# Model Card: Mastery Model

## Model Details

- **Model Name:** mastery_model
- **Model Version:** mastery_model_v2_baseline_001
- **Algorithm:** RandomForestClassifier
- **Framework:** scikit-learn
- **Trained At:** 2026-05-21T05:59:11.764140+00:00
- **Seed:** 42

## Intended Use

Predict per-student per-LO mastery label (weak, developing, proficient, mastered)
based on behavioral and performance features. Used in the mastery prediction
endpoint to classify student mastery level for a given learning outcome.

## Training Data

- **Source:** training_mastery_prediction.csv (synthetic dataset v2)
- **Split Counts:** train=24515, validation=5218, test=5187
- **Features:** attempt_count, accuracy, average_marks_ratio, average_time_seconds, hint_usage_rate, attendance_percentage, assignment_completion_rate, average_login_per_week, inactive_days_last_14 (all numeric)
- **Target:** mastery_label (integer 0-3, mapped to weak/developing/proficient/mastered)

## Metrics

### Validation Set
- Macro F1: 0.8649
- Weighted F1: 0.8952

### Test Set
- Macro F1: 0.8754
- Weighted F1: 0.9029

## Per-Class Performance (Test Set)

| Class | Precision | Recall | F1 | Support |
|-------|-----------|--------|-----|---------|
| weak | 0.9667 | 0.9717 | 0.9692 | 2120 |
| developing | 0.9155 | 0.9089 | 0.9122 | 1394 |
| proficient | 0.7814 | 0.7555 | 0.7683 | 814 |
| mastered | 0.8395 | 0.865 | 0.8521 | 859 |

## Known Limitations

- Trained on synthetic data only — performance on real student data is unknown.
- 4-class mastery labels derived from synthetic mastery_score thresholds.
- All features are numeric; no text or contextual features used.
- Class distribution may not reflect real-world mastery patterns.
- No encoding needed since all features are already numeric.

## Fallback Behavior

When the model is not loaded or confidence is below the threshold (0.55),
the system falls back to rule-based mastery estimation using mastery_score
thresholds: <0.4 weak, <0.6 developing, <0.8 proficient, else mastered.