knowledgespaces¶

Knowledge Space Theory for Python — from item analysis to adaptive assessment.

Structures

Build knowledge structures and spaces from prerequisite relations.

Knowledge Structures and Relations

Query Experts

Derive structures by querying human experts or LLMs.

Querying Experts

CB-KST

Derive structures from skill maps and competence models.

Competence-Based KST (CB-KST)

Assessment

Adaptive assessment with BLIM and Expected Information Gain.

Adaptive Assessment

Parameter Estimation

Estimate BLIM parameters from response data via EM.

Parameter Estimation (EM)

Command Line

ks inspect, ks query, ks assess for non-programmers.

Command Line Interface

MCP Server

Use KST via LLM — tools, docs, and guided workflows.

API Reference

Full API documentation generated from source code.

Quick example¶

import knowledgespaces as ks

# Build a knowledge structure
structure = ks.space_from_prerequisites(
    ["add", "sub", "mul"],
    [("add", "sub"), ("sub", "mul")],
)

# Assess a student
result = ks.assess(structure, {"add": True, "sub": True, "mul": False})
print(result["state"])        # {'add', 'sub'}
print(result["outer_fringe"]) # {'mul'} — what to learn next

Install¶

Requires Python 3.10 or later. Available on PyPI:

pip install knowledgespaces

Then head to Getting Started for a five-minute tour.