A framework for predicting academic orientation using supervised machine learning

School guidance is declared an integral part of the education and training process, as it accompanies students in their educational and professional choices. Accordingly, the current situation in light of the Covid-19 epidemic requires a reconsideration of school guidance together with the methods of accompanying the student to choose the field that suits his/her personality, knowledge qualifications, perceptual and intellectual skills in order to achieve an excellent educational level that enables the learner to work in future professions. The current study aims to predict a student's potential and provide support for academic guidance. This paper emphasizes the importance of supervised machine learning and classification algorithms to predict the personality type based on student traits. Based on the information gathered, the results of this study indicate that it contributes significantly to providing a comprehensive approach to support academic self-orientation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.