1.2 KiB
1.2 KiB
Weighted Evidence Model
Purpose
The earlier evidence engine treated all evidence items equally. This revision adds a more realistic model with:
- evidence-type weights
- recency weighting
- dimension-level rubric storage
- confidence estimates based on weighted support
Evidence weighting
Default weights:
- explanation: 1.0
- problem: 1.5
- transfer: 2.0
- project: 2.5
Recency policy
Each evidence item can be marked is_recent. Recent items receive a multiplier. This allows weak recent performance to matter more than stale success, which is useful for resurfacing fragile concepts.
Confidence
Confidence is currently derived from total weighted evidence mass using a saturating function:
confidence = total_weight / (total_weight + 1.0)
This is simple, monotonic, and interpretable.
Current mastery rule
A concept is mastered if:
- weighted mean score >= mastery threshold
- confidence >= confidence threshold
A previously mastered concept resurfaces if:
- weighted mean score < resurfacing threshold
- and recent weak evidence drags its summary downward enough
Future work
- per-dimension mastery thresholds
- decay by timestamp instead of a boolean recent flag
- Bayesian knowledge tracing
- separate competence vs fluency models