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Summary
This summary is machine-generated.

This study introduces an entropy-based indicator to analyze learner behavior trends in massive open online courses (MOOCs). The findings show this indicator, combined with behavioral features, significantly improves the prediction of academic performance.

Keywords:
MOOCsacademic performance predictiondata mining

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Area of Science:

  • Educational Technology
  • Data Science
  • Learning Analytics

Background:

  • Massive Open Online Courses (MOOCs) offer flexible and accessible learning opportunities, generating vast amounts of learner log data.
  • Analyzing learner behavior within MOOCs is crucial for understanding academic performance and improving educational strategies.
  • Data mining techniques are increasingly applied to MOOC data to uncover patterns and predict outcomes.

Purpose of the Study:

  • To develop and validate an entropy-based indicator for quantifying learner behavior change trends in MOOCs.
  • To explore the relationship between these behavior change trends and learners' academic performance.
  • To enhance the prediction of academic performance by integrating novel behavioral features.

Main Methods:

  • Utilized learner behavior log data from 12 MOOC courses.
  • Proposed an entropy-based indicator to quantify behavior change trends.
  • Constructed a set of behavioral features and combined them with the entropy indicator.
  • Employed four benchmark models for academic performance prediction and compared results.

Main Results:

  • The entropy-based indicator effectively represents trends in learner behavior.
  • A correlation was found between behavior change trends and academic performance.
  • The proposed feature selection model effectively identified key predictive features.
  • The combined features (behavioral features + behavior entropy) significantly improved prediction performance compared to previous studies on the same platform.

Conclusions:

  • Learner behavior analysis in MOOCs can be enhanced using entropy-based indicators.
  • The integration of behavioral features and behavior entropy offers a powerful approach for predicting academic performance.
  • This research provides valuable insights for educational institutions utilizing MOOC platforms to monitor and support student success.