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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Amhaimar, Lahcen
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Computer Science & IT Engineering

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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 Bakhouyi, Abdellah Dehbi, Amine Khalidi, Abderrahim Nadir, Younes Tazouti, Yassine