Higher Education

, Volume 68, Issue 5, pp 653–668 | Cite as

A new indicator for higher education student performance

  • Adelfio Giada
  • Boscaino Giovanni
  • Capursi Vincenza


The debate on academic achievement is a heated issue that involves all the higher education contexts. This paper attempts to provide an indicator that can make the measurement of university student performance easier and that can be easily applied to different systems, making comparisons more fair. The Italian University System is used as a starting point to make several considerations on the current measures and to build up a new performance indicator. Then, a generalization for other marking systems is shown and finally a quantile regression is performed to investigate some determinants of the new performance indicator, also with respect to the current one.


GPA Measurement of educational path Credits and marks Quantile regression 


  1. Adelfio, G., & Boscaino, G. (2013). The student talent in a random effects Quantile Regression Model for university performance. In V. M. R. Muggeo, V. Capursi, G. Boscaino, & G. Lovison (Eds). Proceedings of the 28th international workshop on statistical modelling (Vol. 2, pp. 479–483). Istituto Poligrafico Europeo Casa editrice.Google Scholar
  2. Agasisti, T., & Dal Bianco, A. (2009). Reforming the university sector: Effects on teaching efficiency—evidence from Italy. Higher education, (Vol. 57, pp. 477–498). Springer Science.Google Scholar
  3. Attanasio, M., Boscaino, G., Capursi, V., & Plaia, A. (2013). May the students’ career performance helpful in predicting an increase in universities income? In P. Giudici, S. Ingrassia, & M. Vichi (Eds.), Statistical models for data analysis, series in studies in classification, data analysis, and knowledge organization (pp. 9–16). Switzerland: Springer International Publishing.Google Scholar
  4. Baker, S. (2011). ’Two tribes’ to the wall? Elite set may adopt GPA. At the heart of the higher education debate. http://www.timeshighereducation.co.uk/416582.article. Accessed 1 November 2013
  5. Biggs, J., & Tang, C. (2007). Teaching for quality learning at university (3rd ed.). Berkshire: McGraw Hill, Society for Research into Higher Education & Open University Press.Google Scholar
  6. Chansky, N. M. (1964). A note on the grade point average in research. Educational and Psychological Measurement, 24, 95–99.CrossRefGoogle Scholar
  7. ECTS (2009). ECTS user’s guide. ISBN: 978-92-79-09728-7. http://ec.europa.eu/education/lifelong-learning-policy/doc/ects/guide_en.pdf. Accessed November 1, 2013
  8. Johnson, V. E. (1997). An alternative to traditional GPA for evaluating student performance. Statistical Science, 12(4), 251–278.CrossRefGoogle Scholar
  9. Hunt, D. (2001). Grade Point Average. Discussion paper. Imperial College Union, https://www.imperialcollegeunion.org/your-union/how-were-run/committees/12-13/Union_Council/file/2016. Accessed 1 November 2013
  10. Koenker, R. (2005). Quantile regression. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  11. Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33–50.CrossRefGoogle Scholar
  12. Jamelske, E. (2009). Measuring the impact of a university first-year experience program on student GPA and retention. Higher Education, 57, 373–391.CrossRefGoogle Scholar
  13. Koenker, R., & d’Orey, V. (1987). Computing regression quantiles. Applied Statistics, 36, 383–393.CrossRefGoogle Scholar
  14. Koenker, R., & d’Orey, V. (1994). Remark AS R92. A remark on algorithm AS 229: Computing dual regression quantiles and regression rank scores. Applied Statistics, 43, 410–414.CrossRefGoogle Scholar
  15. Koenker, R. (1994). Confidence intervals for regression quantiles. In: M. P, & M. Huskova (Eds.), Asymptotic statistics: Proceedings of the 5th Prague symposium on asymptotic statistics (pp. 349–359). Heidleberg: Physica-Verlag.Google Scholar
  16. Koenker, R. (2012). quantreg: Quantile Regression. R package version 4.94. http://CRAN.R-project.org/package=quantreg
  17. Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91, 74–89.CrossRefGoogle Scholar
  18. Light, G., Cox, R., & Calkins, S. (2009). Learning and teaching in higher education: The reflective professional (2nd ed.). Beverley Hills, CA: Sage.Google Scholar
  19. R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
  20. Rugg, H. O. (1918). Teachers’ marks and the reconstruction of the marking system. The Elementary School Journal, 18, 701–719.CrossRefGoogle Scholar
  21. Sadler, D. R. (2013). The futility of attempting to codify academic achievement standards. Higher Education,. doi:10.1007/s10734-013-9649-1.Google Scholar
  22. Smith, E., & Coombe, K. (2006). Quality and qualms in the marking of university assignments by sessional staff: An exploratory study. Higher Education, 51, 45–69.CrossRefGoogle Scholar
  23. Soh, K. C. (2011). Grade point average: What’s wrong and what’s the alternative? Journal of Higher Education Policy and Management, 33(1), 27–36.CrossRefGoogle Scholar
  24. Wintre, M. G., Dilouya, B., Pancer, S. M., Pratt, M. W., Birnie-Lefcovitch, S., & Polivy, J., et al. (2011). Academic achievement in first-year university: Who maintains their high school average? Higher Education, 62, 467–481.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Adelfio Giada
    • 1
  • Boscaino Giovanni
    • 1
  • Capursi Vincenza
    • 1
  1. 1.Dipartimento di Scienze Economiche, Aziendali e StatisticheUniversity of PalermoPalermoItaly

Personalised recommendations