Worked example paths for LKT models and feature construction.

A typical example starts by loading the data, standardizing columns, creating folds, ordering events, and preparing time-based information for later feature engineering.

AFM estimates student performance in relation to knowledge component difficulty and learning rate. It is helpful for understanding broad learning structure without focusing on richer success/failure separation.

PFA separates the impact of successful and unsuccessful prior responses, making it easier to reflect actual learner history in the model and to reason about strengths and weaknesses across components.