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August 20, 2025F1000ResearchOpen Access

Personalised and Collaborative Learning Experience (PCLE) Framework for AI-driven Learning Management System (LMS)

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Authors

CWClaireta Tang WeilingLLLew Sook LingOYOoi Shih Yin

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Overview

This study demonstrates collaborative filtering improves course recommendations in AI-driven e-learning systems, suggesting better student engagement.

Key Points

  • Collaborative filtering improved course recommendations, enhancing student engagement in e-learning environments.
  • K-Nearest Neighbours outperformed other models on the Coursera dataset, achieving superior accuracy throughout the study.
  • Machine learning models like KNN, SVD, and NCF were used to evaluate model performance on two education-related datasets.
  • Findings highlight the importance of dataset characteristics on algorithm effectiveness, underscoring future research potential for diverse contexts.

Cite This Study

Weiling et al. (2025) studied this question.

synapsesocial.com/papers/68af4cd3ad7bf08b1ead5fb0https://doi.org/10.12688/f1000research.166248.1
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