Enhancing Assessments Review (EAR) Team (Faculty of Biology, Medicine and Health)

The Enhancing Assessments Review (EAR) Team: Jen Mcbride, Sally Hickson and Nicky High.

Review assessments across a programme, consult with colleagues and students, and make recommendations for enhancement back to the programme and unit teams   Recent developments in assessment and feedback—such as the rise of generative AI and a growing emphasis on authentic and inclusive assessment—have led some academics to adapt their practices. However, changes to individual units often overlook the broader student journey across a programme, can result in over-assessment (impacting both student- and staff-workloads), assessment bottlenecks, and/or a preponderance of a particular assessment type. Indeed, large units often default to multiple-choice or short-answer examinations to reduce marking burden, yet a variety of assessment types is essential for students to have meaningful skills development. All of this can impact negatively on student satisfaction and contribute to widening awarding-gaps. However, designing innovative and effective assessments is time-consuming and staff indicate that, in addition to time, they lack sufficient expertise to do this effectively.

To tackle these challenges, we developed the “Enhancing Assessment Review” (EAR) process. This collaborative initiative involves a team of colleagues reviewing assessment and feedback across programmes and offering enhancement recommendations. The process is highly consultative, engaging programme directors, unit coordinators, professional services staff, and students. To date, the EAR framework has led to the removal or consolidation of 101 summative assessments—saving over 2 million student-written words and around 506 hours of staff marking annually. Changes align with student priorities and plans to reduce awarding gaps: fewer high-stakes exams, more coursework and skills-based tasks, and greater assessment variety. Moreover, the EAR process supports improved assessment design by promoting authenticity, optionality, reduced deadline clustering, and GenAI-aware approaches.