knowledgespaces
Knowledge Space Theory in Python. Derive structures, estimate knowledge states, find learning paths.
Structures
Build knowledge spaces from surmise relations or skill maps. Compute atoms, bases, fringes, and learning paths over arbitrary domains.
Assessment
Estimate a learner's knowledge state via Bayesian inference. Adaptive item selection minimizes the number of questions needed.
Estimation
Fit BLIM parameters with the EM algorithm. Multi-restart optimization, goodness-of-fit via G², AIC, and BIC.
Integration
Command-line interface, MCP server for LLM-based workflows, and CSV/JSON I/O compatible with R packages.
From prerequisites to assessment in five lines.
import knowledgespaces as ks
structure = ks.space_from_prerequisites(
["add", "sub", "mul"],
[("add", "sub"), ("sub", "mul")],
)
result = ks.assess(
structure,
{"add": True, "sub": True, "mul": False}
)Structure
4 valid knowledge states identified from 8 possible subsets. Union-closed.
Assessment
Most likely knowledge state and the recommended next topic.
Talk to your data.
Your AI assistant becomes a KST expert. Build structures, run assessments, and explore learning paths through natural conversation.
7 tools · 13 resources · 4 prompts
Arthur's estimated knowledge state is {add, mul}. His outer fringe contains sub, which depends only on add (already mastered). Recommended next topic: subtraction.