What drives postgraduate awarding gaps? Modelling master’s degree outcomes

There is a concern across higher education that educational outcomes vary across a range of student factors. More research is needed, particularly at postgraduate level, to better understand the key effects of various factors on student outcomes.

Underpinned by Astin’s input-environment-outcome conceptual model for assessment, this paper uses student data (n > 6,000) over recent years from a single UK higher education institution and multilevel ordinal logistic regression to model key influences on the likelihood of particular postgraduate taught (PGT) outcomes (in three categories: pass/merit/distinction). Predictors include undergraduate attainment, and a range of candidate characteristics such as student ethnicity, sex, age, department, undergraduate performance.

We find that there is a strong relationship between undergraduate and postgraduate outcomes, and that department and student ethnicity are relatively small but important factors influencing the likelihood of particular PGT outcomes. The key finding is that important gaps in PGT outcomes remain when a range of factors are included in the analysis – conceptually in both ‘inputs’ and ‘environment’ as per Astin. Institutions need to carefully take account of these patterns of attainment when developing policy to enhance equality in PGT education. This research can help inform more focused policies aimed at closing attainment ‘gaps’ over time.