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Rogaten, Jekaterina and Rienties, Bart Carlo
(2018).
DOI: https://doi.org/10.1080/23752696.2018.1484671
Abstract
With the introduction of the Teaching Excellence Framework a lot of attention is focussed on measuring learning gains. A vast body of research has found that individual student characteristics influence academic progression over time. This case-study aims to explore how advanced statistical techniques in combination with Big Data can be used to provide potentially new insights into how students are progressing over time, and in particular how students’ socio-demographics (i.e., gender, ethnicity, Social Economic Status, prior educational qualifications) influence students’ learning trajectories. Longitudinal academic performance data were sampled from 4,222 first year STEM students across nine modules and analysed using multilevel growth-curve modeling. There were significant differences between white and non-white students, and students with different prior educational qualifications. However, student-level characteristics accounted only for a small portion of variance. The majority of variance was explained by module-level characteristics and assessment level characteristics.
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About
- Item ORO ID
- 55348
- Item Type
- Journal Item
- ISSN
- 2375-2696
- Project Funding Details
-
Funded Project Name Project ID Funding Body A longitudinal mixed method study of learning gain: applying Affective-Behaviour Cognition framework at 3 institutions 10007773 Not Set - Keywords
- Learning gains; learning analytics; higher education; STEM; grades
- Academic Unit or School
- Institute of Educational Technology (IET)
- Research Group
- Education
- Copyright Holders
- © 2018 The Authors
- Depositing User
- Jekaterina Rogaten