The Prediction of Bachelor Admission Trends in the University Using Data Mining Techniques
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Abstract
The declining number of students has become a pressing issue, impacting many universities worldwide, both in the present and the future. Currently, there is an increase in the number of public and private universities. However, university enrollment rates are decreasing, leading to intensified competition among universities for student admissions. The objective of this research is to predict fields of study trends in Bachelor degree of University applicants and to use this information for decision-making, resource allocation, and university curriculum development by using Data Mining Technique. The predictive models in this study comprise 3 formats: Naïve Bayes, Logistic Regression and Decision Tree. The dataset used in this research is sourced from the information system of the Academic Promotion and Registration Division at Nakhon Pathom Rajabhat University, encompassing undergraduate student records from the academic years 2014 to 2018, totaling 89,847 records. The results of comparing the performance of data classification models using K-Fold Cross Validation revealed that the best-performing prototype model is Logistic Regression, with an accuracy of 61.3%. This research can be applied to predict trends in prospective undergraduate students' selection of academic majors, inform strategic decision-making, and facilitate resource allocation within the university.
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P. Rojanavasu. "Educational Data Analytics using Association Rule Mining and Classification." 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), Thailand, pp. 142-145, 2019.
W. L. Liu and W. McKibbin. "Macroeconomic Impacts of Global Demographic Change: The Case of Australia." Asian Economic Papers, Vol. 21, No. 3, pp. 78-111, 2022.
D. Gu, K. Andreev, and M. E. Dupre. "Major Trends in Population Growth Around the World." China CDC Weekly, Vol. 3, No. 28, pp. 604 - 613, July, 2021.
B. H. Kunaryo, M. Somantri, and A. Sofwan. "Applicant Mapping of Predicting Seriousness in Choosing Private University using Self Organizing Map (SOM) Algorithm." International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Indonesia, pp. 130-134, 2020.
J. P. Wu, M. S. Lin, and C.L. Tsai. "A Predictive Model That Aligns Admission Offers with Student Enrollment Probability." Education Sciences, Vol. 13, No. 5, No. 440, April, 2023.
R. Jeansuti, H. Jeansuti, S. Tanawutpornpinit, and S. Siriwatcharaworakun. "Approaches to Developing New Normal Teachers in Digital Literacy." Journal of teacher professional development, Vol. 2, No. 2, pp. 16-31, May-August, 2021.
M. Yağcı. "Educational data mining: prediction of students' academic performance using machine learning algorithms." Smart Learning Environments, Vol. 9, No. 1, March, 2022.
H. Bousnguar, L. Najdi, and A. Battou. "Forecasting approaches in a higher education setting." Education and Information Technologies, Vol. 27, pp. 1993-2011, 2021.
H. A. Mengash. "Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems." IEEE Access, Vol. 8, pp. 55462-55470, 2020.
H. Sabnani, M. More, P. Kudale, and S. Janrao. "Prediction of Student Enrolment Using Data Mining Techniques." International Research Journal of Engineering and Technology (IRJET), Vol. 5, No. 4, pp. 1830-1833, 2018.
A. P.D. Cruz, Ma. L.B. Basallo, B.A.B. III, J. Aguilar, C. K.P. Calvo, J.C.T. Arroyo, and A.J.P. Delima. "Higher Education Institution (HEI) Enrollment Forecasting Using Data Mining Technique." International Journal of Advanced Trends in Computer Science and Engineering, Vol. 9, No. 2, pp. 2060-2064, 2020.
I. Ognjanovic, D. Gasevic, and S. Dawson. "Using institutional data to predict student course selections in higher education." The Internet and Higher Education, Vol. 29, pp. 49-62, April, 2016.
Muladi, U. Pujianto, and U. Qomaria. "Predicting high school graduates using NaÏve Bayes in State University Entrance Selections." International Conference on Vocational Education and Training (ICOVET), Indonesia, pp. 155-159, 2020.
A. I. Adekitan and E. Noma-Osaghae. "Data mining approach to predicting the performance of first year student in a university using the admission requirements." Education and Information Technologies, Vol. 24, pp. 1527-1543, 2019.
S. Vanitha and R. Jayahree. "A Prediction on Education Time Series Data Using Statical Machine Learning Model-An Experiment Analysis." Journal of Theoretical and Applied Information Technology, Vol. 100, No. 14, pp. 5189-5200, 2020.
I. D. Yakubu and J. A. Awaab. "Assessing Students' Enrolment in Bolgatanga Polytechnic Using Time Series Analysis." East African Scholars Journal of Engineering and Computer Sciences, Vol. 2, No. 4, pp. 120-135, 2019.
M. V. Amazona and A. A. Hernandez. "Modelling Student Performance Using Data Mining Techniques: Inputs for Academic Program Development." ICCDE '19: Proceedings of the 2019 5th International Conference on Computing and Data Engineering, pp. 36-40, 2019.
E. N. Iqbal and R. Asif. "Analyzing Applicant's Pre-admission data and Predicting Applicant's Performance in Pre-Admission Test using Data Mining Techniques." International Conference on Innovations in Computer Science & Software Engineering (ICONICS), Karachi, Pakistan, pp. 1-11, 2022.
G. Dewantoro and N. Ardisa. "A Decision Support System for Undergraduate Students Admissions using Educational Data Mining." 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Indonesia, pp. 105-109, 2020.
Y. Zhao, Q. Xu, and M. Chen. "Predicting Student Performance in a Master of Data Science Program using Admissions Data." Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020), pp. 325-333, 2020.
S. Garmpis, M. Maragoudakis, and A. Garmpis. "Assisting Educational Analytics with AutoML Functionalities." Computers, Vol. 11, No. 6, pp. 97, 2022.
B. Ujkani, D. Minkovska, and L. Stoyanova. "A Machine Learning Approach for Predicting Student Enrollment in the University." International Scientific Conference Electronics (ET), Sozopol, Bulgaria, pp. 1-4, 2021.
W. Tenpipat and K. Akkarajitsakul. "Student Dropout Prediction: A KMUTT Case Study." International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand, pp. 1-5, 2020.
N. Bedregal-Alpaca, V. Cornejo-Aparicio, J, Zárate-Valderrama, and P. Yanque-Churo. "Classification Models for Determining Types of Academic Risk and Predicting Dropout in University Students." International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 11, No. 1, pp. 266-272, 2020.
M. Charlotte. "Modeling an Automated Student's Performance Predictor by Using Decision Tree." East African Journal of Science and Technology, Vol. 9, No. 1, pp. 121-137, 2019.
M. Ursan and M. Bucos. "Predictive Analytics Models for Student Admission to Master Programs in Romania." International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, pp. 1-4, 2022.
T. Pinyo. "The Study on the Report of the Logistic Regression Analysis Results in Research Work." Humanities, Social Sciences and arts, Vol. 12, No. 5, pp. 544-558, 2019.
S. Jeganathan, S. Parthasarath, A.R. Lakshminarayanan, P. M. Kumarand, and K. A. Khan. "Predicting the Post Graduate Admissions using Classification Techniques." International Conference on Emerging Smart Computing and Informatics (ESCI) AISSMS Institute of Information Technology, India, pp. 5-7, 2021.
M. A. A. Walid, S. M. Masum Ahmed, and S. M. S. Sadique. "A Comparative Analysis of Machine Learning Models for Prediction of Passing Bachelor Admission Test in Life-Science Faculty of a Public University in Bangladesh." IEEE Electric Power and Energy Conference (EPEC), Edmonton, Canada, pp. 1-6, 2020.