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In this paper we use ml algorithms in order to predict the performance of students, taking into account both past semester grades and socioeconomic factors. A decade of research work conducted between 2010 and november 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the. Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic.
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The results show that applying ml algorithms on balanced data and feature selection based on their frequencies can enhance the classifier’s performance to predict the possibility of students’. Feature engineering, the process of modification and selection of. The study directs future researchers to achieve highly accurate prediction results in different scenarios using different available inputs or techniques
After determining the most important variables resulted from each method in the previous section, we will use those selected variables to build multiples linear regression models, and then we will.
Abstract—this research aims to develop machine learning models for students' academic performance and study strategy prediction which could be generalized to all courses in higher. Accurately predicting students’ future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students’ on. Nt machine learning methods were used in this thesis Ion, decision trees, and naïve bayes classification
