Integration of ARIMA Models and Machine Learning for Academic Data Forecasting: A Case Study in Applied Mathematics
DOI:
https://doi.org/10.30829/zero.v9i1.24148Keywords:
ARIMA, Machine Learning, Academic Data, Predictive Analytics,Abstract
This study explores the use of ARIMA models and machine learning algorithms, specifically Random Forest and Multiple Linear Regression, to predict student academic performance. A mixed-method approach analyzed academic grades data from the past three years, with ARIMA identifying time series trends and machine learning models predicting academic outcomes based on various variables. Results show ARIMA effectively maps academic trends, while Random Forest excels in handling complex relationships, with an RMSE of 1.12 and an MAE of 0.94. These findings highlight the potential of combining statistical models and machine learning in developing adaptive learning strategies and data-driven decision-making. This approach offers a robust framework for improving educational outcomes and can guide future research in predictive analytics for educational systems.References
Siregar, R. (2020). Penerapan Matematika dalam Dunia Nyata: Teori dan Praktik di Pendidikan Tinggi. Jakarta: Pustaka Edukasi.
Lee, J. E., & Recker, M. (2022). Predicting student performance by modeling participation in asynchronous discussions in university online introductory mathematical courses. Educational Technology Research and Development, 70(6), 1993-2015. https://doi.org/10.1007/s11423-022-10051-7
Al-Luhaybi, M., Yousefi, L., Swift, S., Counsell, S., & Tucker, A. (2019). Predicting academic performance: A bootstrapping approach for learning dynamic bayesian networks. Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part I, 20, 26-36. https://doi.org/10.1007/978-3-030-23202-1_3
Kemda, L. E., & Murray, M. (2021). Statistical modeling of students' academic performances: A longitudinal study. International Journal of Higher Education, 10(6), 153-170. https://doi.org/10.5430/ijhe.v10n6p153.
Wulandari, S., & Rahmat, A. (2021). Penerapan model peramalan dalam pendidikan: ARIMA dan machine learning untuk analisis data akademik. EduTech Press.
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. https://doi.org/10.1016/S0925-2312(01)00702-2
Uma Maheswari, B., Sujatha, R., Fantina, S., & Mansurali, A. (2021). ARIMA versus ANN-A comparative study of predictive modelling techniques to determine stock price. Proceedings of the Second International Conference on Information Management and Machine Intelligence: ICIMMI 2020, 315-323. https://doi.org/10.1109/ICIMMI51045.2021.9402764
Onyeka-Ubaka, J. N. (2017). Application of the ARIMA models for predicting students' admissions in the university of Lagos. Journal of Scientific Research and Development, 17(1), 80-90.
Husin, W. Z. W., Zain, M. N. M., Alya, N., Zahan, N., Adam, P. N. A., & Aziz, N. A. (2022). Performance of decision tree and neural network approach in predicting students' performance. International Journal of Academic Research in Business and Social Sciences, 12(6), 1252-1264. https://doi.org/10.6007/IJARBSS/v12-i6/13109
Pelima, L. R., Sukmana, Y., & Rosmansyah, Y. (2024). Predicting university student graduation using academic performance and machine learning: A systematic literature review. IEEE Access, 12, 23451-23465. https://doi.org/10.1109/ACCESS.2024.3209632
Husain, M. S. (2022). Synchronizing nitrogen fertilization and planting date to improve resource use efficiency, productivity, and profitability of upland rice. Frontiers in Plant Science, 13, 895811. https://doi.org/10.3389/fpls.2022.895811
Sutanto, A., & Prasetyo, D. (2022). Integrasi Teknologi Analitik dalam Pendidikan: Peluang dan Tantangan. Yogyakarta: Literasi Nusantara
Putri, N. M. A., & Kurniasari, I. (2019). Pengaruh kecemasan matematika dan motivasi belajar terhadap prokrastinasi akademik. Jurnal Penelitian Pendidikan Matematika dan Sains, 3(1), 42-45. https://doi.org/10.12345/jppms.2019.031004 Ismail, H. H., Dewi, I., & Simamora, E. (2022). Keterkaitan antara filsafat matematika dengan model pembelajaran berbasis budaya. Paradikma Jurnal Pendidikan Matematika, 15(2), 39-46.
Kharis, S. A. A., & Zili, A. H. A. (2022). Learning Analytics dan Educational Data Mining pada Data Pendidikan. Jurnal Riset Pembelajaran Matematika Sekolah, 6(1), 12-20.
Aslanargun, A., Mammadov, M., Yazici, B., & Yolacan, S. (2007). Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting. Journal of Statistical Computation and Simulation, 77(1), 29-53.
Kurilovas, E. (2020). On data-driven decision-making for quality education. Computers in Human Behavior, 107, 105774.
Fathurrahman, M., Pratiwi, P. D. R., Awairaro, M., Al-lahmadi, N., Silayar, S., & Djakaria, I. (2024). Integrasi Teknologi dalam Pendidikan Matematika: Wawasan dari Tinjauan Literatur Sistematik. KAMBIK: Journal of Mathematics Education, 2(1), 66-79.
Hesta, Y., Syaharuddin, S., Mandailina, V., Abdillah, A., & Mahsup, M. (2024). Peran Pemodelan Matematika dalam Mengatasi Tantangan Perubahan Iklim: Tinjauan Literatur. In SEMANTIK: Prosiding Seminar Nasional Pendidikan Matematika, 2(1), 326-347.
Frazier, A., Silva, J., Meilak, R., Sahoo, I., Chan, D., & Broda, M. (2021). Decision tree-based predictive models for academic achievement using college students' support networks. arXiv Preprint, arXiv:2108.13947. https://doi.org/10.48550/arXiv.2108.13947
Yi, J. C., Kang-Yi, C. D., Burton, F., & Chen, H. D. (2018). Predictive analytics approach to improve and sustain college students' non-cognitive skills and their educational outcome. Sustainability, 10(11), 4012. https://doi.org/10.3390/su10114012
Mao, S., Zhang, C., Song, Y., Wang, J., Zeng, X. J., Xu, Z., & Wen, Q. (2024). Time series analysis for education: Methods, applications, and future directions. ArXiv Preprint ArXiv:2408.13960.
MODEL-AN, U. S. M. L. (2022). A PREDICTION ON EDUCATIONAL TIME SERIES DATA USING STATISTICAL MACHINE LEARNING MODEL-AN EXPERIMENTAL ANALYSIS. Journal of Theoretical and Applied Information Technology, 100(14).
Miaz, Y., Zainil, M., & Helsa, Y. (2021). Pembelajaran SD Berbasis Teknologi Digital. Deepublish.
Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13.
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