How is good feedback expressed? A corpus analysis of feedback provided by Edinburgh University Students' Association Teaching Awards (Best Feedback) Nominees Team Members: Derek Jones, Tanya Lubicz-Nawrocka, Tim Fawns, Gill Aitken, Tamara Mulherin School: Edinburgh Medical School (Centre for Medical Education) Abstract The quality of assessment and feedback is of national and international concern to higher education institutions. Despite direction on the content of feedback there remains a high degree of flexibility in how feedback is conveyed. By looking at the uses of language in feedback we can provide guidance on how to emphasise dialogic aspects and, in doing so, enhance student satisfaction and performance. Our aim is to undertake a pilot study to explore dialogic and meta-discursive elements evident in feedback given by tutors nominated by students for an Edinburgh University Student’s Association 2016/17 ‘Best Feedback’ award in order to enhance best practice guidance. A Research Assistant will be employed to undertake a corpus analysis of undergraduate feedback examples provided by nominees. We will use the work of Hyatt (2005) and Hyland & Tse (2004) to construct a framework for the identification of patterns of language and language use. During analysis we will be alert to comment types not originally identified by Hyatt whose research was restricted to Master’s programmes located in Educational Studies. NVivo 10 will be used to aid coding and analysis. A report of the findings will be published on the Students’ Association website (with a small number of hard copies) and shared via the Teaching Matters blog. Findings may also be submitted for publication to relevant journals and presentation at conferences. Final project report Download the final project report (PDF) Other project outcomes “‘Tain’t what you do (it’s the way that you do it)”: Investigating feedback comments Teaching Matters blog post (July 2018), University of Edinburgh Lubicz-Nawrocka T and Bunting K (2019) Student perceptions of teaching excellence: an analysis of student-led teaching award nomination data Teaching in Higher Education 24 (1) p63-80 This article was published on 2024-02-26
