How to cite this paper
Alshamaila, Y., Alsawalqah, H., Habib, M., Al-Madi, N., Faris, H., Alshraideh, M., Aljarah, I & Masadeh, R. (2024). An intelligent rule-oriented framework for extracting key factors for grants scholarships in higher education.International Journal of Data and Network Science, 8(2), 1325-1340.
Refrences
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Aulck, L., Nambi, D., & West, J. (2019). Using machine learning and genetic algorithms to optimize scholarship alloca-tion for student yield. In SIGKDD ‘19: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4-8).
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of ed-ucational data mining, 1(1), 3-17.
Cohen, W. W. (1995). Fast effective rule induction. In Machine learning proceedings 1995 (pp. 115-123). Morgan Kauf-mann.
Delima, A. J. P. (2019). Predicting scholarship grants using data mining techniques. Int. J. Mach. Learn. Comput, 9(4), 513-519.
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Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization.
Gaines, B. R., & Compton, P. (1995). Induction of ripple-down rules applied to modeling large databases. Journal of Intel-ligent Information Systems, 5, 211-228
Hassan, H., Anuar, S., & Ahmad, N. B. (2019). Students’ performance prediction model using meta-classifier approach. In Engineering Applications of Neural Networks: 20th International Conference, EANN 2019, Xersonisos, Crete, Greece, May 24-26, 2019, Proceedings 20 (pp. 221-231). Springer International Publishing.
Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C. J., & Yang, S. J. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learn-ing Environments, 28(2), 206-230.
Khruahong, S., & Tadkerd, P. (2020). Analysis of Scholarship Consideration Using J48 Decision Tree Algorithm for Data Mining. In Cooperative Design, Visualization, and Engineering: 17th International Conference, CDVE 2020, Bangkok, Thailand, October 25–28, 2020, Proceedings 17 (pp. 230-238). Springer International Publishing
Nechvoloda, L. V., & Shevchenko, N. Y. (2019). Fuzzy formalization and automation of the process of special academic scholarship distribution in higher educational institutions. Інформаційні технології і засоби навчання, (70,№ 2), 298-312..
Ranawaka, U. M., & Rajapakse, C. (2020, September). Predicting examination performance using machine learning ap-proach: A case study of the Grade 5 scholarship examination in Sri Lanka. In 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) (pp. 202-209). IEEE.
Rebai, S., Yahia, F. B., & Essid, H. (2020). A graphically based machine learning approach to predict secondary schools’ performance in Tunisia. Socio-Economic Planning Sciences, 70, 100724.
Rivas, A., Fraile, J. M., Chamoso, P., González-Briones, A., Rodríguez, S., & Corchado, J. M. (2019). Students perfor-mance analysis based on machine learning techniques. In Learning Technology for Education Challenges: 8th Interna-tional Workshop, LTEC 2019, Zamora, Spain, July 15–18, 2019, Proceedings 8 (pp. 428-438). Springer International Publishing.
Sharma, A., Ram, A., & Bansal, A. (2020). Feature extraction mining for student performance analysis. In Proceedings of ICETIT 2019: Emerging Trends in Information Technology (pp. 785-797). Springer International Publishing.
Sugiyarti, E., Jasmi, K. A., Basiron, B., Huda, M., Shankar, K., & Maseleno, A. (2018). Decision support system of schol-arship grantee selection using data mining. International Journal of Pure and Applied Mathematics, 119(15), 2239-2249.
Susilowati, T., Manickam, P., Devika, G., Shankar, K., Latifah, L., Muslihudin, M., ... & Maseleno, A. (2019). Decision support system for determining lecturer scholarships for doctoral study using CBR (Case-based reasoning) meth-od. International Journal of Recent Technology and Engineering, 8(1), 3281-3290.
Son, L. H., & Fujita, H. (2019). Neural-fuzzy with representative sets for prediction of student performance. Applied Intel-ligence, 49(1), 172-187.
Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N. (2020). Precision education with statistical learning and deep learning: a case study in Taiwan. International Journal of Educational Technology in Higher Education, 17, 1-13.
Wang, X., Mei, X., Huang, Q., Han, Z., & Huang, C. (2021). Fine-grained learning performance prediction via adaptive sparse self-attention networks. Information Sciences, 545, 223-240.
Alshanqiti, A., & Namoun, A. (2020). Predicting student performance and its influential factors using hybrid regression and multi-label classification. IEEE Access, 8, 203827-203844.
Al Nagi, E., & Al-Madi, N. (2020, October). Predicting students’ performance in online courses using classification tech-niques. In 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 51-58). IEEE.
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 commu-nication systems (ICICS) (pp. 104-109). IEEE.
Aulck, L., Nambi, D., & West, J. (2019). Using machine learning and genetic algorithms to optimize scholarship alloca-tion for student yield. In SIGKDD ‘19: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4-8).
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of ed-ucational data mining, 1(1), 3-17.
Cohen, W. W. (1995). Fast effective rule induction. In Machine learning proceedings 1995 (pp. 115-123). Morgan Kauf-mann.
Delima, A. J. P. (2019). Predicting scholarship grants using data mining techniques. Int. J. Mach. Learn. Comput, 9(4), 513-519.
Eibe, F., Hall, M. A., & Witten, I. H. (2016). The WEKA workbench. Online appendix for data mining: practical machine learning tools and techniques. In Morgan Kaufmann. San Francisco, California: Morgan Kaufmann Publishers.
Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization.
Gaines, B. R., & Compton, P. (1995). Induction of ripple-down rules applied to modeling large databases. Journal of Intel-ligent Information Systems, 5, 211-228
Hassan, H., Anuar, S., & Ahmad, N. B. (2019). Students’ performance prediction model using meta-classifier approach. In Engineering Applications of Neural Networks: 20th International Conference, EANN 2019, Xersonisos, Crete, Greece, May 24-26, 2019, Proceedings 20 (pp. 221-231). Springer International Publishing.
Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C. J., & Yang, S. J. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learn-ing Environments, 28(2), 206-230.
Khruahong, S., & Tadkerd, P. (2020). Analysis of Scholarship Consideration Using J48 Decision Tree Algorithm for Data Mining. In Cooperative Design, Visualization, and Engineering: 17th International Conference, CDVE 2020, Bangkok, Thailand, October 25–28, 2020, Proceedings 17 (pp. 230-238). Springer International Publishing
Nechvoloda, L. V., & Shevchenko, N. Y. (2019). Fuzzy formalization and automation of the process of special academic scholarship distribution in higher educational institutions. Інформаційні технології і засоби навчання, (70,№ 2), 298-312..
Ranawaka, U. M., & Rajapakse, C. (2020, September). Predicting examination performance using machine learning ap-proach: A case study of the Grade 5 scholarship examination in Sri Lanka. In 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE) (pp. 202-209). IEEE.
Rebai, S., Yahia, F. B., & Essid, H. (2020). A graphically based machine learning approach to predict secondary schools’ performance in Tunisia. Socio-Economic Planning Sciences, 70, 100724.
Rivas, A., Fraile, J. M., Chamoso, P., González-Briones, A., Rodríguez, S., & Corchado, J. M. (2019). Students perfor-mance analysis based on machine learning techniques. In Learning Technology for Education Challenges: 8th Interna-tional Workshop, LTEC 2019, Zamora, Spain, July 15–18, 2019, Proceedings 8 (pp. 428-438). Springer International Publishing.
Sharma, A., Ram, A., & Bansal, A. (2020). Feature extraction mining for student performance analysis. In Proceedings of ICETIT 2019: Emerging Trends in Information Technology (pp. 785-797). Springer International Publishing.
Sugiyarti, E., Jasmi, K. A., Basiron, B., Huda, M., Shankar, K., & Maseleno, A. (2018). Decision support system of schol-arship grantee selection using data mining. International Journal of Pure and Applied Mathematics, 119(15), 2239-2249.
Susilowati, T., Manickam, P., Devika, G., Shankar, K., Latifah, L., Muslihudin, M., ... & Maseleno, A. (2019). Decision support system for determining lecturer scholarships for doctoral study using CBR (Case-based reasoning) meth-od. International Journal of Recent Technology and Engineering, 8(1), 3281-3290.
Son, L. H., & Fujita, H. (2019). Neural-fuzzy with representative sets for prediction of student performance. Applied Intel-ligence, 49(1), 172-187.
Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N. (2020). Precision education with statistical learning and deep learning: a case study in Taiwan. International Journal of Educational Technology in Higher Education, 17, 1-13.
Wang, X., Mei, X., Huang, Q., Han, Z., & Huang, C. (2021). Fine-grained learning performance prediction via adaptive sparse self-attention networks. Information Sciences, 545, 223-240.