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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)
Nai, Soukaina
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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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.
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 Elbaghazaoui, Bahaa Eddine Rifai, Amal Sadiq, Abdelalim