curriculum-analyze-outcomes

• Analyze assessment results
• Calculate mastery rates
• Identify performance patterns
• Generate analytics reports
• Measure learning outcomes

• Assessment Data: Student scores, responses
• Learning Objectives: What was assessed
• Demographics (optional): For gap analysis
• Historical Data (optional): For trends

1. Load and Validate Data

Import:

• Assessment scores by student
• Item-level responses
• Learning objective mappings
• Student demographic data (if analyzing equity)
• Timestamps for trend analysis

2. Calculate Objective Mastery Rates

For each learning objective:

```markdown

Objective: Students will identify the role of chlorophyll in photosynthesis

Items Assessing This Objective: MC-1, MC-5, SA-2

Mastery Threshold: 75% correct

Results:

• Mastered (≥75%): 23 students (76.7%)
• Approaching (50-74%): 5 students (16.7%)
• Needs Support (<50%): 2 students (6.7%)

Average Score: 82.3%

Median Score: 85%

Mode: 90%

Standard Deviation: 12.4

Distribution:

```

90-100%: ████████████████████ 18 students

80-89%: ████████ 7 students

70-79%: ███ 3 students

60-69%: ██ 2 students

50-59%: █ 1 student

< 50%: █ 1 student

```

Interpretation:

Strong performance overall. 76.7% of students have mastered this objective, exceeding the target of 70%. Focus support on 2 students struggling significantly.

Recommendations:

• Continue current instructional approach (effective for majority)
• Provide small group intervention for 2 students below 50%
• Consider extension activities for 18 students scoring 90%+

```

3. Identify High/Low Performing Objectives

```markdown

| Objective | Avg Score | Mastery Rate | Status | Action |

|-----------|-----------|--------------|--------|--------|

| LO-1.1 | 82% | 77% | ✅ Strong | Continue |

| LO-1.2 | 78% | 70% | ✅ Adequate | Monitor |

| LO-1.3 | 65% | 45% | ⚠️ Low | Reteach |

| LO-2.1 | 58% | 30% | ❌ Very Low | Redesign |

Low Performing Objectives (Mastery < 60%):

• LO-1.3: Only 45% mastery - Students struggle with applying concepts
• LO-2.1: Only 30% mastery - Major instructional gap

Analysis:

Pattern shows students understand content (LO-1.1, LO-1.2 strong) but cannot apply it (LO-1.3, LO-2.1 weak). Need more application practice and scaffolding.

```

4. Analyze Achievement Gaps

```markdown

Performance by Demographic Group

By Gender:

| Group | Avg Score | Mastery Rate | Gap |

|-------|-----------|--------------|-----|

| Female | 78% | 72% | +5% |

| Male | 73% | 67% | Baseline |

Analysis: Small gap favoring female students (5 percentage points). Not statistically significant but worth monitoring.

By Race/Ethnicity:

| Group | Avg Score | Mastery Rate | Gap |

|-------|-----------|--------------|-----|

| Asian | 82% | 78% | +8% |

| White | 75% | 70% | Baseline |

| Latino/a | 68% | 58% | -12% |

| Black | 65% | 55% | -15% |

Analysis: ⚠️ Significant gaps for Latino/a (-12%) and Black students (-15%). This requires immediate attention to ensure equitable outcomes.

Potential Contributing Factors:

• Language barriers in assessment items?
• Cultural bias in examples/scenarios?
• Prior knowledge gaps?
• Instructional approach not reaching all learners?

Recommendations:

1. Review assessment items for bias (use /curriculum.review-bias)
2. Check prerequisite mastery by group
3. Implement culturally responsive teaching strategies
4. Provide targeted support for affected groups
5. Monitor gap closure in future assessments

By Socioeconomic Status (Free/Reduced Lunch):

| Group | Avg Score | Mastery Rate | Gap |

|-------|-----------|--------------|-----|

| Not FRL | 77% | 73% | +7% |

| FRL | 70% | 66% | Baseline |

Analysis: Moderate gap (7 points). Consider resource access issues.

```

5. Item Analysis (Psychometrics)

```markdown

| Item | Difficulty (p) | Discrimination (D) | Quality | Action |

|------|---------------|-------------------|---------|--------|

| MC-1 | 0.85 | 0.45 | ✅ Good | Keep |

| MC-2 | 0.52 | 0.60 | ✅ Excellent | Keep |

| MC-3 | 0.95 | 0.15 | ⚠️ Too Easy, Low Disc | Revise |

| MC-4 | 0.25 | 0.10 | ❌ Too Hard, Low Disc | Replace |

Metrics:

• Difficulty (p-value): Proportion answering correctly

- 0.85 = 85% correct = Easy

- 0.50 = 50% correct = Moderate

- 0.25 = 25% correct = Hard

• Discrimination: Correlation with total score

- >0.40 = Excellent

- 0.30-0.39 = Good

- 0.20-0.29 = Fair

- <0.20 = Poor (doesn't distinguish high/low performers)

Item MC-4 Analysis:

Very difficult (only 25% correct) AND poor discrimination (0.10). This suggests item is flawed—even high performers get it wrong. Review for:

• Ambiguous wording
• Trick question
• Content not taught
• Multiple defensible answers

Recommendations:

• Replace MC-4 with clearer item
• Make MC-3 slightly more challenging
• Keep MC-1 and MC-2 (functioning well)

```

6. Generate Analytics Dashboard

Create visual summary:

```markdown

# Learning Analytics Dashboard: [COURSE/UNIT]

Period: [Date Range]

Students: [N]

Assessments: [Count]

📊 Average Course Performance: 74% (C+)

📈 Objective Mastery Rate: 68% (14/20 objectives)

⚠️ At-Risk Students: 5 (16.7%)

✅ High Performers: 12 (40%)

```

Unit 1: ████████░░ 80% mastery

Unit 2: ██████░░░░ 60% mastery ⚠️

Unit 3: ███████░░░ 70% mastery

```

```

A (90-100%): ██████████ 10 students (33%)

B (80-89%): ████████ 8 students (27%)

C (70-79%): ████ 4 students (13%)

D (60-69%): ███ 3 students (10%)

F (< 60%): █████ 5 students (17%) ⚠️

```

[Line graph showing performance over time]

• Week 1: 65%
• Week 3: 72%
• Week 5: 74%
• Trend: +9 percentage points improvement 📈

1. Reteach Unit 2 objectives (low mastery)
2. Intervene with 5 at-risk students (scoring below 60%)
3. Address achievement gap for Latino/a and Black students (-12% and -15%)
4. Replace flawed assessment items (MC-4)
5. Provide enrichment for high performers (12 students ready for extension)

---

Analytics Metadata:

• Generated: [Date]
• Data Sources: [Assessments included]
• Next Analysis: [Recommended timing]

```

7. CLI Interface

```bash

# Analyze single assessment

/curriculum.analyze-outcomes --assessment "unit1-exam-results.csv" --objectives "objectives.json"

# Course-level analysis

/curriculum.analyze-outcomes --course "BIO-101" --period "Fall 2024" --demographics

# Trend analysis

/curriculum.analyze-outcomes --assessments "results/*.csv" --trend --start "2024-09-01" --end "2024-11-30"

# Equity focus

/curriculum.analyze-outcomes --assessment "results.csv" --equity-analysis --demographics "students.csv"

# Help

/curriculum.analyze-outcomes --help

```

Input from:

• /curriculum.grade-assist - Student scores
• /curriculum.design - Learning objectives
• /curriculum.assess-design - Assessment structure

Output to:

• /curriculum.iterate-feedback - Data for revision recommendations
• Educators for decision-making
• Administrators for reporting

• 0: Analysis completed successfully
• 1: Cannot load assessment data
• 2: Data format invalid
• 3: Insufficient data for analysis
• 4: Missing objective mappings