A framework for predicting academic orientation using supervised machine learning

被引：7
作者：
El Mrabet H. [1 ,2 ]
Ait Moussa A. [2 ]
机构：
[1] Regional Center for Education and Training Professions, Oujda
[2] Department of Computer Sciences, Faculty of Sciences, Mohammed First University, Oujda
来源：
Journal of Ambient Intelligence and Humanized Computing | 2023年 / 14卷 / 12期
关键词：
Classification algorithms; Machine learning; Smart school guidance;
D O I：
10.1007/s12652-022-03909-7
中图分类号：
TP181 [自动推理、机器学习];
学科分类号：
摘要：
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.
引用
收藏
页码：16539 / 16549
页数：10
相关论文
共 31 条
[1]  
Asthana P., Hazela B., Applications of machine learning in improving learning environment, Multimed Big Data Comput IoT Appl, (2019)
[2]  
Asthana P., Hazela B., Applications of machine learning in improving learning environment, Multimedia Big Data Computing for Iot Applications. Intelligent Systems Reference Library, 163, (2020)
[3]  
OrieB, a linguistic CRS for supporting decision making in academic orientation, World Scientific Proceedings Series on Computer Engineering and Information Science Computational Intelligence in Decision and Control, Chapter, 2, pp. 841-846, (2008)
[4]  
Chung J.Y., Lee S., Dropout early warning systems for high school students using machine learning, Child Youth Serv Rev, 96, pp. 346-353, (2019)
[5]  
Cristianini N., Shawe-Taylor J., An introduction to support vector machines and other kernel-based learning methods, (2000)
[6]  
Big Five Factor Model, Theory and Structure, pp. 559-566, (2015)
[7]  
Delic-Zimic A., Gadzo N., Implementation of ICT in education, Advanced Technologies, Systems, and Applications II. IAT 2017. Lecture Notes in Networks and Systems, 28, (2018)
[8]  
El Mrabet H., Moussa A.A., Smart school guidance and vocational guidance system through the internet of things, Proceedings of the 2Nd International Conference on Networking, Information Systems & security—NISS19, (2019)
[9]  
El Mrabet H., Ait Moussa A., IoT-school guidance: A holistic approach to vocational self-awareness & career path, Educ Inf Technol, 26, pp. 5439-5456, (2021)
[10]  
El Mrabet M.A., El Makkaoui K., Faize A., Supervised machine learning: A survey, 4Th International Conference on Advanced Communication Technologies and Networking (Commnet, (2021)
1234