---
id: wiki-2026-0508-assessment
title: Assessment (Educational + ML Evaluation)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [평가, evaluation, formative, summative, validity, reliability, rubric, ml-evaluation]
duplicate_of: none
source_trust_level: B
confidence_score: 0.88
verification_status: applied
tags: [assessment, evaluation, education, validity, reliability, fairness, rubric, ml-eval, llm-judge]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: education / ML
  applicable_to: [Educational Tech, ML Evaluation, Performance Review]
---

# Assessment

## 📌 한 줄 통찰
> **"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).

## 📖 핵심

### 매 timing 의 분류
1. **Diagnostic** (진단): 매 시작 전 의 수준.
2. **Formative** (형성): 매 진행 중 의 feedback.
3. **Summative** (총괄): 매 final 의 성취.
4. **Authentic**: 매 real-world task.

### 매 quality criteria
- **Validity** (타당도): 매 measure 의 right thing?
  - **Construct**: 매 construct 의 capture.
  - **Content**: 매 domain 의 cover.
  - **Predictive**: 매 future 의 predict.
  - **Face**: 매 looks-like-it.
- **Reliability** (신뢰도): 매 consistent?
  - **Test-retest**: 매 시간 의 stable.
  - **Inter-rater**: 매 rater 의 agree.
  - **Internal consistency** (Cronbach's α).
- **Fairness**: 매 equal opportunity.
- **Authenticity**: 매 real-world ≈.

### 매 educational paradigm

#### Behaviorist (전통)
- 매 multiple choice.
- 매 right/wrong.

#### Cognitivist
- 매 understanding.
- 매 short answer / explain.

#### Constructivist
- 매 portfolio.
- 매 project.
- 매 self/peer reflection.

### 매 ML evaluation 의 parallel
| Education | ML |
|---|---|
| Validity | 매 construct 의 measure |
| Reliability | 매 consistent across runs |
| Fairness | 매 group equity |
| Diagnostic | 매 capability profiling |
| Formative | 매 dev set |
| Summative | 매 test set |
| Authentic | 매 real-world deploy |

### 매 modern issue

#### LLM-as-judge
- 매 fast + 매 cheap.
- 매 self-bias (GPT-4 가 GPT-4 의 favor).
- 매 calibration 필요.

#### Multi-dimensional
- 매 single metric 의 X.
- 매 quality + safety + cost + latency.

#### Adaptive
- 매 IRT (Item Response Theory).
- 매 difficulty 의 adapt.
- 매 GRE / 매 personalized education.

#### Continuous
- 매 portfolio.
- 매 logging-based.
- 매 longitudinal.

### 매 rubric (good)
- 매 specific criteria.
- 매 levels (4-6).
- 매 anchored example.
- 매 actionable feedback.

## 💻 패턴

### Rubric (educational)
```yaml
# 매 essay rubric
criteria:
  - name: Argument
    levels:
      4: "Sophisticated argument with nuance and counter-evidence"
      3: "Clear argument with relevant support"
      2: "Argument present but weakly supported"
      1: "No clear argument or off-topic"
  - name: Evidence
    levels:
      4: "Multiple high-quality sources, integrated"
      3: "Adequate sources cited"
      2: "Few or weak sources"
      1: "No evidence or invented"
  - name: Writing
    levels:
      4: "Polished, varied, error-free"
      3: "Clear, mostly correct"
      2: "Comprehensible but error-laden"
      1: "Incomprehensible"

scoring: weighted_sum  # 매 levels[criterion] * weight
```

### LLM-as-judge (educational)
```python
def judge_essay(essay, rubric):
    prompt = f"""Score this essay against the rubric. Return JSON.

Rubric: {rubric}

Essay:
{essay}

Format:
{{
  "argument": {{ "score": 1-4, "evidence": "..." }},
  "evidence": {{ "score": 1-4, "evidence": "..." }},
  "writing": {{ "score": 1-4, "evidence": "..." }},
  "feedback": "actionable feedback in 3 sentences"
}}"""
    
    response = llm.generate(prompt)
    return json.loads(response)

# 매 calibration
# 매 N=3 judge → 매 average. 매 disagreement → 매 human review.
```

### Inter-rater agreement (Cohen's kappa)
```python
from sklearn.metrics import cohen_kappa_score

def measure_reliability(rater1_scores, rater2_scores):
    kappa = cohen_kappa_score(rater1_scores, rater2_scores)
    if kappa < 0.4: return 'poor'
    if kappa < 0.6: return 'fair'
    if kappa < 0.8: return 'good'
    return 'excellent'
```

### IRT (adaptive testing)
```python
import numpy as np

def irt_3pl(theta, a, b, c):
    """매 3-parameter logistic.
    theta: ability, a: discrimination, b: difficulty, c: guessing."""
    return c + (1 - c) / (1 + np.exp(-a * (theta - b)))

def adaptive_next_item(theta_estimate, item_pool, answered_ids):
    # 매 information 의 maximum 의 item.
    candidates = [item for item in item_pool if item.id not in answered_ids]
    info = lambda item: item.a**2 * irt_3pl(theta_estimate, item.a, item.b, item.c) * \
                        (1 - irt_3pl(theta_estimate, item.a, item.b, item.c))
    return max(candidates, key=info)
```

### Fairness check (group)
```python
def fairness_check(scores, group_labels):
    by_group = collections.defaultdict(list)
    for score, group in zip(scores, group_labels):
        by_group[group].append(score)
    
    means = {g: np.mean(s) for g, s in by_group.items()}
    
    # 매 disparate impact
    max_mean = max(means.values())
    min_mean = min(means.values())
    if min_mean / max_mean < 0.8:
        return f'WARN: disparate impact: {min_mean/max_mean:.2f} < 0.8'
    return 'OK'
```

### Portfolio assessment
```python
class Portfolio:
    def __init__(self, student_id):
        self.student_id = student_id
        self.artifacts = []
    
    def add(self, artifact):
        self.artifacts.append({
            'id': artifact.id,
            'date': artifact.date,
            'type': artifact.type,  # essay, code, image
            'reflection': artifact.reflection,
        })
    
    def progression(self):
        # 매 시간 의 growth 의 visualize
        scores_over_time = [(a.date, a.score) for a in self.artifacts]
        return scores_over_time
```

### ML evaluation suite (multi-dim)
```python
def evaluate_model(model, eval_set):
    return {
        'accuracy': accuracy(model, eval_set),
        'fairness': fairness_check(model, eval_set, sensitive='gender'),
        'safety': safety_score(model, harm_set),
        'calibration': ece(model, eval_set),
        'latency_p95': latency(model),
        'cost_per_1k': cost(model),
        'human_pref': pairwise_human(model, baseline, n=100),
    }
```

## 🤔 결정 기준
| 상황 | Approach |
|---|---|
| Standardized test | Summative + IRT |
| Personalized learning | Diagnostic + adaptive |
| Skill development | Formative + portfolio |
| LLM evaluation | Multi-metric + LLM-judge + human |
| Hiring | Authentic + rubric + structured |
| Performance review | 360° + portfolio |

**기본값**: Multi-method + rubric + inter-rater check + fairness audit.

## 🔗 Graph
- 부모: [[Evaluation]]
- 응용: [[Rubric]]
- ML parallel: [[ML-Evaluation]] · [[Benchmarks]] · [[LLM-as-Judge]] · [[Bias-Correction-Algorithm]]
- Adjacent: [[Algorithmic-Fairness]] · [[Validity]] · [[Reliability]]

## 🤖 LLM 활용
**언제**: 매 educational system design. 매 ML evaluation suite. 매 performance review framework. 매 rubric 작성.
**언제 X**: 매 single high-stakes metric (Goodhart). 매 fairness 의 ignore.

## ❌ 안티패턴
- **Single-metric**: 매 saturate / game.
- **No rubric**: 매 inter-rater disagreement.
- **Stale benchmark**: 매 contamination.
- **No fairness check**: 매 disparate impact.
- **Diagnostic 의 stigma**: 매 student labeling.
- **LLM judge 의 single**: 매 self-bias.
- **No validation 의 construct**: 매 wrong thing measured.

## 🧪 검증 / 중복
- Verified (educational psychology + ML evaluation literature).
- 신뢰도 B.
- Related: [[Benchmarks]] · [[Bias-Correction-Algorithm]] · [[Algorithmic-Fairness]] · [[LLM-as-Judge]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — type + criteria + ML parallel + rubric / IRT / fairness code |
