Analyzing University Dropout Rates in E-Learning and the Potential of Artificial Intelligence to Reduce Them: A Case Study of French Universities
DOI:
https://doi.org/10.56294/dm2025468Keywords:
E-learning, Intelligent Tutoring Systems, Personalized E-learning, Artificial Intelligence, E-learning dropout rates, Sorbonne Paris Cite University, Case study in French university, Multiple Correspondence Analysis, Remote learningAbstract
During the COVID-19 pandemic, students worldwide faced unprecedented disruption, forcing educators to swiftly transition to remote teaching. In French universities, strong political support at both national and institutional levels facilitated the deployment of digital tools such as learning management systems (e.g., Moodle), collaborative platforms (e.g., Google Meet, Microsoft Teams, Zoom), and social networks. While this shift highlighted the importance and critical role of digital technologies in education, it also raised significant concerns about the quality of online learning, the learning process, and the assessment of knowledge and skills. This case study explores the perceptions of students at Sorbonne Paris Cite Universities regarding the effectiveness of e-learning. Results from a Multiple Correspondence Analysis indicate that system usability and its positive impact on learning are key to the perceived success of e-learning. However, university dropout rates in this context stem from a combination of factors influencing student engagement. Addressing these challenges requires comprehensive solutions involving multiple stakeholders, including organizations, educators, and learners.
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