Article Sidebar
Views PDF: 0
Predicting learning status of informatics students at Dong Thap University based on machine learning models
Main Article Content
Abstract
For effective student support, it is essential to forecast student academic performance status. This study explores student academic performance and applies four machine learning (ML) models to predict student learning alert status. The research collects input data to build a model including parameters such as entrance grades, information about accommodation, learning environment, and first-semester result from undergraduates of the Faculty of Mathematics and Computer Education, Dong Thap University. The effective ML techniques such as Logistic regression (LR), Support vector machine (SVM), Decision trees (DTs), and Random Forest (RF) are suggested to predict student learning alert status. The outcomes are evaluated on metrics like Accuracy, Precision, Recall, and F1 score. The results show that the forecasting ability of logistic regression models outperforms in classifying student performance compared to other methods by yielding optimal classification results like high accuracy and Sensitivity followed by RF, SVM, and DTs.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Keywords
decision trees, learning alert status, logistic regression, random forest, support vector machine
References
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49.
Alsalman, Y. S., Halemah, N. K. A., AlNagi, E. S., & Salameh, W. (2019, June). Using decision tree and artificial neural network to predict students academic performance. In 2019 10th international conference on information and communication systems (ICICS) (pp. 104-109). IEEE.
Anand, M. (2019). Advances in edm: a state of the art. Software Engineering: Proceedings of CSI 2015, 193-201.
Beaulac, C., & Rosenthal, J. S. (2019). Predicting university students’ academic success and major using random forests. Research in Higher Education, 60, 1048-1064.
Bydžovská, H. (2016). A Comparative Analysis of Techniques for Predicting Student Performance. International Educational Data Mining Society.
Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human behavior, 107, 105584.
Chung, J. Y., & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346-353.
Dewan, M. A. A., Lin, F., & Wen, D. (2015, August). Predicting dropout-prone students in e-learning education system. In 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) (pp. 1735-1740). IEEE.
Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S. (2019). Using machine learning to predict student difficulties from learning session data. Artificial Intelligence Review, 52, 381-407.
Iqbal, Z., Qadir, J., Mian, A. N., & Kamiran, F. (2017). Machine learning based student grade prediction: A case study. arXiv preprint arXiv:1708.08744. DOI: arxiv.org/abs/1708.08744.
Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 982.
Liu, Q., Tong, S., Liu, C., Zhao, H., Chen, E., Ma, H., & Wang, S. (2019, July). Exploiting cognitive structure for adaptive learning. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 627-635).
Nguyen, P. H., Sheu, T. W., & Nagai, M. (2015). Predicting the student learning outcomes based on the combination of Taylor approximation method and Grey models. J. Sci. VNU J. Sci. Educ. Res, 31(2), 70-83.
Reeves, B. (2018). Development of rubrics to support teacher judgement of student proficiency in ethical Decision-Making (Masters Research thesis).
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 10(3), e1355.
Saa, A. A. (2016). Educational data mining & students’ performance prediction. International Journal of Advanced Computer Science and Applications, 7(5). doi: 10.14569/IJACSA.2016.070531.
Sultana, S., Khan, S., & Abbas, M. A. (2017). Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. International Journal of Electrical Engineering Education, 54(2), 105-118.
Sheikh, W., Parulkar, A., Ahmed, M. B., Osler, B., Liu, B., Sharma, E., ... & Chu, A. (2020). Recursive feature elimination (rfe) selects for important features in predicting post-operative cardiac arrest (poca) in patients undergoing coronary artery bypass grafting (cabg): insights from the sts registry. Journal of the American College of Cardiology, 75(11_Supplement_1), 1533-1533.
Tanuar, E., Heryadi, Y., Abbas, B. S., & Gaol, F. L. (2018, September). Using machine learning techniques to earlier predict student's performance. In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR) (pp. 85-89). IEEE.
Tokan, M. K., & Imakulata, M. M. (2019). The effect of motivation and learning behaviour on student achievement. South African Journal of Education, 39(1).
Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2019). Implementing AutoML in educational data mining for prediction tasks. Applied Sciences, 10(1), 90.
Wang, F., Liu, Q., Chen, E., Huang, Z., Chen, Y., Yin, Y., ... & Wang, S. (2020, April). Neural cognitive diagnosis for intelligent education systems. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 04, pp. 6153-6161).
Xu, B., & Yang, D. (2016). Motivation classification and grade prediction for MOOCs learners. Computational intelligence and neuroscience, 2016, 4-4. doi: 10.1155/2016/2174613