Prediction of Passing Probability of Students in Higher Education Level Using Machine Learning Algorithm
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Abstract
The purposes of this research were: 1) to develop a model to predict the probability of first-year students in the Computer Science program passing to the second year, and 2) to implement an application predicting the probability of first-year students in the Computer Science program passing to the second year.
The data were collected using a questionnaire administered to first to fourth-year students in the Computer Science program, totaling 125 individuals, at Buriram Rajabhat University. Data were also collected from the Office of Academic Promotion and Registration of Buriram Rajabhat University. Imbalanced data were adjusted using SMOTE. The model was created using three algorithmic techniques, including logistic regression, random forest, and decision tree. The Efficiency of each algorithm was measured using accuracy and root mean square error (RMSE). The Results of the measurement of the models’ efficiency revealed that the model utilizing logistic regression had the highest prediction efficiency.
With regard to the results of the development of an application for predicting the probability of passing to the second year of the first-year computer science students, the most efficient model was deployed into a web application used for prediction using Python and Django Framework with PostgreSQL as the database. Moreover, the results of an analysis of users’ satisfaction with the web application showed that the users’ satisfaction was at the highest level with a mean score of 4.59 and a standard deviation of 0.55.
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References
Akarachantachote, N., & Panitsupakamol, D. (2019). Comparison of imbalanced data problem solving for income classification of type I pharmacies entrepreneur. The 9 th STOU National Research Confer-ence, Nonthaburi, Thailand. http://sci-database.hcu.ac.th/science/file/rsID292_F1_20200122213859.pdf
Atik, D. A., Işildar, Y. G., & Erkoç, F. (2021). Prediction of secondary school students’ environmental attitudes by a logistic regression model. Environment, Development and Sustainability, 24, 4355-4370. https://doi.org/10.1007/S10668-021-01618-3
Bancha, P. (2021). Sā ngō̜kān rīanrū samrap AI dūai Python machine learning [Create learning for AI with Python machine learning]. Se-Ed.
Beaulac, C., & Rosenthal, S. J. (2019). predicting university students’ academic success and major using Random Forests. Research in Higher Education, 60(7), 1048-1064. https://doi.org/10.1007/S11162-019-09546-Y
Behr, A., Giese, M., Herve, D., Teguim, K., & Theune, K. (2020). Early prediction of university dropouts – a random forest approach. Journal of Economics and Statistics, 240(6), 743-789. https://doi.org/10.1515/JBNST-2019-0006
Chacha, R. C. B., López, L. G. W., Guerrero, X. V. V., & Villacis, G. V. W. (2019, December 3-5). Stu-dent dropout model based on logistic regression. First International Conference, ICAT 2019, Quito, Ec-uador. https://doi.org/10.1007/978-3-030-42520-3_26
Clitan, I., Puscasiu, A., Muresan, V., Unguresan L. M., & Abrudean, M., (2021). Web application for sta-tistical tracking and predicting the evolution of active cases with the novel Coronavirus (SARS-CoV-2). International Journal of Modeling and Optimization, 11(3), 70-74. https://doi.org/10.7763/IJMO.2021.V11.780
Dien, T. T., Duy-Anh, L., Hong-Phat. N., Van-Tuan, N., Thanh-Chanh, T., Minh-Bang, L., Thanh-Hai, N., & Thai-Nghe, N. (2021, July 1-3). Four grade levels-based models with Random Forest for student perfor-mance prediction at a multidisciplinary university. Proceedings of the 15th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2021), Asan, Korea. https://doi.org/10.1007/978-3-030-79725-6_1
Elreedy, D., & Atiya, F. A. (2019). A comprehensive analysis of synthetic minority oversampling tech-nique (SMOTE) for handling class imbalance. Information Sciences, 505, 32-64. https://doi.org/10.1016/j.ins.2019.07.070
Fathiya, H., & Sadath, L. (2021, March 17-18). University admissions predictor using Logistic Regres-sion. 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Amity University Dubai, UAE46-51. https://doi.org/10.1109/ICCIKE51210.2021.9410717
Gotardo, A. M. (2019). Using decision tree algorithm to predict student performance. Indian Journal of Science and Technology, 12(5), 1-8. https://doi.org/10.17485/IJST/2019/V12I5/140987
Grus, J. (2019). Data science from scratch (2nd ed). Core Function.
Hussain, S., & Khan, Q. M. (2021). Student-performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning. Annals of Data Science, 10(3), 637-655. https://doi.org/10.1007/s40745-021-00341-0
Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, P. (2021). Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques. IEEE Access, 9, 39707-39716. https://doi.org/10.1109/ACCESS.2021.3064084
Kepan, S., Leelapatarapun, P., & Yokkhun, A. (2018). Analysis of factors influencing the dismissal of students using data mining techniques case study: Computer Science Program and Information Tech-nology Program of Yala Rajabhat University [Master’s thesis, Yala Rajabhat University]. YRU Wisdom Bank. https://wb.yru.ac.th/handle/yru/3447
Khakata, E., Omwenga, O., & Msanjila. S. (2019). Student performance prediction on internet mediated environments using decision trees. International Journal of Computer Applications, 181(42), 1-9. https://doi.org/10.5120/IJCA2019918466
Li, S., & Liu, T. (2021). Performance prediction for higher education students using deep learning. Hindawi Complexity, 2021, 9958203. https://doi.org/10.1155/2021/9958203
Pacharawongsakda, E. (2020). A little book of big data and machine learning. IDC Premier.
Prasetyo, D. H., Hogantara, A. P., & Isnainiyah, N. I. (2021). A web-based diabetes prediction application using XGBoost algorithm. Journal of Computing and Applied Informatics, 5(2), 49-59. https://doi.org/10.32734/JOCAI.V5.I2-6290
Rekha, R., Abirami, A. P., Aishvarya, G., Akshaya, B., Annapoorna, K. A., & Sanchana, S. (2021). A web based application for tracking public transport and predicting usage. International Journal of Aquatic Science, 12(2), 3770-3783. https://www.journal-aquaticscience.com/article_135799.html
Saraubon, K. (2020). Rīanrū data science læ AI: Machine learning dūai python [Learn data science and AI: Machine learning with Python. Media Network.
Srisa-ard, B. (2013). Kānwičhai bư̄angton [Introduction to research] (9th ed.). Suweerivasarn.
Thaweechat, N., Pengprachan, O., Yathongchai, W., & Yathongchai, C. (2022). A prediction system for undergraduate student dropout at faculty of science, Buriram Rajabhat University using data mining techniques. Science and Technology Buriram Rajabhat University, 4(1), 47-60. https://ph02.tci-thaijo.org/index.php/scibru/article/view/242082
Wu, J., Lin, M., & Tsai, C. (2023). A predictive model that aligns admission offers with student enrollment probability. Education Sciences, 13(5), 1-13. https://doi.org/10.3390/educsci13050440
