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Mohamed, Khoual
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AI in Moroccan education: evaluating student acceptance using machine learning classification models Mohamed, Khoual; Zineb, Elkaimbillah; Zineb, Mcharfi; Bouchra, El Asri
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp452-462

Abstract

Personalized learning is becoming a reality in education thanks to the rise of AI. This study investigates the possibilities of AI within the realm of education, focusing on the individualization of the learning experience. The research is based on the responses of 395 students from various faculties in Morocco. The questionnaire aimed to assess the students’ opinions of AI, their level of knowledge, their previous experiences, and their perception of the application of AI within educational settings. Employing classification techniques such as decision trees (DT), multilayer perceptron (MLP), and random forests (RF), our aim was to predict the receptivity of AI in education. The findings highlight significant differences in how Moroccan students perceive AI, identifying key factors such as familiarity with the technology, ethical concerns, and perception of its potential impact on the learning experience. Classification models showed varied performance in anticipating these attitudes. This study highlights the critical importance of understanding students’ perspectives on AI in education. These findings offer crucial insights for education policymakers as well as designers of educational technology solutions in Morocco. The findings can be used as a guide to adapt the incorporation of AI into the education sector with discernment, taking into account students’ perceptions and preferences.
Building knowledge graph for relevant degree recommendations using semantic similarity search and named entity recognition Zineb, Elkaimbillah; Zineb, Mcharfi; Mohamed, Khoual; Bouchra, El Asri
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp463-474

Abstract

Career guidance is a critical and often daunting process, particularly during the transition from high school to higher education within the Moroccan education system. Faced with a vast array of university programs and career options, students frequently struggle to make informed decisions that align with their aspirations and skills. To address this challenge, our research introduces an innovative system that combines semantic similarity search with knowledge graph (KG) construction to enhance the precision and personalization of academic recommendations. By utilizing Sentence-BERT (SBERT) for semantic similarity, we generate embedding vectors that capture nuanced relationships between student profiles and degree descriptions. Subsequently, named entity recognition (NER) is applied to extract essential information such as skills, fields of study, and career opportunities from these profiles and descriptions. The extracted entities and their interrelationships are then structured into a coherent KG, stored in a Neo4j database, enabling efficient querying and visualization of complex data connections. This approach provides a transparent and explainable framework, ultimately delivering tailored advice that aligns with students’ individual needs and educational goals.
Co-Authors Bouchra, El Asri Zineb, Elkaimbillah Zineb, Mcharfi