Bayesian Knowledge Tracing Implemented in a Telecommunications Serious Game

The integration of Bayesian Knowledge Tracing (BKT) into the 3D serious game Volatile Systems represents a significant advancement in educational technology, specifically designed to replace flawed streak-based assessment methods with a more nuanced approach to student mastery. By focusing on the complex subject of Hamming Codes, the project establishes a framework that balances immersive, narrative-driven gameplay with rigorous pedagogical assessment. This architecture utilizes fine-grained knowledge components and procedurally generated practice problems to create a personalized learning environment that adapts dynamically to individual student proficiency.

To validate the efficacy of this adaptive system, researchers employed a synthetic dataset of 75 students modeled via the Rasch method, ensuring the BKT algorithm could reliably estimate learner progress before full-scale deployment. The implementation, supported by the pyBKT library, yielded an average predictive accuracy of 73.6% and a root mean square error of 0.4325. These metrics confirm that the model successfully tracks student understanding within a free-roam virtual escape room, effectively controlling progression and difficulty based on real-time mastery estimates.

Ultimately, this research demonstrates that the synthesis of procedural content generation and Bayesian modeling provides a robust solution for serious game design. By shifting the focus from simple performance metrics to the continuous estimation of knowledge states, the study offers a scalable model for future educational tools. This approach successfully bridges the gap between engagement and instruction, providing a viable path for developing adaptive, data-driven learning experiences within complex technical domains.