In this work, machine learning techniques are deemed to predict student academic performances in their historical performance of Final Grades (FGs). Acceptance of Technology enabled the teaching-learning processes, as it has become a vital element to perceive the goal of academic quality. Research is improving and growing fast in Educational Data Mining (EDM) due to many students' information. Researchers urge to invent valuable patterns about students' learning behavior using their data that needs to be adequately processed to transform it into helpful information. This paper proposes a prediction model of students' academic performances with new data features, including student's behavioral features, Psychometric, family support, learning logs via e-learning management systems, and demographic information. In this paper, data collection and pre-processing are firstly conducted following the grouping of students with similar patterns of academic scores. Later, we selected the applicable supervised learning algorithms, and then the experimental work was implemented. The performance of the student's predictive model assessment is comprised of three steps: First, the critical Feature selection approach is evaluated. Second, a set of renowned classifiers are trained and tested. Third, ensemble meta-based models are improvised to boost the accuracy of the classifier. Subsequently, the present study is associated with the solutions that help the students evaluate and improve their academic performance with a glimpse of their historical grades. Ultimately, the results were produced and evaluated. The results showed the effectiveness of our proposed framework in predicting students' academic performance.

In this work, machine learning techniques are deemed to predict student academic performances in their historical performance of Final Grades (FGs). Acceptance of Technology enabled the teaching-learning processes, as it has become a vital element to perceive the goal of academic quality. Research is improving and growing fast in Educational Data Mining (EDM) due to many students' information. Researchers urge to invent valuable patterns about students' learning behavior using their data that needs to be adequately processed to transform it into helpful information. This paper proposes a prediction model of students' academic performances with new data features, including student's behavioral features, Psychometric, family support, learning logs via e-learning management systems, and demographic information. In this paper, data collection and pre-processing are firstly conducted following the grouping of students with similar patterns of academic scores. Later, we selected the applicable supervised learning algorithms, and then the experimental work was implemented. The performance of the student's predictive model assessment is comprised of three steps: First, the critical Feature selection approach is evaluated. Second, a set of renowned classifiers are trained and tested. Third, ensemble meta-based models are improvised to boost the accuracy of the classifier. Subsequently, the present study is associated with the solutions that help the students evaluate and improve their academic performance with a glimpse of their historical grades. Ultimately, the results were produced and evaluated. The results showed the effectiveness of our proposed framework in predicting students' academic performance.

In this work, machine learning techniques are deemed to predict student academic performances in their historical performance of Final Grades (FGs). Acceptance of Technology enabled the teaching-learning processes, as it has become a vital element to perceive the goal of academic quality. Research is improving and growing fast in Educational Data Mining (EDM) due to many students' information. Researchers urge to invent valuable patterns about students' learning behavior using their data that needs to be adequately processed to transform it into helpful information. This paper proposes a prediction model of students' academic performances with new data features, including student's behavioral features, Psychometric, family support, learning logs via e-learning management systems, and demographic information. In this paper, data collection and pre-processing are firstly conducted following the grouping of students with similar patterns of academic scores. Later, we selected the applicable supervised learning algorithms, and then the experimental work was implemented. The performance of the student's predictive model assessment is comprised of three steps: First, the critical Feature selection approach is evaluated. Second, a set of renowned classifiers are trained and tested. Third, ensemble meta-based models are improvised to boost the accuracy of the classifier. Subsequently, the present study is associated with the solutions that help the students evaluate and improve their academic performance with a glimpse of their historical grades. Ultimately, the results were produced and evaluated. The results showed the effectiveness of our proposed framework in predicting students' academic performance.