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Area of Science:

  • Cognitive Science
  • Educational Technology
  • Behavioral Sciences

Background:

  • Bayesian Knowledge Tracing (BKT) models learner knowledge states over time.
  • Existing BKT tools are mainly in Python/C++, limiting R users in psychology and education.
  • R is a widely used language in educational and psychological research.

Purpose of the Study:

  • Introduce the BKT R package for broader accessibility.
  • Implement standard BKT and five common variants (PPS, IOE, IDE, ILE, LFB).
  • Provide tools for parameter estimation, data integration, and model output in R.

Main Methods:

  • Parameter estimation using the Expectation-Maximization algorithm.
  • Validation via simulation studies and real-world data from the Cognitive Tutor system.
  • Comparison with existing Python implementations for fidelity.

Main Results:

  • The BKT R package demonstrates high fidelity with established Python implementations.
  • Robust parameter recovery was observed across various simulation scenarios.
  • The package successfully applied to real-world educational data.

Conclusions:

  • The BKT R package significantly improves accessibility and flexibility for BKT modeling in R.
  • Enhances reproducibility of cognitive and educational research using BKT.
  • Facilitates advanced learning analytics for R users in behavioral sciences.