Quantified Learner Dynamics

Using quantified learner dynamics to preserve the integrity of learning and knowledge assessment in adaptive retrieval practice.

Paper

This repository accompanies a paper:

van der Velde, M., Krambeer, M., & van Rijn, H. (2025). Preserving the integrity of study behaviour in online retrieval practice using quantified learner dynamics. Proceedings of the 18th International Conference on Educational Data Mining, 680--687. https://doi.org/10.5281/zenodo.15870147

Please refer to the paper for a detailed description of the methods and results.

Notebooks

• Fitting the XGBoost model
• Evaluating the XGBoost model

Usage

Generate keystroke and learning performance features from the response data:

make features

Fit the XGBoost model for each learner:

make fit

Evaluate the performance of fitted models:

make evaluate

Do all of the above:

make all

Funding

This project is co-financed by the National Education Lab AI.