Which first-year students are making most learning gains in STEM subjects?

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 4222 first-year STEM students across nine modules and analysed using multi-level growth-curve modelling. 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.

KEYWORDS:

• Learning gains
• learning analytics
• higher education
• STEM
• grades

Acknowledgments

This research was conducted as part of a “Longitudinal mixed-method study of learning gains: applying ABC framework” project funded by Higher Education Funding Council for England (HEFCE) as part of its wider work on learning gain (to find out more see the HEFCE website: http://www.hefce.ac.uk/lt/lg). The authors are also grateful for the excellent feedback from the reviewers.

Additional information

Funding

This work was supported by the Higher Education Funding Council for England [A longitudinal mixed-method study of learning gains: applying ABC framework at three institutions].