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Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting

Nguyen, Quan; Rienties, Bart and Richardson, John T. E. (2019). Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting. Assessment & Evaluation in Higher Education (Early Access).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1080/02602938.2019.1679088
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Abstract

Although the attainment gap between black and minority ethnic (BME) students and White students has persisted for decades, the potential causes of these disparities are highly debated. The emergence of learning analytics allows researchers to understand how students engage in learning activities based on their digital traces in a naturalistic setting. This study investigates the attainment gap by analysing the differences in behavioural engagement between different ethnic groups. Using multilevel models of academic performance, demographics, and online traces of 149,672 students enrolled in 401 modules in a distance learning setting, we confirmed the existing attainment gap. After controlling for other demographics, module characteristics and engagement, BME students were between 19% and 79% less likely to complete, pass, or achieve an excellent grade compared to White students. Given the same academic performance, BME students spent 4-12% more time on studying than White students. While the attainment gap remained persistent after controlling for academic engagement, our study further highlighted the inequality of attainment between BME and White students.

Item Type: Journal Item
Copyright Holders: 2019 Informa UK Ltd.
ISSN: 1469-297X
Keywords: attainment gap; learning analytics; distance learning; BME students; engagement
Academic Unit/School: Institute of Educational Technology (IET)
Research Group: Centre for Research in Education and Educational Technology (CREET)
OpenTEL
Item ID: 67217
Depositing User: Bart Rienties
Date Deposited: 10 Oct 2019 10:05
Last Modified: 15 Dec 2019 10:18
URI: http://oro.open.ac.uk/id/eprint/67217
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