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September 5, 2025Smart Learning EnvironmentsOpen Access

Unveiling the reasons behind learners’ dropout from educational platforms: analyzing sentiment intensity throughout texts

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Authors

KBKhalid BenabbesMNMustapha NaïmiBHBrahim Hmedna

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Overview

This analysis reveals the impact of sentiment on dropout risk in e-learning, suggesting improved engagement strategies. It emphasizes the role of a Bayesian model in extracting sentiments from feedback.

Key Points

  • A novel Bayesian model for sentiment analysis predicts learner dropout risk effectively.
  • The model achieved 97.12% accuracy for long texts, outperforming traditional approaches significantly.
  • Sentiment analysis demonstrated a strong correlation with dropout rates, indicating its importance in learning environments.
  • Extracting meaning from learner feedback can help tailor content and improve overall engagement.

Cite This Study

Benabbes et al. (2025) studied this question.

synapsesocial.com/papers/68bb4de86d6d5674bcd0186chttps://doi.org/10.1186/s40561-025-00394-1
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