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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.

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.

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Educational and vocational guidance is a particularly important issue today, as it strongly determines the chances of successful professional integration into the increasingly difficult labor market. Families have understood this well since they have been interested, in the educational orientation of their child. In this sense, we have set up a system for classifying questions of educational and professional orientation in based on Holland's test using the BERT method. Text classification, particularly the classification of questions is a basic task in natural language processing which is a very profound concept in the field of artificial intelligence. As the most abundant data in the world today is in the form of texts, having a powerful word processing system is essential and is more than just a necessity. Recently, Transformers models such as Bidirectional encoder representations of transformers or BERT are a very popular NLP models known for producing remarkable results compared to other methods in a wide variety of NLP tasks. In this article, we demonstrate how to implement a multi-class classification using BERT. In particular, we explain the classification of questions concerning the field of educational and vocational guidance following the RIASEC typology of Holland. Our model allows us to obtain the category of each input question. In our case, we define four classes (Activity, Occupations, Abilities, and Personality) for the set of questions, which constitute our data set. The results of this approach demonstrate that our model achieves competitive performance.