Bayesian Knowledge Tracing Implementation

A Python implementation of the Bayesian Knowledge Tracing (BKT) model based on Corbett and Anderson's work (1995). BKT is a statistical model that aims to estimate a student's knowledge state through their pattern of correct and incorrect responses.

Overview

This implementation models the acquisition of student knowledge as described in:

Corbett, A. T.; Anderson, J. R. (1995). "Knowledge tracing: Modeling the acquisition of procedural knowledge". User Modeling and User-Adapted Interaction. 4 (4): 253–278.

Requirements

See the Requirements file for detailed dependencies.

Setup

1. Clone this repository
2. Install the required dependencies:

pip install -r Requirements

Usage

Run the main script

python run.py

Model Parameters

The BKT model uses four parameters:

• Initial Knowledge (p(L₀)): Probability a student knows the skill before any practice
• Learning Rate (p(T)): Probability of transitioning from not knowing to knowing the skill
• Slip (p(S)): Probability of making a mistake despite knowing the skill
• Guess (p(G)): Probability of correctly answering despite not knowing the skill

Configurations

Model parameters and runtime settings can be configured in config.json.