A Comparative Study of Machine Learning Methods for Automatic Classification of Academic and Vocational Guidance Questions

Authors

  • Omar Zahour Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.
  • El Habib Benlahmar Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.
  • Ahmed Eddaouim Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.
  • Oumaima Hourrane Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

DOI:

https://doi.org/10.3991/ijim.v14i08.13005

Keywords:

Academic and vocational guidance, E-orientation, Machine learning, Automatic classification, Comparative study

Abstract


Academic and vocational guidance is a particularly important issue today, as it strongly determines the chances of successful integration into the labor market, which has become increasingly difficult. Families have understood this because they are interested, often with concern, in the orientation of their child. In this context, it is very important to consider the interests, trades, skills, and personality of each student to make the right decision and build a strong career path. This paper deals with the problematic of educational and vocational guidance by providing a comparative study of the results of four machine-learning algorithms. The algorithms we used are for the automatic classification of school orientation questions and four categories based on John L. Holland's Theory of RIASEC typology. The results of this study show that neural networks work better than the other three algorithms in terms of the automatic classification of these questions. In this sense, our model allows us to automatically generate questions in this domain. This model can serve practitioners and researchers in E-Orientation for further research because the algorithms give us good results.

Author Biographies

Omar Zahour, Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

Phd Student on Computer science ,Laboratory of Information Technology and Modeling ,Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

El Habib Benlahmar, Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

higher education teacher, computer science specialty ,Laboratory of Information Technology and Modeling ,Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

Ahmed Eddaouim, Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

higher education teacher, computer science specialty ,Laboratory of Information Technology and Modeling ,Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

Oumaima Hourrane, Laboratory of Information Technology and Modeling Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

Phd Student on Computer science ,Laboratory of Information Technology and Modeling ,Hassan II University, Faculty of Sciences Ben M'SIK, Casablanca, Morocco.

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Published

2020-05-20

How to Cite

Zahour, O., Benlahmar, E. H., Eddaouim, A., & Hourrane, O. (2020). A Comparative Study of Machine Learning Methods for Automatic Classification of Academic and Vocational Guidance Questions. International Journal of Interactive Mobile Technologies (iJIM), 14(08), pp. 43–60. https://doi.org/10.3991/ijim.v14i08.13005

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Section

Papers