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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE)
Dehbi, Amine
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Computer Science & IT Education Engineering

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Education and smart technologies: towards a new pedagogical paradigm Dehbi, Amine; Bakhouyi, Abdellah; Khaddar, Al Mahdi; Talea, Mohamed
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i1.30470

Abstract

Smart education, a new field of technology related to education, has emerged as a unique response to current educational challenges. This is becoming increasingly important for academic progress and aligns with the transformative impact of technology. This study addresses the transformative impact of smart technologies on education, focusing on the integration of the internet of things, big data, and artificial intelligence. Through a bibliometric and content analysis based on Scopus and Web of Science databases, we identify the most active researchers, leading universities, and the countries that contribute most significantly to the field of smart education. The findings reveal a significant increase in related publications, highlighting the growing importance of these technologies in enhancing teaching and learning experiences. The study shows the advantages and challenges of adopting such technologies, providing insights into their practical applications and the future direction of educational innovations. Integrating smart technologies in education is crucial for improving quality of life and academic outcomes, necessitating further research and development to fully realize their potential. This research contributes to the understanding of technological impacts on education and supports the development of strategies for their effective implementation.
Enhancing learning outcomes in smart education: a supervised machine learning predictive analytics model for course completion Bakhouyi, Abdellah; Dehbi, Amine; Amhaimar, Lahcen; Tazouti, Yassine; Nadir, Younes; Khalidi, Abderrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4711-4721

Abstract

Predictive analytics have become increasingly capable of delivering actionable and accessible feedback to enhance teacher performance to enhance student outcomes in higher education. This study introduces a supervised machine learning predictive model designed to forecast the duration required to complete a course in a video learning environment using a dataset of 8,665 statements from 490 students from National Higher School of Art and Design at Hassan II University in Casablanca over six academic years (2019-24). This paper analyzes decision trees (DT), random forest (RF), support vector machines (SVM), gradient boosting (GB), and linear regression (LR) techniques. The CMI-5 standard and JSON format are used to automatically transfer learning activity data from the learning management system (LMS) to the learning record store (LRS). The results indicate that DT, RF, and GB achieved 100 percent predictor accuracy.
Co-Authors Amhaimar, Lahcen Bakhouyi, Abdellah Khaddar, Al Mahdi Khalidi, Abderrahim Nadir, Younes Talea, Mohamed Tazouti, Yassine