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

Studies in Higher Education

Volumn 43, Issue 9, 2018, Pages 1586-1596

  Langan, A M ^a   Harris, W E ^a   Barrett, N ^a   Hamshire, C ^a   Wibberley, C ^a  

^a MANCHESTER METROPOLITAN UNIVERSITY   (United Kingdom)

Author keywords

factor selection; machine learning; Metric benchmarks; RandomForest; sensitivity

Indexed keywords

EID: 85006907292     PISSN: 03075079     EISSN: 1470174X     Source Type: Journal    
DOI: 10.1080/03075079.2016.1266613     Document Type: Article

References (35)

1. Agasisti, T., and F., Bonomi. 2014. “Benchmarking Universities’ Efficiency Indicators in the Presence of Internal Heterogeneity.” Studies in Higher Education 39: 1237–55. doi: 10.1080/03075079.2013.801423
2. Asif, M., 2015. “Determining Improvement Needs in Higher Education Benchmarking.” Benchmarking: An International Journal 22: 56–74. doi: 10.1108/BIJ-02-2013-0025
3. BIS. 2016. Success as a Knowledge Economy: Teaching Excellence, Social Mobility and Student Choice. London: Department for Business, Innovation and Skills. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/523546/bis-16-265-success-as-a-knowledge-economy-web.pdf
4. Breiman, L., 2001. “Random Forests.” Machine Learning 45: 5–32. doi: 10.1023/A:1010933404324
5. Camp, R. C., 1989. Benchmarking: The Search for Industry Best Practices that Lead to Superior Performance. Madison: Quality Press
6. De Toni, A., and S., Tonchia. 2001. “Performance Measurement Systems-models, Characteristics and Measures.” International Journal of Operations and Production Management 21: 46–71. doi: 10.1108/01443570110358459
7. Draper, D., and M., Gittoes. 2004. “Statistical Analysis of Performance Indicators in UK Higher Education.” Journal of the Royal Statistical Society Series A (Statistics in Society) 167: 449–74. doi: 10.1111/j.1467-985X.2004.apm12.x
8. Garengo, P., S., Biazzo, and U. S., Bititci. 2005. “Performance Measurement Systems in SMEs: A Review for a Research Agenda.” International Journal of Management Reviews 7: 25–47. doi: 10.1111/j.1468-2370.2005.00105.x
9. Genuer, R., J. M., Poggi, and C., Tuleau-Malot. 2010. “Variable Selection Using Random Forests.” Pattern Recognition Letters 31: 2225–36. doi: 10.1016/j.patrec.2010.03.014
10. Grömping, U., 2009. “Variable Importance Assessment in Regression: Linear Regression Versus Random Forest.” The American Statistician 63: 308–19. doi: 10.1198/tast.2009.08199
11. Hall, M. A., and G., Holmes. 2003. “Benchmarking Attribute Selection Techniques for Discrete Class Data Mining.” IEEE Transactions on Knowledge and Data Engineering 15: 1437–47. doi: 10.1109/TKDE.2003.1245283
12. Hamshire, C., T., Willgoss, and C., Wibberley. 2012. “The Placement was Probably the Tipping Point’–The Narratives of Recently Discontinued Students.” Nurse Education in Practice 12: 182–6. doi: 10.1016/j.nepr.2011.11.004
13. Hamshire, C., T., Willgoss, and C., Wibberley. 2013. “Should I Stay or Should I Go? A Study Exploring Why Healthcare Students Consider Leaving Their Programme.” Nurse Education Today 33: 889–95. doi: 10.1016/j.nedt.2012.08.013
14. Hardman, J., A., Paucar-Caceres, and A., Fielding. 2013. “Predicting Students’ Progression in Higher Education by Using the Random Forest Algorithm.” Systems Research and Behavioral Science 30: 194–203. doi: 10.1002/sres.2130
15. Hazelkorn, E., 2015. Rankings and the Reshaping of Higher Education: The Battle for World Class Excellence. Basingstoke: Palgrave Macmillan
16. HESA. 2011. International Benchmarking in UK Higher Education. Higher Education Statistics Agency Report. https://benchmarking.hesa.ac.uk/wp-content/uploads/2011/10/HESA_International_Benchmarking_report.pdf
17. Jackson, N., and H., Lund. 2000. Benchmarking for Higher Education. Milton Keynes: Open University Press
18. Kumar, S., and C., Chandra. 2001. “Enhancing the Effectiveness of Benchmarking in Manufacturing Organizations.” Industrial Management and Data Systems 101: 80–89. doi: 10.1108/02635570110384357
19. Kyrö, P., 2003. “Revising the Concept and Forms of Benchmarking.” Benchmarking: An International Journal 10: 210–25. doi: 10.1108/14635770310477753
20. Leman, J., 2011. “Benchmarking and the NSS.” Presentation at the Higher Education Surveys for Enhancement Conference 2011. https://www.heacademy.ac.uk/resource/benchmarking-and-nss
21. Liaw, A., and M., Wiener. 2002. “Classification and Regression by Random Forest.” R News 2: 18–22
22. Nonis, S. A., and D., Wright. 2003. “Moderating Effects of Achievement Striving and Situational Optimism on the Relationship between Ability and Performance Outcomes of College Students.” Research in Higher Education 44: 327–46. doi: 10.1023/A:1023029815944
23. Northcott, D., and S., Llewellyn. 2005. “Benchmarking in UK Health: A Gap Between Policy and Practice?” Benchmarking: An International Journal 12: 419–35. doi: 10.1108/14635770510619357
24. Partovi, F. Y., 1994. “Determining What to Benchmark: An Analytic Hierarchy Process Approach.” International Journal of Operations and Production Management 14: 25–39. doi: 10.1108/01443579410062068
25. Prasad, A. M., L. R., Iverson, and A., Liaw. 2006. “Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction.” Ecosystems 9: 181–99. doi: 10.1007/s10021-005-0054-1
26. Rokach, L., and O., Maimon. 2014. “Data Mining with Decision Trees: Theory and Applications.” World Scientific. Series in Machine Perception and Artificial Intelligence 69. http://www.worldscientific.com/worldscibooks/10.1142/6604
27. Saisana, M., B., d’Hombres, and A., Saltelli. 2011. “Rickety Numbers: Volatility of University Rankings and Policy Implications.” Research Policy 40: 165–77. doi: 10.1016/j.respol.2010.09.003
28. Shober, A. F., 2013. “Debate: Benchmarking Inequality–Driving Education Progress in the USA.” Public Money and Management 33: 242–4. doi: 10.1080/09540962.2013.799800
29. Stapenhurst, T., 2009. The Benchmarking Book. Oxon: Routledge
30. Strobl, C., J., Malley, and G., Tutz. 2009. “An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests.” Psychological Methods 14: 323–48. doi: 10.1037/a0016973
31. Tam, M., 2001. “Measuring Quality and Performance in Higher Education.” Quality in Higher Education 7: 47–54. doi: 10.1080/13538320120045076
32. Tillema, S., 2007. “Public Sector Organisations’ Use of Benchmarking Information for Performance Improvement: Theoretical Analysis and Explorative Case Studies in Dutch Water Boards.” Public Performance and Management Review 30: 496–520. doi: 10.2753/PMR1530-9576300402
33. Williams, R., and G., de Rassenfosse. 2016. “Pitfalls in Aggregating Performance Measures in Higher Education.” Studies in Higher Education 41: 51–62. doi: 10.1080/03075079.2014.914912
34. Yorke, M., and B., Longden. 2004. Retention and Student Success in Higher Education. Maidenhead: Society for Research in Higher Education/Open University Press
35. Zairi, M., 1998. Effective Management of Benchmarking Projects. Oxford: Butterworth Heinemann