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MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm.

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Online video education, including Massive Open Online Courses (MOOCs), has grown significantly. This study develops a reliable user experience evaluation model for online video teaching methods, demonstrating high accuracy.

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction

Background:

  • The rapid advancement of science and technology in China has transformed entertainment and education.
  • Internet-based industries, particularly online video, have emerged as significant sectors.
  • The COVID-19 pandemic has accelerated the adoption and recognition of convenient online video education.

Purpose of the Study:

  • To evaluate the effectiveness and user experience quality of online video teaching models.
  • To establish a comprehensive user experience quality evaluation method for domestic online video education.
  • To adapt and apply existing models to the specific context of Chinese online education.

Main Methods:

  • Selection of MOOC (Massive Open Online Course) as the primary case study platform.
  • Development of a user experience evaluation model using a fuzzy decision tree algorithm.
  • Focus on data sources, result accuracy, and reliability in model construction.

Main Results:

  • The developed model demonstrates high accuracy in evaluating user experience.
  • The model proves to be highly reliable for assessing online video education platforms.
  • The fuzzy decision tree algorithm effectively captures user experience nuances.

Conclusions:

  • The established evaluation model is suitable for domestic online video education.
  • MOOC platforms can be effectively analyzed using this user experience model.
  • The study validates the effectiveness and reliability of the proposed evaluation framework.