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

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.