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Updated: Nov 20, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs).

Asra Khalid1, Karsten Lundqvist1, Anne Yates2

  • 1School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.

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|January 22, 2021
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Summary

A new online recommender system, NoR-MOOCs, effectively addresses information overload in Massive Open Online Courses (MOOCs). It offers accurate, scalable recommendations, overcoming limitations of traditional methods for dynamic learning environments.

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Area of Science:

  • Educational Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • Massive Open Online Courses (MOOCs) are increasingly popular, leading to information overload.
  • Existing recommender systems struggle with scalability, data sparsity, and dynamic updates in MOOC environments.
  • Traditional recommendation techniques are ill-suited for the rapidly growing and changing nature of MOOC data.

Purpose of the Study:

  • To propose a novel online recommender system, NoR-MOOCs, for Massive Open Online Courses.
  • To develop a system that is accurate, scalable, and handles incremental data updates.
  • To overcome the limitations of traditional recommendation algorithms in the MOOC context.

Main Methods:

  • Development of a novel online recommender system named NoR-MOOCs.
  • Extensive experimentation using the COCO dataset.
  • Empirical comparison against traditional KMeans and Collaborative Filtering algorithms.

Main Results:

  • NoR-MOOCs demonstrates superior performance compared to KMeans and Collaborative Filtering.
  • The system achieves significant improvements in predictive accuracy metrics.
  • The system shows significant improvements in classification accuracy metrics.

Conclusions:

  • NoR-MOOCs effectively addresses the challenges of recommending learning resources in MOOCs.
  • The proposed system offers a scalable and accurate solution for dynamic online learning environments.
  • NoR-MOOCs provides a viable alternative to traditional recommender systems for MOOC platforms.