2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 1 , PP: 103-116, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Nissreen El Saber 1 * , Aya Gamal Mohamed 2 , Khalid A. Eldrandaly 3
Doi: https://doi.org/10.54216/FPA.130108
Student academic performance can be affected by social, economic, and educational factors. Many research works studied these factors applying to different levels in the educational organizations’ models. The importance spans giving professional educational advice to vulnerable students, supporting the student’s development of special education-related skills, and encouraging students to handle their education challenges. For educational organizations, dealing with pandemics and other obstacles has proven to be essential for education sustainability. One way is to be proactive and use the power of exploring and discovering educational data to predict students’ performance and attitude. Mining educational data can benefit from Business Intelligence (BI) in visualizing, organizing, and extracting insights for student’s performance. Educational Data Mining (EDM) is used in this research to predict students' performance. A novel data fusion framework is introduced for Business Intelligence using educational data mining. This study aims to show the techniques that predict students' performance and the most effective methods for each of them. The proposed framework used the advantage of business intelligence concepts and tools to highlight the metrics providing better statistical and analytical understanding.
data mining , educational data mining , business intelligence , learning management systems , sustainability
[1] S. S., V. P. K. M. S., M. M. A., S. R. G. S., K. S., T. S. Mangalapalli, “Data Mining in Education: A Review of Current Practices,” https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152430632&doi=10.1109%2fICAIS56108.2023.10073932&partnerID=40&md5=8aa10dc18e9eb2473daed4f06eaea5d6, 2023.
[2] Anita Venugopal,Aditi Sharma,F. Abdul Munaim Al Rawas,Rama Devi S., Enhancing Fusion Teaching based Research from the Student Perspective, Fusion: Practice and Applications, Vol. 12 , No. 2 , (2023) : 109-119 (Doi : https://doi.org/10.54216/FPA.120209).
[3] A. 2016 Algarni and abdulmohsen algarni, “Data Mining in Education,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 7, pp. 456–461, Jun. 2016.
[4] M. M. Abuteir, A. Mustafa El-Halees, M. M. Abu Tair, and A. M. El-Halees, “Mining Educational Data to Improve Students’ Performance: A Case Study Arabic Sarcasm Sentiment Analysis View project Distributed Arabic Text Classification View project Mining Educational Data to Improve Students’ Performance: A Case Study,” vol. 2, no. 2, 2012,
[5] B. Wibawa, J. S. Siregar, D. A. Asrorie, and H. Syakdiyah, “Learning analytic and educational data mining for learning science and technology,” 2021, p. 060001. doi: 10.1063/5.0041844.
[6] Wafaa A. Saleh,Sherine M. Abdelkader,Heba Rashad,Amal Abdelgawad, The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives, American Journal of Business and Operations Research, Vol. 10 , No. 1 , (2023) : 25-41 (Doi : https://doi.org/10.54216/AJBOR.100103)
[7] R. A. Huebner, “A survey of educational data A survey of educational data-mining research mining research.”
[8] W. Xiao, P. Ji, and J. Hu, “A survey on educational data mining methods used for predicting students’ performance,” Engineering Reports, vol. 4, no. 5. John Wiley and Sons Inc, May 01, 2022. doi: 10.1002/eng2.12482.
[9] P. Kaur, M. Singh, and G. S. Josan, “Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector,” in Procedia Computer Science, Elsevier, 2015, pp. 500–508. doi: 10.1016/j.procs.2015.07.372.
[10] H. Pallathadka, S. Jain, S. Kamble, and K. Tongkachok, “Educational Data Mining: A Comprehensive Review and Future Challenges,” ECS Trans, vol. 107, no. 1, pp. 16129–16136, Apr. 2022, doi: 10.1149/10701.16129ecst.
[11] Á. Rocha, J. L. Reis, M. K. Peter, and Z. Bogdanović, “Marketing and Smart Technologies Proceedings of ICMarkTech 2019.” [Online]. Available: http://www.springer.com/series/8767
[12] I. D. Shetty, D. Shetty, and S. Roundhal, “Student Performance Prediction,” International Journal of Computer Applications Technology and Research, vol. 8, no. 5, pp. 157–160, Apr. 2019, doi: 10.7753/IJCATR0805.1003.
[13] M. F. Sikder, M. J. Uddin, and S. Halder, “Predicting students yearly performance using neural network: A case study of BSMRSTU,” in 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, Institute of Electrical and Electronics Engineers Inc., Nov. 2016, pp. 524–529. doi: 10.1109/ICIEV.2016.7760058.
[14] M. Pojon, “Using Machine Learning to Predict Student Performance,” 2017.
[15] A. U. Khasanah and H. Harwati, “Educational data mining techniques approach to predict student’s performance,” International Journal of Information and Education Technology, vol. 9, no. 2, pp. 115–118, Feb. 2019, doi: 10.18178/ijiet.2019.9.2.1184.
[16] J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Domínguez, “Analyzing and predicting students’ performance by means of machine learning: A review,” Applied Sciences (Switzerland), vol. 10, no. 3. MDPI AG, Feb. 01, 2020. doi: 10.3390/app10031042.
[17] M. D. S. Mussa, S. C. de Souza, E. F. da S. Freire, R. G. Cordeiro, and H. R. M. da Hora, “BUSINESS INTELLIGENCE IN EDUCATION: AN APPLICATION OF PENTAHO SOFTWARE,” Revista Produção e Desenvolvimento, vol. 4, no. 2, pp. 29–41, Jul. 2018, doi: 10.32358/rpd. 2018.v4.274.
[18] H. El Aouifi, M. El Hajji, Y. Es-Saady, and H. Douzi, “Predicting learner’s performance through video sequences viewing behavior analysis using educational data mining,” Educ Inf Technol (Dordr), vol. 26, no. 5, pp. 5799–5814, Sep. 2021, doi: 10.1007/s10639-021-10512-4.
[19] M. Sanchez Peralta, “Business Intelligence in E-Learning, a Case Study of an Ecuadorian University,” in 2018 XIII Latin American Conference on Learning Technologies (LACLO), IEEE, Oct. 2018, pp. 29–32. doi: 10.1109/LACLO.2018.00016.
[20] P. Strecht, L. Cruz, J. Mendes Moreira, C. Soares, J. Mendes-Moreira, and R. Abreu, “A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance,” 2015.
[21] A. Triayudi and W. O. Widyarto, “Educational Data Mining Analysis Using Classification Techniques,” J Phys Conf Ser, vol. 1933, no. 1, p. 012061, Jun. 2021, doi: 10.1088/1742-6596/1933/1/012061.
[22] R. Alamri and B. Alharbi, “Explainable Student Performance Prediction Models: A Systematic Review,” IEEE Access, vol. 9. Institute of Electrical and Electronics Engineers Inc., pp. 33132–33143, 2021. doi: 10.1109/ACCESS.2021.3061368.
[23] A. Abdulahi Hasan and H. Fang, “Data Mining in Education: Discussing Knowledge Discovery in Database (KDD) with Cluster Associative Study,” in 2021 2nd International Conference on Artificial Intelligence and Information Systems, New York, NY, USA: ACM, May 2021, pp. 1–6. doi: 10.1145/3469213.3471319.
[24] H. A. Abdelhafez and H. Elmannai, “Developing and Comparing Data Mining Algorithms That Work Best for Predicting Student Performance,” International Journal of Information and Communication Technology Education, vol. 18, no. 1, pp. 1–14, Feb. 2022, doi: 10.4018/ijicte.293235.
[25] G. Ben Brahim, “Predicting Student Performance from Online Engagement Activities Using Novel Statistical Features,” Arab J Sci Eng, vol. 47, no. 8, pp. 10225–10243, Aug. 2022, doi: 10.1007/s13369-021-06548-w.
[26] D. A. Shafiq, M. Marjani, R. A. A. Habeeb, and D. Asirvatham, “Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review,” IEEE Access, vol. 10. Institute of Electrical and Electronics Engineers Inc., pp. 72480–72503, 2022. doi: 10.1109/ACCESS.2022.3188767.
[27] S. M. Dol and Dr. P. M. Jawandhiya, “A Review of Data Mining in Education Sector,” Journal of Engineering Education Transformations, vol. 36, no. S2, pp. 13–22, Jan. 2023, doi: 10.16920/jeet/2023/v36is2/23003.
[28] Khyati Chaudhary,Gopal Chaudhary, A Decision Support System for Credit Risk Assessment using Business Intelligence and Machine Learning Techniques, American Journal of Business and Operations Research, Vol. 10 , No. 2 , (2023) : 32-38 (Doi : https://doi.org/10.54216/AJBOR.100204).
[29] S. Anardani, L. Sofyana STT, and A. Maghfur, “Analysis of business intelligence system design for student performance monitoring,” J Phys Conf Ser, vol. 1381, no. 1, p. 012015, Nov. 2019, doi: 10.1088/1742-6596/1381/1/012015.
[30] C. da R. Brito et al., EDUNINE2018 - II IEEE World Engineering Education Conference: the role of professional associations in contemporaneous engineer careers: proceedings: March 11 to 14, 2018, Buenos Aires, Argentina.
[31] A. Bansal, A. Singhal, Amity University. School of Engineering and Technology. Department of Computer Science and Engineering, Amity University, Institute of Electrical and Electronics Engineers. Uttar Pradesh Section, and Institute of Electrical and Electronics Engineers, Proceedings of the 7th International ConferenceConfluence 2017 on Cloud Computing, Data Science and Engineering: 12th-13th January 2017, Amity University, Noida, Uttar Pradesh, India.
[32] M. D. S. Mussa, S. C. de Souza, E. F. da S. Freire, R. G. Cordeiro, and H. R. M. da Hora, “BUSINESS INTELLIGENCE IN EDUCATION: AN APPLICATION OF PENTAHO SOFTWARE,” Revista Produção e Desenvolvimento, vol. 4, no. 2, pp. 29–41, Jul. 2018, doi: 10.32358/rpd. 2018.v4.274.
[33] W. Villegas-Ch, X. Palacios-Pacheco, and S. Luján-Mora, “A business intelligence framework for analyzing educational data,” Sustainability (Switzerland), vol. 12, no. 14, pp. 1–21, Jul. 2020, doi: 10.3390/su12145745.
[34] A. Ghyasi and H. Rashidi, “A New Approach Based on Business Intelligence and Bayesian Network for Analysis of Corporate Accounting Systems,” 2023.
[35] C. N. Villavicencio, J. J. E. Macrohon, X. A. Inbaraj, J. H. Jeng, and J. G. Hsieh, “Covid-19 prediction applying supervised machine learning algorithms with comparative analysis using Weka,” Algorithms, vol. 14, no. 7, Jul. 2021, doi: 10.3390/a14070201.
[36] S. Shaik, C. Shravya, K. Pravalika, and S. Subhani, “CITATIONS 78 READS 1,763,” 2019. [Online]. Available: https://www.researchgate.net/publication/363296423
[37] A. Çakır and B. Demirel, “A software tool for determination of breast cancer treatment methods using data mining approach,” J Med Syst, vol. 35, no. 6, pp. 1503–1511, 2011, doi: 10.1007/s10916-009-9427-x.
[38] J. D. Wark, “Power Up: Combining Behavior Monitoring Software with Business Intelligence Tools to Enhance Proactive Animal Welfare Reporting,” Animals, vol. 12, no. 13, p. 1606, Jun. 2022, doi: 10.3390/ani12131606.
