

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This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made “average” use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes.

Item Type: Journal Item
Copyright Holders: 2019 British Educational Research Association
ISSN: 1467-8535
Keywords: predictive analytics; online teachers; Higher Education; Quasi-experimental
Academic Unit/School: Institute of Educational Technology (IET)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Other Departments > Business Development Unit
Other Departments
Item ID: 62192
Depositing User: Martin Hlosta
Date Deposited: 08 Jul 2019 09:18
Last Modified: 14 Dec 2019 00:24
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