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Study Partners Recommendation for xMOOCs learners.

Bin Xu1, Dan Yang2

  • 1Computing Center, Northeastern University, Shenyang 110819, China.

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Summary
This summary is machine-generated.

To improve low completion rates in massive open online courses (MOOCs), this study recommends study partners using learner behavior and knowledge. This approach enhances student engagement and course completion compared to content-only methods.

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Area of Science:

  • Educational Technology
  • Computer Science
  • Social Computing

Background:

  • Massive Open Online Courses (MOOCs) offer global access to education but suffer from low completion rates.
  • A key factor in student attrition is the inability to resolve learning challenges through peer interaction.

Purpose of the Study:

  • To develop a study partner recommendation system for MOOCs.
  • To enhance student retention and course completion rates by facilitating peer support.

Main Methods:

  • Analyzed discussion forum messages to identify learner behavior patterns.
  • Classified learners into distinct groups based on their online interactions.
  • Employed a topic model to assess learners' knowledge awareness within the course content.
  • Constructed a social network based on forum activity to understand learner relationships.
  • Integrated behavioral features and knowledge awareness for personalized study partner recommendations.

Main Results:

  • The proposed recommendation method, utilizing both content and social network information, outperformed methods relying solely on content.
  • The system effectively identified and recommended suitable study partners, contributing to improved learning experiences.

Conclusions:

  • Leveraging social network information alongside content analysis significantly enhances study partner recommendation in MOOCs.
  • This approach offers a viable strategy to address low MOOC completion rates by fostering a more connected and supportive learning environment.