摘要：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.
