refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-assessment
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title: Assessment (Educational + ML Evaluation)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [평가, evaluation, formative, summative, validity, reliability, rubric, ml-evaluation]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.88
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verification_status: applied
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tags: [assessment, evaluation, education, validity, reliability, fairness, rubric, ml-eval, llm-judge]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: education / ML
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applicable_to: [Educational Tech, ML Evaluation, Performance Review]
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---
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# Assessment
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## 📌 한 줄 통찰
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> **"매 성장 의 거울"**. 매 current 의 measure + 매 gap → 매 direction. 매 selection 의 X — 매 growth 의 support. 매 modern AI 의 ML evaluation 의 same principle (validity / reliability / fairness).
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## 📖 핵심
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### 매 timing 의 분류
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1. **Diagnostic** (진단): 매 시작 전 의 수준.
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2. **Formative** (형성): 매 진행 중 의 feedback.
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3. **Summative** (총괄): 매 final 의 성취.
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4. **Authentic**: 매 real-world task.
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### 매 quality criteria
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- **Validity** (타당도): 매 measure 의 right thing?
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- **Construct**: 매 construct 의 capture.
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- **Content**: 매 domain 의 cover.
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- **Predictive**: 매 future 의 predict.
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- **Face**: 매 looks-like-it.
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- **Reliability** (신뢰도): 매 consistent?
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- **Test-retest**: 매 시간 의 stable.
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- **Inter-rater**: 매 rater 의 agree.
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- **Internal consistency** (Cronbach's α).
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- **Fairness**: 매 equal opportunity.
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- **Authenticity**: 매 real-world ≈.
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### 매 educational paradigm
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#### Behaviorist (전통)
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- 매 multiple choice.
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- 매 right/wrong.
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#### Cognitivist
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- 매 understanding.
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- 매 short answer / explain.
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#### Constructivist
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- 매 portfolio.
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- 매 project.
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- 매 self/peer reflection.
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### 매 ML evaluation 의 parallel
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| Education | ML |
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|---|---|
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| Validity | 매 construct 의 measure |
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| Reliability | 매 consistent across runs |
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| Fairness | 매 group equity |
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| Diagnostic | 매 capability profiling |
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| Formative | 매 dev set |
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| Summative | 매 test set |
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| Authentic | 매 real-world deploy |
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### 매 modern issue
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#### LLM-as-judge
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- 매 fast + 매 cheap.
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- 매 self-bias (GPT-4 가 GPT-4 의 favor).
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- 매 calibration 필요.
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#### Multi-dimensional
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- 매 single metric 의 X.
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- 매 quality + safety + cost + latency.
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#### Adaptive
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- 매 IRT (Item Response Theory).
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- 매 difficulty 의 adapt.
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- 매 GRE / 매 personalized education.
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#### Continuous
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- 매 portfolio.
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- 매 logging-based.
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- 매 longitudinal.
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### 매 rubric (good)
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- 매 specific criteria.
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- 매 levels (4-6).
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- 매 anchored example.
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- 매 actionable feedback.
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## 💻 패턴
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### Rubric (educational)
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```yaml
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# 매 essay rubric
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criteria:
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- name: Argument
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levels:
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4: "Sophisticated argument with nuance and counter-evidence"
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3: "Clear argument with relevant support"
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2: "Argument present but weakly supported"
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1: "No clear argument or off-topic"
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- name: Evidence
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levels:
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4: "Multiple high-quality sources, integrated"
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3: "Adequate sources cited"
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2: "Few or weak sources"
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1: "No evidence or invented"
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- name: Writing
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levels:
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4: "Polished, varied, error-free"
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3: "Clear, mostly correct"
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2: "Comprehensible but error-laden"
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1: "Incomprehensible"
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scoring: weighted_sum # 매 levels[criterion] * weight
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```
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### LLM-as-judge (educational)
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```python
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def judge_essay(essay, rubric):
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prompt = f"""Score this essay against the rubric. Return JSON.
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Rubric: {rubric}
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Essay:
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{essay}
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Format:
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{{
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"argument": {{ "score": 1-4, "evidence": "..." }},
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"evidence": {{ "score": 1-4, "evidence": "..." }},
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"writing": {{ "score": 1-4, "evidence": "..." }},
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"feedback": "actionable feedback in 3 sentences"
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}}"""
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response = llm.generate(prompt)
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return json.loads(response)
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# 매 calibration
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# 매 N=3 judge → 매 average. 매 disagreement → 매 human review.
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```
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### Inter-rater agreement (Cohen's kappa)
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```python
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from sklearn.metrics import cohen_kappa_score
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def measure_reliability(rater1_scores, rater2_scores):
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kappa = cohen_kappa_score(rater1_scores, rater2_scores)
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if kappa < 0.4: return 'poor'
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if kappa < 0.6: return 'fair'
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if kappa < 0.8: return 'good'
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return 'excellent'
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```
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### IRT (adaptive testing)
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```python
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import numpy as np
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def irt_3pl(theta, a, b, c):
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"""매 3-parameter logistic.
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theta: ability, a: discrimination, b: difficulty, c: guessing."""
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return c + (1 - c) / (1 + np.exp(-a * (theta - b)))
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def adaptive_next_item(theta_estimate, item_pool, answered_ids):
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# 매 information 의 maximum 의 item.
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candidates = [item for item in item_pool if item.id not in answered_ids]
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info = lambda item: item.a**2 * irt_3pl(theta_estimate, item.a, item.b, item.c) * \
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(1 - irt_3pl(theta_estimate, item.a, item.b, item.c))
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return max(candidates, key=info)
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```
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### Fairness check (group)
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```python
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def fairness_check(scores, group_labels):
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by_group = collections.defaultdict(list)
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for score, group in zip(scores, group_labels):
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by_group[group].append(score)
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means = {g: np.mean(s) for g, s in by_group.items()}
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# 매 disparate impact
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max_mean = max(means.values())
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min_mean = min(means.values())
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if min_mean / max_mean < 0.8:
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return f'WARN: disparate impact: {min_mean/max_mean:.2f} < 0.8'
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return 'OK'
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```
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### Portfolio assessment
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```python
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class Portfolio:
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def __init__(self, student_id):
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self.student_id = student_id
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self.artifacts = []
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def add(self, artifact):
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self.artifacts.append({
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'id': artifact.id,
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'date': artifact.date,
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'type': artifact.type, # essay, code, image
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'reflection': artifact.reflection,
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})
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def progression(self):
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# 매 시간 의 growth 의 visualize
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scores_over_time = [(a.date, a.score) for a in self.artifacts]
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return scores_over_time
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```
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### ML evaluation suite (multi-dim)
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```python
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def evaluate_model(model, eval_set):
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return {
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'accuracy': accuracy(model, eval_set),
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'fairness': fairness_check(model, eval_set, sensitive='gender'),
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'safety': safety_score(model, harm_set),
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'calibration': ece(model, eval_set),
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'latency_p95': latency(model),
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'cost_per_1k': cost(model),
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'human_pref': pairwise_human(model, baseline, n=100),
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}
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```
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## 🤔 결정 기준
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| 상황 | Approach |
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|---|---|
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| Standardized test | Summative + IRT |
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| Personalized learning | Diagnostic + adaptive |
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| Skill development | Formative + portfolio |
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| LLM evaluation | Multi-metric + LLM-judge + human |
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| Hiring | Authentic + rubric + structured |
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| Performance review | 360° + portfolio |
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**기본값**: Multi-method + rubric + inter-rater check + fairness audit.
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## 🔗 Graph
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- 부모: [[Evaluation]]
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- 응용: [[Rubric]]
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- ML parallel: [[ML-Evaluation]] · [[Benchmarks]] · [[LLM-as-Judge]] · [[Bias-Correction-Algorithm]]
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- Adjacent: [[Algorithmic Fairness]] · [[Validity]] · [[Reliability]]
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## 🤖 LLM 활용
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**언제**: 매 educational system design. 매 ML evaluation suite. 매 performance review framework. 매 rubric 작성.
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**언제 X**: 매 single high-stakes metric (Goodhart). 매 fairness 의 ignore.
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## ❌ 안티패턴
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- **Single-metric**: 매 saturate / game.
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- **No rubric**: 매 inter-rater disagreement.
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- **Stale benchmark**: 매 contamination.
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- **No fairness check**: 매 disparate impact.
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- **Diagnostic 의 stigma**: 매 student labeling.
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- **LLM judge 의 single**: 매 self-bias.
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- **No validation 의 construct**: 매 wrong thing measured.
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## 🧪 검증 / 중복
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- Verified (educational psychology + ML evaluation literature).
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- 신뢰도 B.
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- Related: [[Benchmarks]] · [[Bias-Correction-Algorithm]] · [[Algorithmic Fairness]] · [[LLM-as-Judge]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — type + criteria + ML parallel + rubric / IRT / fairness code |
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