This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Kustitskaya, T. A., A. A. Kytmanov, M. V. Noskov. Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators. – Cybernetics and Information Technologies, Vol. 22, 2022, No 1, pp. 117-133. https://doi.org/10.2478/cait-2022-0008.Search in Google Scholar
Atahua, A. S., J. V. Guerrero, L. Andrade-Arenas, C. M. Huerta. Data Mining: Application of Digital Marketing in Education. – Advances in Mobile Learning Educational Research, Vol. 3, 2023, pp. 621-629.Search in Google Scholar
Abouzinadah, E., O. Rabie, A. Bessadok. Exploring Students Digital Activities and Performances through Their Activities Logged in Learning Management System Using Educational Data Mining Approach. – Interactive Technology and Smart Education, Vol. 20, 2023, pp. 58-72.Search in Google Scholar
Asif, R., N. G. Haider, K. Mahboob. Quality Enhancement at Higher Education Institutions by Early Identifying Students at Risk Using Data Mining. – Mehran University Research Journal of Engineering and Technology, Vol. 42, 2023, pp. 120-136.Search in Google Scholar
SouzaNeto, P. A., I. Silva, L. A. Guedes, T. M. Barros. Predictive Models for Imbalanced Data: A School Dropout Perspective. – Education Sciences, Vol. 9, 2019.Search in Google Scholar
Düsçtegör, D., E. Alyahyan. Predicting Academic Success in Higher Education: Literature Review and Best Practices. – International Journal of Educational Technology in Higher Education, Vol. 17, 2020, pp. 1-21.Search in Google Scholar
Lin, W. C., Y. H. Hu, G. T. Yao, C. F. Tsai. Under-Sampling Class Imbalanced Datasets by Combining Clustering Analysis and Instance Selection. – Information Sciences, Vol. 477, 2019, pp. 47-54.Search in Google Scholar
Kalegele, K., D. Machuve, N. Mduma. A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction. – Data Science Journal, Vol. 18, 2019, pp. 1-10.Search in Google Scholar
Hammoud, S., F. Kamalov, Gonsalves, F. Thabtah. Data Imbalance in Classification: Experimental Evaluation. – Information Sciences, Vol. 513, 2020, pp. 429-441.Search in Google Scholar
Rawashdeh, J., M. Abdullah, R. Mohammed. Machine Learning with Oversampling and Under-Sampling Techniques: Overview Study and Experimental Results. – In: Proc. of 11th International Conference on Information and Communication Systems (ICICS’20), 2020, pp. 243-248.Search in Google Scholar
Chawla, N. V., K. W. Bowyer, L. O. Hall, Kegelmeyer. SMOTE: Synthetic Minority Over-Sampling Technique. – Journal of Artificial Intelligence Research, Vol. 16, 2002, pp. 321-357.Search in Google Scholar
He, H., Y. Bai, E. A. Garcia, S. L i. ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. – In: Proc. of IEEE International Joint Conference on Neural Networks, 2008, pp. 1322-1328.Search in Google Scholar
Wang, W. Y., B. H. Mao, H. Han. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. – In: Proc. of International Conference on Advances in Intelligent Computing: Intelligent Computing, 2005, pp. 878-887.Search in Google Scholar
DeLaCalleja, J., O. Fuentes. A Distance-Based Over-Sampling Method for Learning from Imbalanced Data Sets. – In: Proc. of 20th International Florida Artificial Intelligence, 2007, pp. 634-635.Search in Google Scholar
Douzas, F. B. G., F. Last. Improving Imbalanced Learning through a Heuristic Oversampling Method Based on k-Means and SMOTE. – Information Sciences, 2018, pp. 1-20.Search in Google Scholar
Zhang, Y. Q., N. V. Chawla, S. Krasser, Y. Tang. SVMS Modeling for Highly Imbalanced Classification. – IEEE Transactions on Systems, Vol. 39, 2008, pp. 281-288.Search in Google Scholar
Maciejewski, T., J. Stefanowski. Local Neighbourhood Extension of SMOTE for Mining Imbalanced Data. – In: Proc. of IEEE Symposium on Computational Intelligence and Data Mining, 2011, pp. 104-111.Search in Google Scholar
Barua, S., M. M. Islam, X. Yao, K. Murase. MWMOTE – Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning. – IEEE Transactions on Knowledge and Data Engineering, Vol. 26, 2014, pp. 405-425.Search in Google Scholar
Bunkhumpornpat, C., K. Sinapiromsaran, C. Lursinsap. Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling Technique for Handling the Class Imbalanced Problem. – In: Proc. of 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2009, pp. 475-482.Search in Google Scholar
Prati, R. C., M. C. Monard, G. E. Batista. A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. – ACM, Vol. 6, 2004, pp. 20-29.Search in Google Scholar
Tahir, M., K. Jawad, M. A. Shah. Students’ Academic Performance and Engagement Prediction in a Virtual Learning Environment Using Random Forest with Data Balancing. – Sustainability, Vol. 14, 2022.Search in Google Scholar
Prasetyo, W. A., A. R. Taufani, U. Pujianto. Students Academic Performance Prediction with k-Nearest Neighbor and C4.5 on Smote-Balanced Data. – In: Proc. of 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI’20), 2020, pp 348-353.Search in Google Scholar
Kissoum, Y., A. Mouhssen, M. A. Karek, S, Mazouzi, M. L. Boughouas. Towards a Big Educational Data Analytics. – In: Proc. of International Conference on Advanced Aspects of Software Engineering (ICAASE’22), 2022, pp. 1-6.Search in Google Scholar
Shaiba, H., M. Bezbradica, S. Almutairi. Predicting Students’ Academic Performance and Main Behavioral Features Using Data Mining Techniques. – In: Proc. of 1st International Conference on Computing, in Advances in Data Science, Cyber Security and IT Applications, 2019, pp. 245-259.Search in Google Scholar
Ajoodha, R., K. Padayachee, E. Buraimoh. Importance of Data Resampling and Dimensionality Reduction in Predicting Students’ Success. – In: Proc. of International Conference on Electrical, Communication, and Computer Engineering (ICECCE’21), 2021, pp. 1-6.Search in Google Scholar
Ullah, Z., B. Fakieh, F. Kateb, F. Saleem. Intelligent Decision Support System for Predicting Student’s e-Learning Performance Using Ensemble Machine Learning. – Mathematics, Vol. 9, 2022.Search in Google Scholar
Ullah, Z., B. Fakieh, F. Kateb, F. Saleem. Comparing Different Resampling Methods in Predicting Students’ Performance Using Machine Learning Techniques. – IEEE Access, Vol. 8, 2020, pp. 67899-67911.Search in Google Scholar
Arham, T., Y. Niaz, A. Amin. Systematic Approach for Re-Sampling and Prediction of Low Sample Educational Datasets. – International Journal of Computing and Digital System, 2021.Search in Google Scholar
Rahman, T., I. Khan, I. Ullah, A. UrRehman, M. Baz, H. Hamam, O. Cheikhrouhou, B. K. Yousafzai, S. A. Khan. Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network. – Sustainability, Vol. 13, 2021.Search in Google Scholar
Lin, J., J. Yu. Data Mining Technology in the Analysis of College Students’ Psychological Problems. – Computer Science and Information Systems, Vol. 12, 2022, pp. 1583-1596.Search in Google Scholar
Lahoud, C., H. E. Khoury, P. Champin, C. Obeid. Novel Hybrid Recommender System Approach for Student Academic Advising Named Cohrs, Supported by Case-Based Reasoning and Ontology. – Computer Science and Information Systems, Vol. 19, 2022, pp. 979-1005.Search in Google Scholar
Sun, C., Z. Wu, J. Yang, J. Wang, T. Tao. Deep Neural Network-Based Prediction and Early Warning of Student Grades and Recommendations for Similar Learning Approaches. – Computer Science and Information Systems, Vol. 12, 2022.Search in Google Scholar
Hamtini, T., I. Aljarah, E. A. Amrieh. Preprocessing and Analyzing Educational Data Set Using x-Api for Improving Student’s Performance. – In: Proc. of Applied Electrical Engineering and Computing Technologies (AEECT’15), 2015, pp. 1-5.Search in Google Scholar