Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques
The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by onlin.
- Pub. date: May 15, 2023
- Pages: 297-307
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The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.
Keywords: Educational data mining, learning achievement, learning analytics, online learning model, student model.
References
Abdel-Basset, M., Chang, V., & Nabeeh, N. A. (2021). An intelligent framework using disruptive technologies for COVID-19 analysis. Technological Forecasting and Social Change, 163, Article 120431. https://doi.org/10.1016/j.techfore.2020.120431
Abuhammad, S. (2020). Barriers to distance learning during the COVID-19 outbreak: A qualitative review from parents’ perspective. Heliyon, 6(11), Article e05482. https://doi.org/10.1016/j.heliyon.2020.e05482
Al-Kumaim, N. H., Mohammed, F., Gazem, N. A., Fazea, Y., Alhazmi, A. K., & Dakkak, O. (2021). Exploring the impact of transformation to fully online learning during COVID-19 on Malaysian University students’ academic life and performance. International Journal of Interactive Mobile Technologies, 15(5), 140-158. https://doi.org/10.3991/ijim.v15i05.20203
Appiah-Kubi, P., & Annan, E. (2020). A review of a collaborative online international learning. International Journal of Engineering Pedagogy, 10(1), 109–124. https://doi.org/10.3991/ijep.v10i1.11678
Badal, Y. T., & Sungkur, R. K. (2022). Predictive modelling and analytics of students’ grades using machine learning algorithms. Education and Information Technologies. 28, 3027-3057. https://doi.org/10.1007/s10639-022-11299-8
Casanova, J. R., Gomes, C. M. A., Bernardo, A. B., Núñez, J. C., & Almeida, L. S. (2021). Dimensionality and reliability of a screening instrument for students at-risk of dropping out from higher education. Studies in Educational Evaluation, 68, Article 100957. https://doi.org/10.1016/j.stueduc.2020.100957
Dechsupa, S., Assawakosri, S., Phakham, S., & Honsawek, S. (2020). Positive impact of lockdown on COVID-19 outbreak in Thailand. Travel Medicine and Infectious Disease, 36, Article 101802. https://doi.org/10.1016/j.tmaid.2020.101802
de Oliveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2021). How does learning analytics contribute to prevent students’ dropout in higher education: A systematic literature review. Big Data and Cognitive Computing, 5(4), Article 64. https://doi.org/10.3390/bdcc5040064
Francis, B. K., & Babu, S. S. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of Medical Systems, 43, Article 162. https://doi.org/10.1007/s10916-019-1295-4
Ghoggali, N., Douak, F., & Ghoggali, W. (2022). Towards a NIR spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction. Optik, 257, Article 168813. https://doi.org/10.1016/j.ijleo.2022.168813
Giani, U., & Martone, P. (1998). Distance learning, problem based learning and dynamic knowledge networks. International Journal of Medical Informatics, 50(1), 273–278. https://doi.org/10.1016/S1386-5056(98)00080-X
Karalar, H., Kapucu, C., & Gürüler, H. (2021). Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system. International Journal of Educational Technology in Higher Education, 18, Article 63. https://doi.org/10.1186/s41239-021-00300-y
Karimi-Haghighi, M., Castillo, C., & Hernández-Leo, D. (2022). A causal inference study on the effects of first year workload on the dropout rate of undergraduates. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education (pp. 15–27). Springer. https://doi.org/10.1007/978-3-031-11644-5_2
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001
Lim, C. L., Ab Jalil, H., Ma’rof, A. M., & Saad, W. Z. (2020). Peer learning, self-regulated learning and academic achievement in blended learning courses: A structural equation modeling approach. International Journal of Emerging Technologies in Learning, 15(3), 110-125. https://doi.org/10.3991/ijet.v15i03.12031
Nachouki, M., & Abou Naaj, M. (2022). Predicting student performance to improve academic advising using the random forest algorithm. International Journal of Distance Education Technologies, 20(1), Article 2. https://doi.org/10.4018/IJDET.296702
Nuankaew, P. (2020). Clustering of mindset towards self-regulated learning of undergraduate students at the University of Phayao. Advances in Science, Technology and Engineering Systems, 5(4), 676–685. https://doi.org/10.25046/aj050481
Nuankaew, P., Nasa-Ngium, P., & Nuankaew, W. S. (2022). Improving predictive model to prevent students’ dropout in higher education using majority voting and data mining techniques. In O. Surinta & K. Kam Fung Yuen (Eds.), Multi-disciplinary trends in artificial intelligence (pp. 61–72). Springer. https://doi.org/10.1007/978-3-031-20992-5_6
Nuankaew, W., & Nuankaew, P. (2021). Educational engineering for models of academic success in Thai universities during the COVID-19 pandemic: Learning strategies for lifelong learning. International Journal of Engineering Pedagogy, 11(4), 96–114. https://doi.org/10.3991/ijep.v11i4.20691
Nuankaew, W., Nuankaew, P., Doenribram, D., & Jareanpon, C. (2023). Weighted voting ensemble for depressive disorder analysis with multi-objective optimization. Current Applied Science and Technology, 23(1), 1–20. https://doi.org/10.55003/cast.2022.01.23.015
Pozdnyakova, O., & Pozdnyakov, A. (2017). Adult students’ problems in the distance learning. Procedia Engineering, 178, 243–248. https://doi.org/10.1016/j.proeng.2017.01.105
Smirani, L. K., Yamani, H. A., Menzli, L. J., & Boulahia, J. A. (2022). Using ensemble learning algorithms to predict student failure and enabling customized educational paths. Scientific Programming, 2022, Article e3805235. https://doi.org/10.1155/2022/3805235
Viloria, A., & Pineda Lezama, O. B. (2019). Mixture structural equation models for classifying university student dropout in Latin America. Procedia Computer Science, 160, 629–634. https://doi.org/10.1016/j.procs.2019.11.036
Wang, J., & Biljecki, F. (2022). Unsupervised machine learning in urban studies: A systematic review of applications. Cities, 129, Article 103925. https://doi.org/10.1016/j.cities.2022.103925
Xu, F., Li, Z., Yue, J., & Qu, S. (2021). A systematic review of educational data mining. In K. Arai (Ed.), Intelligent computing (pp. 764–780). Springer. https://doi.org/10.1007/978-3-030-80126-7_54