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All Journal Journal of Education and Learning (EduLearn) Indonesian Journal of Electrical Engineering and Computer Science
El Faddouli, Nour-eddine
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Humanities Education Library & Information Science Social Sciences Other

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Emerging approaches of artificial intelligence tools for distance learning: a review Faouzi, Ghita; Amrous, Naila; El Faddouli, Nour-Eddine; Khabouze, Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1219-1230

Abstract

Learning management system (LMS) is the best way to deliver educational content in the context of higher education, by settings students worldwide with high-quality educational material. This paper principally seeks to examine the use of e-learning platforms in the last years from 2019 to 2023, which has coincided with the pandemic period, by elucidating the benefits and limitations of e-learning platforms, analyzing the real-world artificial intelligence (AI) algorithms used and their operating context. A comprehensive literature search was conducted on different electronic databases to identify relevant studies related to e-learning and AI tools used during this period by applying inclusion, exclusion criteria and preferred reporting items for systematic reviews and meta-analysis (PRISMA) process. Based on this review the tools were necessary social media and free communication platforms that offer the flexibility and build autonomy to students. On the other hand, many challenges are arisen due to the lack of experience in the term of using those tools or due to technical problems, for this reason, the use of AI tools to enhance learning experience still one of the approved solutions.
A cognitive level evaluation method based on machine learning approach and Bloom of taxonomy for online assessments Chanaa, Abdessamad; El Faddouli, Nour-eddine
Journal of Education and Learning (EduLearn) Vol 18, No 2: May 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v18i2.20948

Abstract

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners’ cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners’ cognitive levels during the online learning process. However, most of the currently used techniques for evaluating cognitive levels rely on labour-intensive and time-consuming manual coding. In this study, we explore the machine learning (ML) algorithms and taxonomy of Bloom’s cognitive levels to explore features that affect learner’s cognitive level in online assessments and the ability to automatically predict learner’s cognitive level and thus, come up with a recommendation or pedagogical intervention to improve learner’s acquisition. The analysis of 15,182 learners’ assessments of a specific learning concept affirms the effectiveness of our approach. We attain an accuracy of 82.21% using ML algorithms. These results are very encouraging and have implications for how automated cognitive-level analysis tools for online learning will be developed in the future.
Co-Authors Amrous, Naila Chanaa, Abdessamad Faouzi, Ghita Khabouze, Mostafa