Dr. Siân Alsop is Research Fellow in the Centre for Global Learning, focusing on equity and attainment in HE. As co-investigator, she is developing a large corpus for the Story Choices project (funded by the EEF), which aims to understand best practice in early years reading. On the Access to Attainment project, she contributed to the development of a new measure for understanding difference in HE student outcomes based on ethnicity (funded by the SRHE). Using corpus linguistic techniques, she led language analysis on the Independent Diversity Review of the CILIP Carnegie and Kate Greenaway Awards (commissioned by CILIP), and also co-led a national survey on Library practice on the project Maximising the Diversity of Public Collections (commissioned by Libraries Connected). She is currently leading the development of a large corpus of feedback texts to investigate the relationship between written feedback, attainment and ethnicity.

Siân was previously a Lecturer in Academic Writing and has worked as Research Assistant on a number of projects relating to academic discourse and attainment disparity across disciplines, including the development of the British Academic Written English (BAWE). She was awarded a PhD by Coventry University in 2016 for her work on the pragmatic annotation of the Engineering Lecture Corpus (ELC).

Siân has significant experience of teaching in HE. She is currently Functional Lead for postgraduate taught provision within Global Learning, and runs the Centre's core PhD Reviewing Literature module. She has supervised three PhD theses to completion and is currently supervising 12 PhD theses (9 as Director of Studies), including joint studentships with Deakin University, in areas of feedback and assessment, academic language, attainment disparity and EDI in HE, and intersections between AI and student learning. Over the past fourteen years, she has successfully designed and delivered undergraduate and postgraduate across levels and disciplines of academic writing, linguistics, and education. 

education, attainment, equity, corpus linguistics, academic writing, language of lectures, data visualisation, computational methods

As a sector, we know that not all students have equal opportunity to succeed in their academic careers, which poses a problem for our students, for academic institutions, and for wider society. I work with large bodies of language data to understand attainment disparities among different groups of students. Through my research and classroom practice I want to contribute to our knowledge of the nature of these disparities, and to find effective ways to address them.