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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Elbaghazaoui, Bahaa Eddine
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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An in-depth analysis of a tutoring solution by digital technology Nai, Soukaina; Rifai, Amal; Sadiq, Abdelalim; Elbaghazaoui, Bahaa Eddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4058-4073

Abstract

In Morocco, the dropout rate in primary and secondary education remains high due to environmental, social, familial, and educational factors. To address this issue, students rely on private tutoring or online platforms. However, socio-economic disparities make private tutoring inaccessible to many, while technical and pedagogical challenges limit the effectiveness of online platforms, deepening educational inequalities. This article proposes a nationwide participatory tutoring approach involving educational administration and teachers to ensure equitable and quality learning. We analyze existing models to identify their limitations and propose a structured tutoring system tailored to different student profiles. This system is based on a specific algorithm that defines skill assessment, remediation, and progress tracking. Unified modeling language UML is used to structure and present our approach in detail. Then, we compare current Moroccan platforms, particularly Massar, with our system, evaluating student engagement, pedagogical monitoring, curriculum alignment, and remediation effectiveness. Finally, we discuss our results, highlighting our system’s potential to reduce learning gaps, improve education, and significantly decrease the dropout rate in Morocco.
Artificial intelligence predictive modeling for educational indicators using data profiling techniques Nai, Soukaina; Elbaghazaoui, Bahaa Eddine; Rifai, Amal; Sadiq, Abdelalim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3063-3073

Abstract

In Morocco, the escalating challenges in the education sector underscore the necessity for precise predictions and informed decision-making. Effective management of the education system depends on robust statistical data, which is crucial for guiding decisions, refining policies, and improving both the quality and accessibility of education. Reliable indicators are vital for ensuring efficiency, equity, and accuracy in educational planning and decision- making. Without dependable data, implementing effective policies, addressing the needs appropriately, and achieving positive outcomes becomes difficult. This paper aims to identify the optimal machine learning model for analyzing educational indicators by comparing a range of advanced models across a comprehensive set of metrics. The objective is to determine the most effective model for profiling relevant information and addressing predictive challenges with high accuracy.
Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram Monitoring Signals Fauzi, Muhammad; Yulianto, Endro; Irianto, Bambang Guruh; Luthfiyah, Sari; Triwiyanto, Triwiyanto; Shankhwar, Vishwajeet; Elbaghazaoui, Bahaa Eddine
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 3 (2022): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i3.149

Abstract

Physical activity is an activity of body movement by utilizing skeletal muscles that are carried out daily. One form of physical activity is exercise which aims to improve health and fitness. Parameters related to health and wellness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. The authors used electromyographic (EMG) signals to measure muscle fatigue by monitoring changes in electrical muscle activity. This study aims to analyze the effect of muscle fatigue on cardiac signals during subjects perform physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. The authors use MPF, MDF, and MNF to get more detailed measurement results and perform a T-test. The test results showed a significant value (p-value <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628, indicating a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the device in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.
Proposal for a learner model adapted for personalized tutoring based on IMS-LIP and PAPI Nai, Soukaina; Rifai, Amal; Sadiq, Abdelalim; Elbaghazaoui, Bahaa Eddine
International Journal of Evaluation and Research in Education (IJERE) 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/ijere.v14i6.33366

Abstract

The effectiveness of online tutoring systems largely depends on their ability to adapt to the individual needs of learners, to personalize learning activities, and to provide immediate and effective assessment and remediation. This effectiveness can only be ensured if accurate information is available regarding learners’ progress and learning profiles. In this article, we aim to propose a learner model tailored to the specificities of our academic support system, incorporating learning functions that enable personalized tutoring based on students’ needs. For this purpose, this study began with a literature review of existing learner models. We focused on five representative samples of the most widely used learner models in current learning systems: instructional management system-learner information package (IMS-LIP), public and private information for learners (PAPI), CARCHIOLO, knowledge on demand (KOD), and learner model for personalized adaptation (LMPA). We examined their characteristics and then compared them based on the following criteria: adaptability, user preferences, personalized learning, pedagogical requirements, assessment, and remediation, to evaluate their potential for integration into our system. The study revealed that these models present several limitations, which led us to propose a new learner model based on the PAPI and IMS-LIP standards. This proposal incorporates a semantic ontological structure that categorizes learner characteristics into six domains: preferences, pedagogy, administration, identification, learning, and assessment. The proposed model represents a promising solution for adapting learning processes to individual learner profiles, thereby fostering more effective and engaging educational experiences.
Co-Authors Bambang Guruh Irianto Endro Yulianto Luthfiyah, Sari Muhammad Fauzi Nai, Soukaina Rifai, Amal Sadiq, Abdelalim Shankhwar, Vishwajeet