## 4 Results and Discussion
If future studies are conducted without considering all these issues, they might experience difficulties in ensuring that the developed systems are applicable. Success only in specific predictions might mislead students and provide incorrect information to the instructors and experts who are responsible for determining education policies. The spread of flipped classrooms, online education, and distance education during the COVID-19 period enabled the more effective recording of student data, logs, homework, quizzes, exams, feedback, and grades in many educational institutions, especially in HEI. Educational institutions could enable more comprehensive studies in this mobile era by measuring students' time on campus, their time on social media, and achievement records with volunteer students by developing special mobile software. Combining all obtained behavioral and educational data with demographic, personal, and cultural data will provide a more accurate prediction of student performance. Therefore, it will be possible to predict students' success in in-class and online courses more accurately by combining all the data, not only with questionnaires or online information. This will enable the generalization of AI and ML for both online and in-class courses, and artificial intelligence for student success in educational institutions will become more widespread. Several studies have demonstrated that regional, national, and cultural differences, education in a foreign language, socioeconomic effects, demographic situation, and the role of instructors could have significant effects on the same scales in predicting student performance [67; 68; 69]. In addition, the developments in e-learning systems provide significant outcomes [70; 71]. In this context, in the globalized world, creating a global education information consortium and the acquisition of data with the same criteria in different educational institutions worldwide will make reaching the goal non-specific and spread it across the globe. Moreover, it will undoubtedly create a tremendous source of information in determining students' performance in terms of success in the semester, at the end of the semester, and in the next semester, whether it is distance education, in-class education, open education, etc. Today's computer technology provides the infrastructure to analyze big data that are obtained. This will make deep learning and data mining more focused on student performance prediction studies and might provide more accurate and applicable results than the results obtained so far. Therefore, the drop-out and end-of-term predictions mentioned above as research topics will be provided as support points to the students. However, the high prediction success of in-term and risk estimations and the implementation of an early warning system will provide the most significant contribution. This will allow students to observe the risk levels and expected success level at the end of the term after enrolling in the course, and the students' interest in the course will be increased. Furthermore, instead of reaching general judgments in predicting students' performance levels, it would be possible to implement personalized and individualized performance evaluation systems and to further the meta-analysis results of [PERSON] and [PERSON]'s [72] studies for HEI.