stanbkt 0.1.2

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StanBKT

Overview

StanBKT is a Python package for fitting Bayesian Knowledge Tracing (BKT) models with Stan (https://mc-stan.org/). It is designed for educational data in long format (student interactions over time) and provides tools for:

• fitting standard and grouped BKT models,
• generating hidden-knowledge predictions,
• simulation utilities for synthetic BKT datasets,
• and plotting/posterior analysis workflows.

Documentation

Full user docs, API reference, and examples are available at:

https://stanbkt.readthedocs.io/

Installation

StanBKT requires Python 3.12+.

Install from PyPI:

pip install stanbkt

Or with uv:

uv add stanbkt

One-time setup (CmdStan)

After installing StanBKT, run the CmdStan setup once on each machine:

from stanbkt.utils import setup_cmdstanpy

# Adjust n_cores for your machine
setup_cmdstanpy(n_cores=4)

This installs/configures CmdStan so model compilation and fitting can run.

Quick Start

from stanbkt.models import StandardBKT
from stanbkt.utils import sim_simple_BKT

# 1) Generate synthetic interaction data
data = sim_simple_BKT(n_students=100, n_problems=30, n_kcs=3, rng_seed=42)

# 2) Fit a BKT model
model = StandardBKT()
model.fit(data)

# 3) Predict latent knowledge and correctness probabilities
pred = model.predict(data)
print(pred.head())

Expected prediction columns include:

• kc_id
• student_id
• problem_id
• pKnow
• pCorrectness
• correct

Data Format

StanBKT expects interaction data in long format with these columns:

• student_id
• problem_id
• correct (0/1)
• timestamp or other ordering field
• kc_id (optional; if omitted, all rows are treated as one KC)

If your dataset uses different column names, pass a column_mapping dictionary to fit and predict.

Notes

• StanBKT uses CmdStanPy under the hood.
• If you use Windows, review the installation notes in the docs for compiler/toolchain setup.
• Running Stan-based inference can take time on first compile.

License

See the LICENSE file.