Research in Higher Education

, Volume 59, Issue 3, pp 382–400 | Cite as

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

  • Cindi Mason
  • Janet Twomey
  • David Wright
  • Lawrence Whitman
Article
First Online:
Received:

Abstract

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm relationships between student attributes and attrition. Methods of prediction have also been evaluated and compared. Utilizing the attributes found in previous studies to have correlation with student attrition, this study considers the results of three different prediction methods—logistic regression, a multi-layer perceptron artificial neural network, and a probabilistic neural network (PNN)—to predict engineering student retention at a case study university. The purpose of this study was to introduce the PNN to the study of engineering student retention prediction and compare the results of the PNN to other commonly used methods in this field of study. The accuracy, sensitivity, specificity and overall results for each method are reported, compared, and discussed as the major contribution of this paper.

Keywords

Student retention Probabilistic neural network Logistic regression Attrition 
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References

  1. Adeli, H., & Panakkat, A. (2009). A probabilistic neural network for earthquake magnitude prediction. Neural networks, 22(7), 1018–1024.CrossRefGoogle Scholar
  2. Alkhasawneh, R., & Hobson, R. (2011). Modeling student retention in science and engineering disciplines using neural networks. In 2011 IEEE global engineering education conference—learning environments and ecosystems in engineering education.Google Scholar
  3. Allen, J., Robbins, S. B., Casillas, A., & Oh, I. S. (2008). Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness. Research in Higher Education, 49(7), 647–664.CrossRefGoogle Scholar
  4. Azar, A. T., & El-Said, S. A. (2013). Probabilistic neural network for breast cancer classification. Neural Computing and Applications, 23(6), 1737–1751.CrossRefGoogle Scholar
  5. Barker, K., Trafalis, T., & Rhoads, T. R. (2004). Learning from student data. In Proceedings of the 2004 systems and information engineering design symposium.Google Scholar
  6. Besterfield-Sacre, M., Moreno, M., Shuman, L., & Atman, C. (2001). Gender and ethnicity differences in freshmen engineering attitudes: A cross-institutional study. Journal of Engineering Education, 9(4), 477–489.CrossRefGoogle Scholar
  7. Creamer, D. G. (1980). Educational advising for student retention: An institutional perspective. Community College Review, 7(4), 11–18.CrossRefGoogle Scholar
  8. Cripps, A. (1996). Using artificial neural nets to predict academic performance. In Proceedings of the 1996 ACM symposium on applied computing. ACM.Google Scholar
  9. Crockett, D. S. (1978). Academic advising: A cornerstone of student retention. New Directions for Student Services, 1978(3), 29–35.CrossRefGoogle Scholar
  10. Dahab, D. A., Ghoniemy, S. S. A., & Selim, G. M. (2012). Automated brain tumor detection and identification using image processing and probabilistic neural network techniques. International Journal of Image Processing and Visual Communication, 1(2), 1–8.Google Scholar
  11. Databytes. (2016). Retention range: The wide variation among 2007 freshmen. Connections Newsletter. Feb. 2016. American Society for Engineering Education (ASEE). http://createsend.com/t/y-45B6B3EF48CE7A3C.
  12. Felder, R., Felder, G., Mauney, M., Hamrin, C., & Dietz, E. (1995). A longitudinal study of engineering student performance and retention III. Gender differences in student performance and attitudes. Journal of Engineering Education, 84(2), 151–163.CrossRefGoogle Scholar
  13. Feldman, M. J. (1993). Factors associated with one-year retention in a community college. Research in Higher Education, 34(4), 503–512.CrossRefGoogle Scholar
  14. Fike, D. S., & Fike, R. (2008). Predictors of first-year student retention in the community college. Community College Review, 36(2), 68–88.CrossRefGoogle Scholar
  15. Grosset, J. (1991). Patterns of integration, commitment, and student characteristics and retention among younger and older students. Research in Higher Education, 32(2), 159–178.CrossRefGoogle Scholar
  16. Hartman, H., & Hartman, M. (2006). Leaving engineering: Lessons from Rowan University’s College of Engineering. Journal of Engineering Education, 95(1), 49–61.CrossRefGoogle Scholar
  17. Herndon, S. (1984). Recent findings concerning the relative importance of housing to student retention. Journal of College and University Student Housing, 14(1), 27–31.Google Scholar
  18. Herzog, S. (2005). Measuring determinants of student return vs. dropout/stopout vs. transfer: A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8), 883–928.CrossRefGoogle Scholar
  19. Herzog, S. (2006). Estimating student retention and degree-completion time: Decision trees and neural networks vis-à-vis regression. New Directions for Institutional Research, 131(1), 17–33.CrossRefGoogle Scholar
  20. Hochstein, S. K., & Butler, R. R. (1983). The effects of the composition of financial aids package on student retention. Journal of Student Financial Aid, 13(1), 21–26.Google Scholar
  21. Jaeger, A. J., & Hinz, D. (2009). The effects of part-time faculty on first semester freshmen retention: A predictive model using logistic regression. Journal of College Student Retention, 10(3), 265–286. Retrieved from http://search.proquest.com/docview/196719548?accountid=15042.
  22. Jin, Q., Imbrie, P. K., Lin, J. J. J., & Chen, X. (2011) A multi-outcome hybrid model for predicting student success in engineering. In American Society for Engineering Education.Google Scholar
  23. Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York: Springer.CrossRefGoogle Scholar
  24. Lin, J. J. J., Imbrie, P. K., & Reid, K. J. (2009). Student retention modelling: An evaluation of different methods and their impact on prediction results. In Research in engineering education symposium (pp. 1–6).Google Scholar
  25. Lin, H.-T., Liang, T.-J., & Chen, S.-M. (2013). Estimation of battery state of health using probabilistic neural network. IEEE Transactions on Industrial Informatics, 9(2), 679–685.CrossRefGoogle Scholar
  26. Litzler, E., & Young, J. (2012). Understanding the risk of attrition in undergraduate engineering: Results from the project to assess climate in engineering. Journal of Engineering Education, 101(2), 319–345.CrossRefGoogle Scholar
  27. Manpower Group. (2017). Focus on engineering 2017. Retrieved from http://www.experisjobs.us/Website-File-Pile/Whitepapers/Experis/engineering-whitepaper.pdf.
  28. Min, Y., Zhang, G., Long, R., Anderson, T., & Ohland, M. (2011). Nonparametric survival analysis of the loss rate of undergraduate engineering students. Journal of Engineering Education, 100(2), 349–373.CrossRefGoogle Scholar
  29. Moller-Wong, C., & Eide, A. (1997). An engineering student retention study. Journal of Engineering Education, 86(1), 7–15.CrossRefGoogle Scholar
  30. Morillo, D. S., & Gross, N. (2013). Probabilistic neural network approach for the detection of SAHS from overnight pulse oximetry. Medical & Biological Engineering & Computing, 51(3), 305–315.CrossRefGoogle Scholar
  31. Murtaugh, P. A., Burns, L. D., & Schuster, J. (1999). Predicting the retention of university students. Research in Higher Education, 40(3), 355–371.CrossRefGoogle Scholar
  32. National Student Clearinghouse©. (2016). Snapshot Report—Persistence and Retention. Research Center. Retrieved from https://nscresearchcenter.org/snapshotreport-persistenceretention22/.
  33. Office of Planning and Analysis (OPA). (2017). College Division 1st Year Freshmen Retention and Graduate Rates; Business Intelligence and Predictive Modeling (BIPM).Google Scholar
  34. Pastell, M. E., & Kujala, M. (2007). A probabilistic neural network model for lameness detection. Journal of Dairy Science, 90(5), 2283–2292.CrossRefGoogle Scholar
  35. President’s Committee of Advisors on Science and Technology, Panel on Educational Technology, Prepare and Inspire: K-12 Science, Technology, Engineering, and Math (STEM) Education for America’s Future, September 15, 2010.Google Scholar
  36. Robst, J., Keil, J., & Russo, D. (1998). The effect of gender composition of faculty on student retention. Economics of Education Review, 17(4), 429–439.CrossRefGoogle Scholar
  37. Rosati, P. (1993). Student retention from first-year engineering related to personality type. Frontiers in Education Conference (pp. 37–39). Washington, DC.Google Scholar
  38. Sankari, Z., & Adeli, H. (2011). Probabilistic neural networks for diagnosis of Alzheimer’s disease using conventional and wavelet coherence. Journal of Neuroscience Methods, 197(1), 165–170.CrossRefGoogle Scholar
  39. Saritha, M., Paul Joseph, K., & Mathew, A. T. (2013). Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognition Letters, 34(16), 2151–2156.CrossRefGoogle Scholar
  40. Schumacher, P., Olinsky, A., Quinn, J., & Smith, R. (2010). A comparison of logistic regression, neural networks, and classification trees predicting success of actuarial students. Journal of Education for Business, 85(5), 258–263. doi: 10.1080/08832320903449477.CrossRefGoogle Scholar
  41. Schwab, K., & Samans, R. (2016). The future of jobs. World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf.
  42. Singh, K. P., Gupta, S., & Rai, P. (2013). Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches. Toxicology and Applied Pharmacology, 272(2), 465–475.CrossRefGoogle Scholar
  43. Smith, R., & Schumacher, P. (2006). Academic attributes of college freshmen that lead to success in actuarial studies in a business college. Journal of Education for Business, 81(5), 256–260. ISSN: 08832323.Google Scholar
  44. Song, T., Jamshidi, M. M., Lee, R. R., & Huang, M. (2007). A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Transactions on Neural Networks, 18(5), 1424–1432.CrossRefGoogle Scholar
  45. Specht, F. (1990). Probabilistic neural networks. Neural Networks, 3(1), 109–118. doi: 10.1016/0893-6080(90)90049-Q.CrossRefGoogle Scholar
  46. Superby, J.-F., Vandamme, J. P., & Meskens, N. (2006). Determination of factors influencing the achievement of the first-year university students using data mining methods. In Workshop on educational data mining.Google Scholar
  47. Tyson, W. (2011). Modeling engineering degree attainment using high school and college physics and calculus coursetaking and achievement. Journal of Engineering Education, 100(4), 760–777.CrossRefGoogle Scholar
  48. Vandamme, J., Meskens, N., & Superby, J. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405–419. doi: 10.1080/09645290701409939.CrossRefGoogle Scholar
  49. Wang, J.-S., Chiang, W.-C., Hsu, Y.-L., & Yang, Y.-T. C. (2013). ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing, 116, 38–45.CrossRefGoogle Scholar
  50. Wilson, S. B., Mason, T. W., & Ewing, M. J. M. (1997). Evaluating the impact of receiving university-based counseling services on student retention. Journal of Counseling Psychology, 44(3), 316–320.CrossRefGoogle Scholar
  51. Wu, S. G., et al. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. In 2007 IEEE International Symposium on Signal processing and information technology. IEEE.Google Scholar
  52. Yu, S.-N., & Chen, Y.-H. (2007). Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters, 28(10), 1142–1150.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of EngineeringWichita State UniversityWichitaUSA
  2. 2.Donaghey College of Engineering and Information Technology (EIT)University of Arkansas at Little RockLittle RockUSA

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