Creating school-wide grading descriptors

Grading descriptors in higher education are essential tools that provide clear criteria for assessing student performance. They offer detailed descriptions of the standards expected for each grade, ensuring consistency, equity and transparency in evaluation (Grainger, Purnell and Zipf, 2008). This approach moves away from norm-referenced assessment, where students are compared to each other, towards criterion-referenced assessment, focusing on the quality of work and alignment with the intended learning outcomes of the assessment. Research by Sadler (2005) highlights the importance of these descriptors in enhancing the reliability and validity of assessments. It is also essential for providing students with clear feedback on how they can improve their work to achieve higher grades. Grading descriptors can be broad (at institutional level) or narrow (at assessment level) or somewhere in between. Most HEI’s in the UK have institutional level descriptors which are readily available on their websites. London Met does not presently have these. The Frameworks for Higher Education Qualifications (FHEQ) provides descriptors by level but does not specify what is expected for the grade classifications within each level, except for L6 where detailed descriptors are provided for a fail, 3rd class, 2:2, 2:1 and 1st class honours degree (The Quality Assurance Agency for Higher Education, 2024). These descriptors outline what is expected at the exit level only and is broad to the qualifications at each level. In 2021, the Southern England Consortium for Credit Accumulation and Transfer (SEEC) (2021) provided more focused descriptions, aligned with the FHEQ, detailing characteristics and context of learning at each level. They can be used to help develop learning outcomes, setting standards and expectations, informing curriculum design and importantly guiding assessment criteria. The aim of this work was to use the FHEQ and SEEC guidelines, to create a broad set of grading descriptors which could be used as a basis for the development of local subject or assessment descriptors. This work also experimented with the use of AI, along with the student voice, in creating a useful and robust set of descriptors.