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A framework for predicting academic orientation using supervised machine learning.

Hicham El Mrabet1,2, Abdelaziz Ait Moussa2

  • 1Regional Center for Education and Training Professions, Oujda, Morocco.

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Summary
This summary is machine-generated.

This study uses machine learning to predict student personality types, aiding academic and career guidance. The findings support personalized educational pathways for future professional success.

Keywords:
Classification algorithmsMachine learningSmart school guidance

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Area of Science:

  • Educational Psychology
  • Computer Science
  • Machine Learning

Background:

  • School guidance is crucial for student educational and professional choices.
  • The COVID-19 pandemic necessitates adapting guidance methods to student needs.
  • Effective guidance requires matching students with fields suiting their personality and skills.

Purpose of the Study:

  • To predict student potential for enhanced academic support.
  • To leverage machine learning for personalized academic guidance.
  • To develop a data-driven approach to student self-orientation.

Main Methods:

  • Utilizing supervised machine learning algorithms.
  • Employing classification techniques to analyze student traits.
  • Predicting personality types based on collected student data.

Main Results:

  • Machine learning effectively predicts student personality types.
  • The study provides a comprehensive framework for academic self-orientation.
  • Identified personality traits correlate with suitability for specific academic fields.

Conclusions:

  • Supervised machine learning offers a robust method for academic guidance.
  • Personalized prediction of student potential supports educational success.
  • This approach facilitates informed career choices and future employability.