Using Logistic Regression Models and Artificial Neural Networks to Study the Factors Affecting the Academic Achievement of University Student
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1093Keywords:
Academic Achievement, Prediction Model, Ordinal Logistic Regression, Artificial Neural Networks, Multi-Layer Perceptron MLPAbstract
This study aimed to identify the most important factors affecting the academic achievement of a university student and then build a predictive model using these factors to predict the student's academic status. The ordinal logistic regression method was used to identify the influencing factors, and neural network technique to build the prediction model. It was applied to a sample of 188 students at Al-Yamamah University in the Kingdom of Saudi Arabia. The analyzed data were collected using a questionnaire of three main axes: representing student related factors, family, and academic factors. The results of the logistic regression model showed that 10 variables had a significant effect on the student's academic achievement: age, cumulative average at high school, the distance of residence, work besides studying, daily study hours, frequent absences, fear of exams, the economic level of the family, parents divorce, and family follow-up. As for the MLP network model with architecture (18-28-3), whose inputs were the significant variables chosen by the logistic model, the values of all performance indicators were supportive of the model's quality and predictive ability, where the correct classification rate values in the training data reached 96%, validity 93% and testing 90%. The results of the classification table were also high, with the correct classification of the three categories of the target variable: 100% good, 91.95% fair, 82.35% poor. The total percentage of correct classification was 94.68%. All these indicators confirm the validity and feasibility of using this model as an initial tool for predicting the academic status of a university student.
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References
[2] K. Gurney, An introduction to neural networks. Taylor & Francis e-Library, 2004.
[3] R. B. Sesay, M. Kpangay, and S. Seppeh, ‘An Ordinal Logistic Regression Model to Identify Factors Influencing Students Academic Performance at Njala University’, Int. J. Res. Sci. Innov., vol. 08, no. 01, pp. 91–100, 2021, doi: 10.51244/ijrsi.2021.8104.
[4] C. F. Rodríguez-Hernández, M. Musso, E. Kyndt, and E. Cascallar, ‘Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation’, Comput. Educ. Artif. Intell., vol. 2, no. March, p. 100018, 2021, doi: 10.1016/j.caeai.2021.100018.
[5] Y. Eratlı Şirin and M. Şahin, ‘Investigation of Factors Affecting the Achievement of University Students with Logistic Regression Analysis: School of Physical Education and Sport Example’, SAGE Open, vol. 10, no. 1, 2020, doi: 10.1177/2158244020902082.
[6] E. S. and J. K. Maryam Bibi, Zaheer Abbas, ‘Identification of Factors behind Academic Performance’, J. ISOSS, vol. 6, no. 1, pp. 29–339, 2020.
[7] E. T. Lau, L. Sun, and Q. Yang, ‘Modelling, prediction and classification of student academic performance using artificial neural networks’, SN Appl. Sci., vol. 1, no. 9, pp. 1–10, 2019, doi: 10.1007/s42452-019-0884-7.
[8] F. Li, Y. Zhang, M. Chen, and K. Gao, ‘Which Factors Have the Greatest Impact on Student’s Performance’, J. Phys. Conf. Ser., vol. 1288, no. 1, 2019, doi: 10.1088/1742-6596/1288/1/012077.
[9] A. Abu Saa, M. Al-Emran, and K. Shaalan, Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques, vol. 24, no. 4. Springer Netherlands, 2019.
[10] M. Albarka, ‘Student Academic Performance Prediction using Artificial Neural Networks: A Case Study’, Int. J. Comput. Appl., vol. 178, no. 48, pp. 24–29, 2019, doi: 10.5120/ijca2019919387.
[11] Z. Ahmad and E. Shahzadi, ‘Prediction of Students’ Academic Performance using Artificial Neural Network’, Bull. Educ. Res., vol. 40, no. 3, pp. 157–164, 2018.
[12] O. C. Asogwa and A. V Oladugba, ‘Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs)’, Am. J. Appl. Math. Stat., vol. 3, no. 4, pp. 151–155, 2015, doi: 10.12691/ajams-3-4-3.
[13] P. Cheewaprakobkit, ‘Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques’, Catal. J. Inst. Interdiscip. Stud., vol. 12, no. 2, pp. 34–43, 2015.
[14] S. H. Teshnizi and S. M. T. Ayatollahi, ‘A comparison of logistic regression model and artificial neural networks in predicting of student’s academic failure’, Acta Inform. Medica, vol. 23, no. 5, pp. 296–300, 2015, doi: 10.5455/aim.2015.23.296-300.
[15] S. A. Naser, I. Zaqout, M. A. Ghosh, R. Atallah, and E. Alajrami, ‘Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology’, Int. J. Hybrid Inf. Technol., vol. 8, no. 2, pp. 221–228, 2015, doi: 10.14257/ijhit.2015.8.2.20.
[16] D. W. Hosmer and S. Lemeshow, Applied Logistic Regression. 2005.
[17] D. G. Kleinbaum and M. Klein, Logistic Regression A Self-Learning Text Second Edition. 2002.
[18] I. Basheer, ‘Artificial neural networks : fundamentals , computing , design , and’, vol. 7012, no. April, pp. 2–31, 2018, doi: 10.1016/S0167-7012(00)00201-3.
[19] K. Mehrotra and C. K. Mohan, Elements of Artificial Neural Networks. 1996.
[20] S. Haykin, Neural Networks and Learning Machines, Third Edit. Pearson Prentice Hall, 2009.
[21] R. Alyuda, ‘Alyuda NeuroIntelligence User Manual’. Alyuda Research, Inc., pp. 1–132, 2003.
