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Implementing predictive learning analytics on a large scale: the teacher's perspective

Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek; Hlosta, Martin and Naydenova, Galina (2017). Implementing predictive learning analytics on a large scale: the teacher's perspective. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, ACM, NY, pp. 267–271.

DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3027385.3027397
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Abstract

In this paper, we describe a large-scale study about the use of predictive learning analytics data with 240 teachers in 10 modules at a distance learning higher education institution. The aim of the study was to illuminate teachers' uses and practices of predictive data, in particular identify how predictive data was used to support students at risk of not completing or failing a module. Data were collected from statistical analysis of 17,033 students' performance by the end of the intervention, teacher usage statistics, and five individual semi-structured interviews with teachers. Findings revealed that teachers endorse the use of predictive data to support their practice yet in diverse ways and raised the need for devising appropriate intervention strategies to support students at risk.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 ACM
ISBN: 1-4503-4870-X, 978-1-4503-4870-6
Keywords: predictive analytics; teachers; student retention; higher education
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Learning and Teaching Innovation (LTI) > Technology Enhanced Learning (TEL)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Other Departments > Strategy Office
Other Departments
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 48922
Depositing User: Christothea Herodotou
Date Deposited: 15 Mar 2017 11:36
Last Modified: 02 May 2018 14:27
URI: http://oro.open.ac.uk/id/eprint/48922
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