Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2058 2018 2018 Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt Nissreen El Saber Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt Aya Gamal Mohamed Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt Khalid A. Eldrandaly 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. 2023 2023 103 116 10.54216/FPA.130108 https://www.americaspg.com/articleinfo/3/show/2058