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Benchmarking factor selection and sensitivity: a case study with nursing courses

Langan, A. M.; Harris, W. E.; Barrett, N.; Hamshire, C.; Wibberley, C.

Authors

A. M. Langan

W. E. Harris

N. Barrett

C. Hamshire

C. Wibberley



Abstract

There is an increasing requirement in higher education (HE) worldwide to deliver excellence. Benchmarking is widely used for this purpose, but methodological approaches to the creation of benchmark metrics vary greatly. Approaches require selection of factors for inclusion and subsequent calculation of benchmarks for comparison. We describe an approach using machine learning to select input factors based on their value to predict completion rates of nursing courses. Data from over 36,000 students, from nine institutions over three years were included and weighted averages provided a dynamic baseline for year on year and within year comparisons between institutions. Anonymised outcomes highlight the variation in benchmarked performances between institutions and we demonstrate the value of accompanying sensitivity analyses. Our methods are appropriate worldwide, for many forms of data and at multiple scales of enquiry. We discuss our results in the context of HE management, highlighting the value of scrutinising benchmark calculations.

Citation

Langan, A. M., Harris, W. E., Barrett, N., Hamshire, C., & Wibberley, C. (2018). Benchmarking factor selection and sensitivity: a case study with nursing courses. Studies in Higher Education, 43(9), 1586-1596. https://doi.org/10.1080/03075079.2016.1266613

Journal Article Type Article
Online Publication Date Dec 22, 2016
Publication Date Sep 2, 2018
Deposit Date May 29, 2024
Journal Studies in Higher Education
Print ISSN 0307-5079
Electronic ISSN 1470-174X
Publisher Routledge
Peer Reviewed Peer Reviewed
Volume 43
Issue 9
Pages 1586-1596
DOI https://doi.org/10.1080/03075079.2016.1266613
Keywords Education