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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics
El Guemmat, Kamal
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementation of suitable information technology governance frameworks for Moroccan higher education institutions Abdelilah, Chahid; Ahriz, Souad; El Guemmat, Kamal; Mansouri, Khalifa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3116-3126

Abstract

This article aims to present formal governance practices of information technology adapted to the general context of Moroccan universities. The study consists of two main phases: the conceptualization phase and the operationalization phase. During the conceptualization phase, the authors reviewed relevant literature on best practices and their associated frameworks in higher education institutions (HEIs). The results revealed that universities had varying levels of maturity in terms of good practices and often used multiple information system frameworks, which can cause organizational and technical problems. In order to find a solution to this situation, the authors conducted in-depth interviews with chief information officers (CIOs) and university officials from four Moroccan universities during the operationalization phase. These interviews enabled them to propose an effective baseline of best practices and an algorithmic approach to assist managers in choosing between two combinations of frameworks that cover all the mechanisms of the baseline. This solution would enable optimal, agile, and easy-to-implement information technology governance in Moroccan universities while avoiding the multiplicity of frameworks.
Enhancing convolutional neural network based model for cheating at online examinations detection Ouahabi, Sara; Aboudihaj, Rihab; Sael, Nawal; El Guemmat, Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp843-852

Abstract

In the last few years, e-learning has revolutioning education, giving students access to diverse and adaptable on-line resources, but it has also face a major challenge: cheating on online exams. Students now use variant cheating methods include consulting unauthorized documents, communicating with others during the exam, searching for information on the internet. Combating these cheating practices has become crucial to preserving the integrity of academic assessments. In this context, artificial intelligence (AI) has emerged as an essential tool for mitigating this fraudulent behavior. Equipped with advanced machine learning capabilities, AI can examine a wide range of data to detect student suspicious behavior. This study develops an approach based on a convolutional neural network (CNN) model designed to detect cheating by analyzing candidates' head movements during online exams. By exploiting the FEI dataset, this model achieves an interesting accuracy of 97.28%. In addition, we compare this model to the well-known transfer learning models used in the literature namely, ResNet50, VGG16, DenseNet21, MobileNetV2, and EfficientNetB0 demonstrating the out performance of our approach in detecting cheating during online exams.
Predictive insights into student online learning adaptability: elevating e-learning landscape El Ghali, Mohamed; Atouf, Issam; El Guemmat, Kamal; Talea, Mohamed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp892-902

Abstract

In Morocco’s rapidly transforming educational landscape, this study delves into students’ adaptability to online learning environments by integrating sophisticated artificial intelligence (AI) algorithms and hyperparameter optimization techniques. This research uses the comprehensive “online learning adaptivity” dataset to identify pivotal factors influencing student flexibility and effectiveness in e-learning platforms. We applied various AI models, with a particular emphasis on the CatBoost classifier, which exhibited exceptional predictive performance, achieving an accuracy rate near 98%. This high precision in predicting student adaptiveness offers essential insights into tailoring digital education systems. The results underscore the significant potential of machine learning technologies to enhance educational methodologies by catering to the diverse needs of students. Such capabilities are instrumental for educators and policymakers dedicated to refining e-learning strategies that effectively accommodate individual learning styles, ultimately improving the broader educational outcomes in Moroccan tertiary education. These findings advocate for a more nuanced understanding of the interplay between student behavior and technological solutions, providing a roadmap for developing more responsive and effective educational platforms.
Developing digital capabilities through IT governance: a PLS-SEM analysis in Moroccan higher education institutions Chahid, Abdelilah; Ahriz, Souad; El Guemmat, Kamal; Mansouri, Khalifa
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8182

Abstract

This study examines the impact of information technology governance (ITG) on digital transformation (DT) in Moroccan higher education institutions, particularly emphasising the mediating role of absorptive capacity. Utilising a rigorous methodological framework, the research analyzes data collected from 110 staff members using structural equation modelling with the SmartPLS tool. The goal is to explore the complex dynamics between ITG practices and DT capability. The findings reveal a positive and statistically significant relationship between ITG mechanisms and absorptive capacity (AC) and between the latter and the success of DT. The study also identifies AC as a crucial mediator between ITG and digital capability (DC). It suggests universities should strengthen their AC and adopt open policies to increase their innovative potential. This contribution enriches the existing literature by empirically confirming the influence of certain IT governance variables on DC within Moroccan universities, offering valuable insights for academic researchers and practitioners involved in IT governance strategies and DT.
Co-Authors Abdelilah, Chahid Aboudihaj, Rihab Ahriz, Souad ATOUF, ISSAM Chahid, Abdelilah El Ghali, Mohamed Mansouri, Khalifa Ouahabi, Sara Sael, Nawal Talea, Mohamed