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Journals / Kuram ve Uygulamada Eğitim Bilimleri / 2016 / Cilt: 16 - Sayı: 3

Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers' Academic Success upon Entering Graduate Education

Elif BAHADIR
Pages
943–964
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The successful prediction rate of the back-propagation neural network (BPNN, or a common type of ANN was 93.02%, while the success of prediction of LRA was 90.75%

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution.
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Key Findings & Data:A sample of 372 student profiles was used to train and test our model.
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Conclusion & Impact:The successful prediction rate of the back-propagation neural network (BPNN, or a common type of ANN was 93.02%, while the success of prediction of LRA was 90.75%
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution. In this study, we present the results of an experimental comparison study of Logistic Regression Analysis (LRA) and Artificial Neural Network (ANN) for predicting prospective mathematics teachers' academic success when they enter graduate education. A sample of 372 student profiles was used to train and test our model. The strength of the model can be measured through Logistic Regression Analysis (LRA). The average correct success rate of students for ANN was higher than LRA. The successful prediction rate of the back-propagation neural network (BPNN, or a common type of ANN was 93.02%, while the success of prediction of LRA was 90.75%

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Cite This Article

BAHADIR, E. (2022). Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers' Academic Success upon Entering Graduate Education. Kuram ve Uygulamada Eğitim Bilimleri, Cilt: 16 - Sayı: 3, 943–964. https://ajindex.com/articles/576726

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References (13)

  1. Agresti, A. (1990).
  2. Categorical data analysis. New York, NY: J.VV Iley & Sons. Baird, L. L. (1975). Comparative prediction of first year graduate and professional school grades in six fields. Educational and Psychological Measurement, 35(4), 941-946.
  3. Bresfelean, V. P., Bresfelean, M., Ghisoiu, N., & Comes, C. A. (2008, June). Determining students' academic failure profile founded on data mining methods. In Information Technology Interfaces, 2008 30th International Conference on (pp. 317-322).
  4. IEEE. Carter, V., & Good, E. (1973). Dictionary of education (4th ed.). New York, NY: McGraw Hill Book. Coit, D. W., Jackson, B. T., & Smith, A. E. (1998). Static neural network process models: Considerations and case studies. International Journal of Production Research, 36(11), 2953-2967.
  5. Drecko, R. F., & Wounderberg, H. W. (1977). MBA admission criteria and academic success. Decision Sciences, 8, 765-769.
  6. Flitman, A. M. (1997). Towards analysing student failures: Neural networks compared with regression analysis and multiple discriminant analysis. Computers & Operations Research, 24(4), 341-347.
  7. French, B. F., Immekus, J. C., & Oakes, W. C. (2005). An examination of indicators of engineering students' success and persistence. Journal of Engineering Education, 94(4), 419-425.
  8. Gayle, J. B., & Jones, T. H. (1973). Admission standards for graduate study in management. Decision Sciences, July, 421-425.
  9. Hardgrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994). Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research, 21(3), 249-263. Explorations in the microstructures of cognition (Vol. 1). Cambridge, MA: MIT Press.
  10. Schaeffers, K. G., Epperson, D. L., & Nauta, M. M. (1997). Women's career development: Can theoretically derived variables predict persistence in engineering majors? Journal of Counseling Psychology, 44, 173-183.
  11. 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. http://dx.doi.org/10.1080/08832320903449477
  12. Schwan, E. S. (1988). MBA admissions criteria: An empirical investigation and validation study. Journal of Education for Business, 63(4), 158-162.
  13. Siraj, F., & Abdoulha, M. A. (2009, May). Uncovering hidden information within university's student enrollment data using data mining. In 2009 Third Asia Internat