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DOI: 10.14569/IJACSA.2019.0101072
PDF

Automatic Classification of Academic and Vocational Guidance Questions using Multiclass Neural Network

Author 1: Omar Zahour
Author 2: El Habib Benlahmar
Author 3: Ahmed Eddaoui
Author 4: Oumaima Hourrane

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 10, 2019.

  • Abstract and Keywords
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Abstract: The educational and professional orientation is an essential phase for each student to succeed in his life and his curriculum. In this context, it is very important to take into account the interests, occupations, skills, and the type of each student's personalities to make the right choice of training and to build a solid professional outline. This article deals with the problematic of educational and vocational orientation and we have developed a model for automatic classification of orientation questions. “E-Orientation Data” is a machine learning method based on John L. Holland’s Theory of RIASEC typology that uses a multiclass neural network algorithm. This model allows us to classify the questions of academic and professional orientation according to their four categories, thus allows automatic generation of questions in this area. This model can serve E-Orientation practitioners and researchers for further research as the algorithm gives us good results.

Keywords: Academic and vocational guidance; multiclass neural network; e-orientation; machine learning; Holland’s theory

Omar Zahour, El Habib Benlahmar, Ahmed Eddaoui and Oumaima Hourrane, “Automatic Classification of Academic and Vocational Guidance Questions using Multiclass Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 10(10), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101072

@article{Zahour2019,
title = {Automatic Classification of Academic and Vocational Guidance Questions using Multiclass Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101072},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101072},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {10},
author = {Omar Zahour and El Habib Benlahmar and Ahmed Eddaoui and Oumaima Hourrane}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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