Exploring student consensus about module-level ethnicity awarding gaps: a Delphi approach

Attention to awarding gaps in higher education linked to ethnicity tend to focus on outcomes at final award. Our project sought to scrutinise awarding gaps at module level where these gaps may emerge. Our aim is twofold: (1) to identify the most important barriers to student success and determine strategies to reduce awarding gaps at module level, as perceived by students from various ethnic backgrounds; and (2) to investigate to what extent there is consensus amongst students of various ethnic backgrounds regarding these barriers and strategies. We employed a two-phase Delphi approach. The first phase involved data analytics to identify modules with awarding gaps in Health and Life Sciences Undergraduate degree programmes. The second phase employed a Delphi approach to collect student feedback on barriers to success and strategies to overcome these barriers, focusing on culture, curriculum, and assessment. The study engaged 36 students in the first round and 53 in the second round. Our research confirmed the existence of awarding gaps at the module level. Students reached consensus on 55 out of 79 factors affecting their academic performance, with notable differences between White and racially minoritised student groups. This study suggests that to close awarding gaps, both a module-level approach and a deep commitment to listen to our students will be needed. Our findings offer actionable recommendations for embedding this understanding into existing university procedures, thereby fostering more equitable educational outcomes.

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Publication Details

Journal
Journal of Further and Higher Education
Published
2024-12-04
DOI
https://doi.org/10.1080/0309877x.2024.2428722
Primary Topic
Delphi Technique in Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring student consensus about module-level ethnicity awarding gaps: a Delphi approach

Kei Long Cheung, Hannah Froome, Nicholas Worsfold, Billy Wong et al.
Journal of Further and Higher Education
Delphi Technique in Research
article

Exploring student consensus about module-level ethnicity awarding gaps: a Delphi approach

Kei Long Cheung, Hannah Froome, Nicholas Worsfold, Billy Wong, Laura Hills, Daniel P. Bailey, Michael C. Thomas
article en

Abstract

Attention to awarding gaps in higher education linked to ethnicity tend to focus on outcomes at final award. Our project sought to scrutinise awarding gaps at module level where these gaps may emerge. Our aim is twofold: (1) to identify the most important barriers to student success and determine strategies to reduce awarding gaps at module level, as perceived by students from various ethnic backgrounds; and (2) to investigate to what extent there is consensus amongst students of various ethnic backgrounds regarding these barriers and strategies. We employed a two-phase Delphi approach. The first phase involved data analytics to identify modules with awarding gaps in Health and Life Sciences Undergraduate degree programmes. The second phase employed a Delphi approach to collect student feedback on barriers to success and strategies to overcome these barriers, focusing on culture, curriculum, and assessment. The study engaged 36 students in the first round and 53 in the second round. Our research confirmed the existence of awarding gaps at the module level. Students reached consensus on 55 out of 79 factors affecting their academic performance, with notable differences between White and racially minoritised student groups. This study suggests that to close awarding gaps, both a module-level approach and a deep commitment to listen to our students will be needed. Our findings offer actionable recommendations for embedding this understanding into existing university procedures, thereby fostering more equitable educational outcomes.

Journal of Further and Higher EducationVol. 49(1)
University of London (GB), Brunel University of London (GB), University of Reading (GB)
Brunel University London
Reduced inequalities
Openalex Percentile: Top 64%
Delphi Technique in Research
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