179
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Machine learning classifiers do not improve the prediction of academic risk: Evidence from Australia

&

References

  • Bamber, D. 1975. “The Area above the Ordinal Dominance Graph and the Area below the Receiver Operating Characteristic Graph.” Journal of Mathematical Psychology 12 (4):387–415. doi: 10.1016/0022-2496(75)90001-2.
  • Barro, R. J. 1991. “Economic Growth in a Cross Section of Countries.” The Quarterly Journal of Economics 106 (2):407–43. doi: 10.2307/2937943.
  • Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24 (2):123–40. doi: 10.1007/BF00058655.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1):5–32. doi: 10.1023/A:1010933404324.
  • Breiman, L. 2017. Classification and Regression Trees. New York: Routledge. doi: 10.1201/9781315139470.
  • Cobb-Clark, D. A., and T.-H. Nguyen. 2012. “Educational Attainment Across Generations: The Role of Immigration Background.” Economic Record 88 (283):554–75. doi: 10.1111/1475-4932.12001.
  • Cortez, P., and A. M. G. Silva. 2008. “Using Data Mining to Predict Secondary School Student Performance.” http://hdl.handle.net/1822/8024.
  • DeLong, E. R., D. M. DeLong, and D. L. Clarke-Pearson. 1988. “Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach.” Biometrics 44 (3):837–45. doi: 10.2307/2531595.
  • Ford, M. 2013. “Achievement Gaps in Australia: What NAPLAN Reveals about Education Inequality in Australia.” Race Ethnicity and Education 16 (1):80–102. doi: 10.1080/13613324.2011.645570.
  • Friedman, J., T. Hastie, and R. Tibshirani. 2009. The Elements of Statistical Learning. 2nd ed. New York, NY: Springer. doi: 10.1007/978-0-387-84858-7.
  • Gonski, D., T. Arcus, K. Boston, V. Gould, W. Johnson, L. O’Brien, and M. Roberts. 2018. Through Growth to Achievement: Report of the Review to Achieve Educational Excellence in Australian Schools. Canberra: Department of Education, Employment and Workplace Relations. docs.education.gov.au/.
  • Gray, G., C. McGuinness, and P. Owende. 2013. “An Investigation of Psychometric Measures for Modelling Academic Performance in Tertiary Education.” In Educational Data Mining 2013.
  • Hoerl, A. E., and R. W. Kennard. 1970. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12 (1):55–67. doi: 10.1080/00401706.1970.10488634.
  • Huang, S., and N. Fang. 2013. “Predicting Student Academic Performance in an Engineering Dynamics Course: A Comparison of Four Types of Predictive Mathematical Models.” Computers & Education 61:133–45. doi: 10.1016/j.compedu.2012.08.015.
  • Jishan, S. T., R. I. Rashu, N. Haque, and R. M. Rahman. 2015. “Improving Accuracy of Students’ Final Grade Prediction Model Using Optimal Equal Width Binning and Synthetic Minority Over-Sampling Technique.” Decision Analytics 2 (1):1. doi: 10.1186/s40165-014-0010-2.
  • Kotsiantis, S. B. 2012. “Use of Machine Learning Techniques for Educational Proposes: A Decision Support System for Forecasting Students’ Grades.” Artificial Intelligence Review 37 (4):331–44. doi: 10.1007/s10462-011-9234-x.
  • Kotsiantis, S., C. Pierrakeas, and P. Pintelas. 2004. “Predicting Students’ Performance in Distance Learning Using Machine Learning Techniques.” Applied Artificial Intelligence 18 (5):411–26. doi: 10.1080/08839510490442058.
  • Li, J., and Y.-M. Jia. 2010. “An Improved Elastic Net for Cancer Classification and Gene Selection.” Acta Automatica Sinica 36 (7):976–81. doi: 10.1016/S1874-1029(09)60042-2.
  • Li, J., Y. Jia, and Z. Zhao. 2013. “Partly Adaptive Elastic Net and Its Application to Microarray Classification.” Neural Computing and Applications 22 (6):1193–200. doi: 10.1007/s00521-012-0885-6.
  • Nicoletti, C., and B. Rabe. 2013. “Inequality in Pupils’ Test Scores: How Much Do Family, Sibling Type and Neighbourhood Matter?” Economica 80 (318):197–218.
  • Noether, G. E. 1967. “Elements of Nonparametric Statistics.” Tech. Rep., Wiley.
  • Reback, R. 2008. “Teaching to the Rating: School Accountability and the Distribution of Student Achievement.” Journal of Public Economics 92 (5):1394–415. doi: 10.1016/j.jpubeco.2007.05.003.
  • Romero, C., and S. Ventura. 2007. “Educational Data Mining: A Survey from 1995 to 2005.” Expert Systems with Applications 33 (1):135–46. doi: 10.1016/j.eswa.2006.04.005.
  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1985. “Learning Internal Representations by Error Propagation.” Tech. Rep., California Univ San Diego La Jolla Inst for Cognitive Science.
  • Shingari, I., D. Kumar, and M. Khetan. 2017. “A Review of Applications of Data Mining Techniques for Prediction of Students’ Performance in Higher Education.” Journal of Statistics and Management Systems 20 (4):713–22. doi: 10.1080/09720510.2017.1395191.
  • Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research 15:1929–58.
  • Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society: Series B (Methodological) 58 (1):267–88. doi: 10.1111/j.2517-6161.1996.tb02080.x.
  • Vandamme, J., N. Meskens, and J. Superby. 2007. “Predicting Academic Performance by Data Mining Methods.” Education Economics 15 (4):405–19. doi: 10.1080/09645290701409939.
  • Wan, L., M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus. 2013. “Regularization of Neural Networks Using Dropconnect.” In Proceedings of the 30th International Conference on Machine Learning, edited by S. Dasgupta and D. McAllester, vol. 28, 1058–66. Atlanta, GA: PMLR. http://proceedings.mlr.press/v28/wan13.html.
  • Yadav, S. K., and S. Pal. 2012. “Data Mining: A Prediction for Performance Improvement of Engineering Students Using Classification.” arXiv preprint arXiv:1203.3832.
  • Zou, H., and T. Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2):301–20. doi: 10.1111/j.1467-9868.2005.00503.x.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.