Understanding how our assessment contributes to retention and awarding gaps for black students on LHCS modules

Project leader(s): Jane LoughlinDuncan BanksEleanor Crabb
Theme: Access, Participation and Success
Faculty: STEM
Status: Current
Dates: April 2021 to December 2023

The aim of this project is to investigate factors contributing to the retention and awarding gaps observed for black students (and other minority ethnic groups) on LHCS modules, with a focus on assessment design and module assessment strategy. The observation that a gap begins to appear after TMA01 submission, suggests that the experience of the assessment could be a key contributing factor. Analysis of individual module data over the period 2016/19, reveals marked awarding gaps in LHCS modules at all UG levels, particularly for the core Biology modules. Furthermore, reduction in awarding gaps in 2019/20, when some modules had exams cancelled or switched to remote exams as a consequence of the covid pandemic, further suggests that assessment design is a key factor.

This project will seek to identify and investigate correlations between retention and awarding gaps in LHCS modules with key features of the module assessment and student demographic data.

Based on these analyses, focus modules will be identified for more detailed investigation using real-time surveys to obtain data on students’ perceptions of their assessment and analysis of performance at the level of individual question types.

This project has been planned alongside and will complement another project focussing on Level 1 modules studied by LHCS students (Understanding awarding gaps for disabled and black LHCS students at Level 1). It should also be informed by any assessment-related findings of the University’s inclusive curriculum project, as that progresses. Completion of the project outlined here could form the basis for numerous subsequent studies including analysis of language used in assessments and how this impacts student understanding and performance, and development of assessment models and practices that help to ensure equitable outcomes for all our students.

The module-level analysis proposed here is much more in-depth than that routinely required of module teams for QME processes and should provide a valuable dataset that module teams can draw on and use as a basis for developing targeted interventions in their modules.