Skip to content

README.md

Latest commit

 

History

History
38 lines (26 loc) · 1.26 KB

README.md

File metadata and controls

38 lines (26 loc) · 1.26 KB

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.