Prediction of Higher Education Students Academic Success Levels Based on Personality Traits

The academic success of students takes in a crucial part in academic institutions since it is a factor measure to identify the performance of an institution. Timely identification of students' CGPA range can reduce concerns about students’ academic life and it will help to improve the success and avoid the risks. The usage of machine learning techniques for prediction purposes has increased recently. In this paper, we focus the study on big five personality model used for determining the behavior of a person and where an individual will lie on the range for each of the five traits; using machine learning techniques to estimate a student's degree of academic accomplishment. In this work, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and Support Vector Classifier (SVC) have been used as machine learning techniques. The results were obtained at a recognition rate for accuracy of 90.2% by the Decision Tree Classification algorithm.