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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 o...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168356/ https://www.ncbi.nlm.nih.gov/pubmed/35692509 http://dx.doi.org/10.1007/s12652-022-03909-7 |
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author | El Mrabet, Hicham Ait Moussa, Abdelaziz |
author_facet | El Mrabet, Hicham Ait Moussa, Abdelaziz |
author_sort | El Mrabet, Hicham |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9168356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-91683562022-06-07 A framework for predicting academic orientation using supervised machine learning El Mrabet, Hicham Ait Moussa, Abdelaziz J Ambient Intell Humaniz Comput Original Research 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. Springer Berlin Heidelberg 2022-06-06 /pmc/articles/PMC9168356/ /pubmed/35692509 http://dx.doi.org/10.1007/s12652-022-03909-7 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research El Mrabet, Hicham Ait Moussa, Abdelaziz A framework for predicting academic orientation using supervised machine learning |
title | A framework for predicting academic orientation using supervised machine learning |
title_full | A framework for predicting academic orientation using supervised machine learning |
title_fullStr | A framework for predicting academic orientation using supervised machine learning |
title_full_unstemmed | A framework for predicting academic orientation using supervised machine learning |
title_short | A framework for predicting academic orientation using supervised machine learning |
title_sort | framework for predicting academic orientation using supervised machine learning |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168356/ https://www.ncbi.nlm.nih.gov/pubmed/35692509 http://dx.doi.org/10.1007/s12652-022-03909-7 |
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