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This study introduces a new online learning evaluation algorithm using big data on learning behaviors. It accurately reflects learning effects beyond just test scores, helping learners understand their progress and stay engaged.

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Area of Science:

  • Educational Technology
  • Data Science
  • Learning Analytics

Background:

  • Current online learning evaluations are limited, primarily relying on test and assignment scores.
  • These traditional methods fail to capture the full spectrum of learning effects and learner engagement.
  • A need exists for a more holistic approach to assess online learning outcomes.

Purpose of the Study:

  • To propose a comprehensive evaluation algorithm for online learning.
  • To leverage big data from diverse learning behaviors for accurate assessment.
  • To enhance learner self-awareness and maintain engagement through improved evaluation.

Main Methods:

  • Development of a novel evaluation algorithm integrating multiple learning behaviors.
  • Utilization of big data analytics to process learner interactions.
  • Application of information entropy theory to define and incorporate a conversion ratio.

Main Results:

  • The proposed algorithm provides a more accurate reflection of learning effects compared to traditional methods.
  • It incorporates a wider range of learner activities, including video views, exercises, exams, and discussions.
  • The algorithm demonstrates potential in improving learners' understanding of their academic standing.

Conclusions:

  • The new algorithm offers a more robust and comprehensive method for evaluating online learning.
  • By considering diverse behavioral data, it provides deeper insights into learner progress.
  • This approach can significantly contribute to maintaining learner motivation and interest in online educational environments.