Analyzing University Dropout Rates in E-Learning and the Potential of Artificial Intelligence to Reduce Them: A Case Study of French Universities
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".