Student academic success prediction system using Random Forest technique
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Abstract
The challenges of academic underperformance and rising dropout rates have contributed to declining student retention rates, resulting in implications such as wasted time, increased expenses, and missed job opportunities. Consequently, the need to analyze and prepare student data is paramount. This study aims to address these challenges by: 1) investigating and constructing a predictive model of student academic performance within the Data Science and Software Innovation (DSSI), Information Technology and Communications (ICT), Chemistry, and Biology programs at the Faculty of Science, Ubon Ratchathani University, utilizing the Random Forest technique, and 2) creating a prototype web application to apply the model and summarize the findings, developing with Django and PostgreSQL. The dataset used for model construction encompasses academic records from both secondary school and university for 1,336 students enrolled in the programs spanning academic years 2017-2021. This dataset includes 11 factors: educational programs, first-term university GPA, and academic performance in 8 core subject groups: Thai Language, Mathematics, Science, Social Studies, Religion and Culture, Arts, Career and Technology, Foreign Languages, alongside high school GPA. The results of the prototype model research for DSSI, ICT, Chemistry, and Biology programs demonstrates the ability to predict student learning outcomes, with F1-measure values of 88.02%, 86.18%, 84.04% and 85.18% respectively. Consequently, the implementation of the developed system holds potential to benefit students, educators, and educational institutions in the design and implementation of more effective and tailored teaching methodologies.
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References
Alboaneen, D., Almelihi, M., Alsubaie, R., Alghamdi, R., Alshehri, L. and Alharthi, R. (2022). Development of a web-based prediction system for students’ academic performance. Data, 7(2), 21.
Amazon Web Services. (2024). What is Django?. Retrieved February 22, 2024, from AWS: https://aws.amazon.com/th/what-is/django/
Brownlee, J. (2020). 4 Types of Classification Tasks in Machine Learning. Retrieved February 22, 2024, from Machine Learning Mastery: https://machinelearningmastery.com/types-of-classification-in-machine-learning/?fbclid=IwAR2gbsHUbY_YUdt4l4RBvHAQyze3YlMYVsnyjq99ZJW36lb5qjMISvYcZ2A
Data Innovation and Governance Institute. (2022). Understand in 5 minutes! What is a Classification Model? (in Thai). Retrieved February 22, 2024, from DIGI: https://digi.data.go.th/blog/what-is-classification-model/
Hailu, B. and Murty, M. N. (2019). Performance analysis and prediction of students using random forest algorithm. International Journal of Engineering Research and Technology, 12(2), 129-136.
Han, J., Kamber, M. and Pei, J. (2012). Data Mining: Concepts and Techniques. Melbourne: Elsevier Inc.
Jamjumrat, C. (2022). What Is A Decision Tree?. Retrieved 22 February 2024, from borntoDev: https://www.borntodev.com/2022/09/15/รู้จักกับ-decision-tree/
Kaensar, C. and Wongnin, W. (2023). Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning. Eurasia Journal of Mathematics, Science and Technology Education, 19(12), em2369.
Kaensar, C. and Wongnin, W. (2024). Enhancing prediction of student learning success in higher education using deep learning (in Thai). Journal of Science and Science Education, 7(1), 21–36.
Kongchai, P., Hiranpongsin, S. and Ditcharoen, N. (2024). Creating a prediction model for prospective university student admissions during the COVID-19 pandemic situation: A case study of the faculty of science at Ubon Ratchathani university (in Thai). The Journal of King Mongkut's University of Technology North Bangkok, 34(2), 1–10.
Li, H. and Zhao, C. (2020). Research on the Application of Machine Learning in Web Application Development. In Proceedings of Smart Grid and Electrical Automation International Conference 2020, (pp. 478-482). Zhangjiajie: China.
Mekha, P., Musikong, P., Palakong, N., Pramokchon, P. and Kasemsumran, P. (2023). Performance comparison of image classification models for corn leaf disease (in Thai). Maejo Information Technology and Innovation Journal, 9(2), 1–16.
Nachouki, M., Elfadil, A.M., Mehdi, R. and Naaj, M (2023). Student course grade prediction using the random forest algorithm: Analysis of predictors' importance. Trends in Neuroscience and Education, 33, 100214.
Naresuan University Publishing House. (2023). Data Mining (in Thai). Retrieved February 22, 2024. from NUPRESS: https://www.nupress.grad.nu.ac.th/data-mining/#3--classification
Riansut, W. (2016). Factors influencing on grade point average of thaksin university’s undergraduate students (in Thai). Science Journal of Phetchaburi Rajabhat University, 13(2), 23-38.
Shalev-Shwartz, S. and Ben-David, S. (2014). Understanding Machine Learning. Cambridge: Cambridge University Press.
Shou, Z., Xie, M., Mo, J. and Zhang, H (2024). Predicting student performance in online learning: a multidimensional time-series data analysis approach. Applied Sciences. 14(6), 1-16.
Soontranon, N. (2023). Evaluation Metrics for Classification Model (in Thai). Retrieved February 22, 2024, from NERD: https://www.nerd-data.com/classification-metrics/
Valverde-Berrocoso, J., Acevedo-Borrega J. and Cerezo-Pizarro, M. (2022), Educational technology and student performance: a systematic review. Frontiers in Education, 7(1), 1-13.
Wongsakoonkan, W., Ju-Ngam, B., Komuttiban, P., Klunbut, P. and Alongkorn, V. (2021). A comparison of learning achievement of the undergraduate students admitted through direct examination and admission case study: department of occupational health and safety, faculty of science and technology: Valaya Alongkorn Rajabhat University Under the Royal Patronage (in Thai). Valaya Alongkorn Review Journal, 11(1), 1-12.