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All Journal Bulletin of Electrical Engineering and Informatics
Abdelhamid Zouhair
Abdelmalek Essaâdi University
Electrical & Electronics Engineering

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Profiling student performance for multi-agent personalization in virtual reality Ghalia Mdaghri Alaoui; Ilhame Khabbachi; Abdelhamid Zouhair; El Mokhtar En-Naimi
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11118

Abstract

This study uses the open university learning analytics dataset (OULAD) to cluster student performance data to improve personalized learning. Three main aspects are the focus of the analysis: instructional involvement, behavior, and demographics. To create significant, comprehensible student profiles, the clustering algorithms k-means, k-modes, and k-prototypes were used for each dimension independently. In order to forecast student categories from input features, supervised classification models, such as support vector machines (SVMs) and random forests, were trained using these profiles as targets. Accuracy, F1-score, and cross-validation were used to assess the categorization models' performance. The outcomes demonstrate how well unsupervised and supervised learning strategies may be combined for adaptive learning. These profiles serve as a foundation for the future design of a multi-agent virtual reality (VR)-learning environment. In this envisioned system, specialized agents would handle behavioral adaptation, demographic personalization, and pedagogical coordination, offering a personalized learning experience tailored to each learner’s profile.
Fast and accurate cheat detection using deep learning algorithms Ilhame Khabbachi; Ghalia Mdaghri Alaoui; Abdelhamid Zouhair
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11207

Abstract

The rapid expansion of online education, accelerated by the global health crisis of 2020, has introduced significant challenges in maintaining academic integrity due to the absence of physical supervision during remote examinations. As digital learning becomes a permanent component of modern education, ensuring fairness and credibility in online assessments has become a critical concern for educational institutions. This study proposes an intelligent deep learning (DL)–based framework for detecting non-compliant behaviors during online examinations using standard webcam video streams. The proposed system integrates real-time video monitoring with automated behavioral analysis by combining object detection and classification models. In particular, you only look once version 5 (YOLOv5) is employed for efficient facial and object detection, while a convolutional neural network (CNN) is used to classify cheating and non-cheating behaviors from extracted visual features. Experimental results demonstrate that the integrated YOLOv5–CNN architecture achieves high detection accuracy and low inference latency, making it suitable for real-time and scalable deployment in online proctoring systems. By enabling objective and automated monitoring, the proposed framework contributes to strengthening fairness, transparency, and trust in digital assessment environments, thereby supporting the long-term sustainability of online education.
Co-Authors El Mokhtar En-Naimi Ghalia Mdaghri Alaoui Ilhame Khabbachi