Semantic Alignment in Disciplinary Tutoring System: Leveraging Sentence Transformer Technology

Authors

  • Rosana Abdoune Mouloud MAMMERI university of Tizi-Ouzou, Algeria
  • Lydia Lazib Mouloud MAMMERI university of Tizi-Ouzou, Algeria
  • Farida Dahmani-Bouarab Mouloud MAMMERI university of Tizi-Ouzou, Algeria
  • Nada Mimouni CNAM Conservatoire national des arts et métiers, France

DOI:

https://doi.org/10.4114/intartif.vol28iss75pp46-62

Keywords:

AI in education, Ontologies, Knowledge representation, Deep learning, machine learning, natural language processing, applications of AI

Abstract

In this work, we present a disciplinary e-tutoring system that integrates ONTO-TDM, an ontology designed for teaching domain modeling, with advanced transformer technology. Our primary objective is to enhance semantic similarity tasks within the system by fine-tuning a Sentence Transformer model. By carefully adjusting training parameters with a curated dataset of question-answer pairs focused on algorithms and data structures, we achieved a notable improvement in system performance. The Sentence Transformer model, combined with domain ontology, achieved an accuracy of 91%, a precision of 93%, a recall of 89%, and an F1-score of 90%, significantly surpassing the results of existing works. This methodology highlights the potential to deliver personalized support and guidance in tutoring scenarios. It effectively addresses the evolving needs of modern education by offering tailored answers and reducing the necessity for constant learner-tutor interaction, thereby improving the efficiency of educational support systems.

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Published

2024-10-19

How to Cite

Abdoune, R., Lazib, L., Dahmani-Bouarab, F., & Mimouni, N. (2024). Semantic Alignment in Disciplinary Tutoring System: Leveraging Sentence Transformer Technology. Inteligencia Artificial, 28(75), 46–62. https://doi.org/10.4114/intartif.vol28iss75pp46-62