Logistic Regression in Determining Affecting Factors Student Success in an Introductory Statistics Subject
Keywords:
accuracy, best fit model, logistic regression, prediction, subjectAbstract
This study aims to find the best model for predicting students’ success based on a binary logistic regression. This analysis was also used to determine the factor that affects student success in StatisticsSubjects. Five different data partitioning sets were used. The results indicate that the data with a partitioning set of 70% for the estimation set and 30% for the evaluation set is the best fit model using six independent variables. The predictors under investigation were assessment achievements such as test 1, test 2, quiz, assignment, group project, and final test marks. The outcome showed a significant difference in test 2 and the final test marks in determining the factor affecting the subject's result.Besides, the overall model explained further that 95.8% of the sample was classified correctly. This study was carried out using SPSS software and excel. In order to determine the significant variables,further research can be done using the linear regression analysis method.
References
Adnan, N. I. M., Wahid, S. N. S., Ujang, S., Yacob, N. A., & Zaini, A. A. (2021). Open And Distance Learning Preparedness Factors Among Academicians In Uitm (Pahang) Using Logistic Regression. AIP Conference Proceedings, 2355(May).
https://doi.org/10.1063/5.0053194 Alija, S. (2015).
Application of ordinal logistic regression in the study of students ’ achievement in external testing. Bulletin of the Transilvania University of Brasov, 8(57), 1–6. Baars, G. J. A., Stijnen, T., & Splinter, T. A. W. (2017).
A model to predict student failure in the first year of the undergraduate medical curriculum. Health Professions Education, 3(1), 5–14. https://doi.org/10.1016/j.hpe.2017.01.001 Chatterjee, S., & Hadi, A. S. (2006).
Regression analysis bye example: fourth edition. In Regression Analysis by Example: Fourth Edition. https://doi.org/10.1002/0470055464 Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013).
Applied Logistic regression. In Wiley Series in Probability and Statistics Wiley series in probability and statistics: Vol. 3rd ed. http://search.lib.virginia.edu/catalog/ocn830163779 Hu, S., & Hu, Y. (2021).
Research on the influence of students gender on students examination scores based on logistic regression model. Journal of Physics: Conference Series, 1955(1). https://doi.org/10.1088/1742-6596/1955/1/012112 Jeslet, D. S., Komarasamy, D., & Hermina, J. J. (2021).
Student result prediction in Covid-19 lockdown using machine learning techniques. Journal of Physics: Conference Series, 1911(1). https://doi.org/10.1088/1742-6596/1911/1/012008 Manieri, E., de Lima, M., & Ghosal, N. (2015).
Testing for success: A logistic regression analysis to determine which pre-admission exam best predicts success in an associate degree in nursing program. Teaching and Learning in Nursing, 10(1), 25–29. https://doi.org/10.1016/j.teln.2014.08.001 Nagelkerke, N. J. D. (1991).
A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691–692. https://doi.org/10.1093/biomet/78.3.691 Shedriko. (2021).
Binary logistic regression in determining affecting factors student graduation in a subject. Jurnal Teknologi dan Open Source, 4(1), 114–120. https://doi.org/10.36378/jtos.v4i1.1401 Suliman, N. A., Abidin, B., Manan, N. A., & Razali, A. M. (2014).
Predicting students’ success at preuniversity studies using linear and logistic regressions. AIP Conference Proceedings, 1613(Soric 2013), 306–316. https://doi.org/10.1063/1.4894355 Wambuguh, O., & Yonn-Brown, T. (2013).
Regular lecture quizzes scores as predictors of final examination performance: A test of hypothesis using logistic regression analysis. International Journal for the Scholarship of Teaching and Learning, 7(1), 1-10. https://doi.org/10.20429/ijsotl.2013.070107 Wang, J., Ge, L., Li, F., Liu, X., Zeng, G., & He, X. (2021).
Analysis of influencing factors and teaching reform of nuclear professional English based on logistic regression. Journal of Physics: Conference Series, 1774(1). https://doi.org/10.1088/1742-6596/1774/1/012023 Wanvarie, S., & Sathapatayavongs, B. (2007).
Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure. Annals of the Academy of Medicine Singapore, 36(9), 770–773.
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