Understanding Attainment Disparity: The Case for a Corpus-Driven Analysis of the Language used in Written Feedback Information to Students of Different Backgrounds

Abstract

Background: Disparity of attainment between different groups of students in UK higher education has been correlated with ethnicity (UUK & NUS, 2019). For example, students who declared their ethnicity as Black were 20% less likely to graduate with a top classification than those who declared their ethnicity as White (OfS, 2018a). The causes of such attainment gaps are complex, and one important factor may be the nature of the feedback given by academic staff on assignments written by different groups of students. This paper aims to explore the feasibility of investigating this hypothesis by analyzing written feedback and looking for patterns in feedback given to different groups of students.• Discussion: Although the data was insufficient to focus on ethnicity, intersectionality, and attainment, it is possible to differentiate the nature of the feedback-including engagement-based on contextual variables.• Conclusions: The positive results show that the framework (RQ1) and the methodology (RQ2) have been successfully developed for this limited dataset and merit application in the analysis of a larger data set to explore the complex relationships between WFI, ethnicity, and attainment.

Uses of Corpus Linguistics in Higher Education Research: An Adjustable Lens

“…Our taxonomy builds on previous systems by offering a more systematic and symmetrical approach to categorisation, whereby five main categories (advice, critique, observation, praise and query) branch three times according to aspect, orientation, and focus. This fairly complex branching structure comprises 60 functional annotation categories and an unclassified category, which allowed us to annotate with high reliability all text in two small pilot corpora (Alsop and Gardner 2019;Alsop, Gardner and Priestley 2020).…”

“…We have therefore included detailed information about, for example, students' prior educational experience, discipline, current grade, gender, and self-identified ethnicity. We have to date begun to identify correlations between language function and discipline (Alsop and Gardner 2019) and also language function, self-identified ethnicity and grade (Alsop, Gardner and Priestley 2020). Gauging the impact of this on higher education experience and outcomes will be done through triangulation with other qualitative methods, including focus groups with staff and with students.…”