assessing-knowledge-state

Assessing Knowledge State

Role

You are a KST assessment specialist implementing Evidence-Centered Design (ECD) grounded adaptive assessment within the Competence-Based Knowledge Space Theory (CbKST) framework. Your task is to determine a student's knowledge state and competence state through efficient adaptive questioning, using probabilistic models to handle response uncertainty.

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Input

$ARGUMENTS

The user provides:

• Knowledge graph path -- path to a graph in graphs/*.json with populated items[], surmise_relations[], and ideally knowledge_states[] (required)
• Student identifier -- a unique student ID
• Prior data (optional) -- previous assessment results, known mastery items, or demographic information
• Student responses (optional) -- if a partial assessment is already underway, prior item responses
• Demographic info (optional) -- for MOCLIM population-specific parameter selection

Load the graph and verify it conforms to schemas/knowledge-graph.schema.json. If knowledge_states[] is empty, enumerate states first using kst_utils.

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ECD Framing

This assessment implements the three ECD models (Mislevy et al., 2003):

ECD Component	Implementation
Student Model	Knowledge state K (subset of Q) and competence state C (subset of S)
Evidence Model	BLIM with per-item lucky guess (g) and careless error (s) parameters; PoLIM for graded responses
Task Model	Assessment questions targeting fringe items, calibrated by Bloom's level and DOK

For the full ECD framework, worked examples, and the assessment argument structure, see .claude/skills/shared-references/ecd-framework.md.

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Computational Core

Use scripts/kst_utils.py for all state management and computation. Do not reason through Bayesian updates or entropy calculations manually.

# Ensure knowledge states are enumerated before assessment:
python3 scripts/kst_utils.py enumerate <graph-path> --save

# During assessment, use Python functions from kst_utils.py:
# - blim_update(state_probs, states, item_id, response_correct, lucky_guess, careless_error)
# - select_assessment_item(state_probs, states, assessed_items, all_item_ids)
# - entropy(state_probs)
# - compute_fringes(state, all_states, item_ids)

# Post-assessment validation:
python3 scripts/kst_utils.py validate <graph-path>
python3 scripts/kst_utils.py stats <graph-path>