Spaced Repetition & Adaptive Quiz System

## ROLE
You are a learning science specialist who designs evidence-based retention systems. You are an expert in spaced repetition algorithms, retrieval practice research, and adaptive learning technology. You apply cognitive science research to create practical, implementable systems.

## OBJECTIVE
Design a spaced repetition and adaptive assessment system that maximizes long-term knowledge retention, identifies and addresses knowledge gaps efficiently, and is practical to implement in an educational setting.

## TASK
Create a comprehensive spaced repetition system:

### Spaced Repetition Algorithm Design

**1. Core Algorithm**
- Leitner System adaptation for your context
  - Box/level progression rules
  - Review interval scheduling per level
  - Demotion rules for incorrect answers
  - Graduation criteria
- SM-2 algorithm adaptation (SuperMemo variant)
  - Ease factor calculation
  - Interval calculation formula
  - Response quality grading scale
  - New card introduction rate limits

**2. Interval Scheduling**
- Initial learning phase intervals (minutes to hours)
- Short-term retention phase (days)
- Long-term retention phase (weeks to months)
- Overdue card handling
- Vacation/absence catch-up strategy
- Maximum interval caps by content type
- Exam preparation mode (compressed scheduling)

**3. Content Organization**
- Card types for different knowledge types:
  - Facts and definitions (basic front/back)
  - Concepts and understanding (application scenarios)
  - Procedures and processes (sequencing cards)
  - Problem-solving (worked example cards)
  - Connections and relationships (comparison cards)
- Deck organization by topic and difficulty
- Tag system for cross-topic review
- Prerequisites and card dependencies
- Card creation guidelines for maximum effectiveness

### Adaptive Assessment Engine

**1. Difficulty Adaptation**
- Item difficulty estimation using student response data
- Student ability estimation using response patterns
- Matching algorithm (present items near ability level)
- Zone of proximal development targeting
- Confidence-weighted scoring

**2. Knowledge Gap Detection**
- Prerequisite checking before new content
- Misconception identification through distractor analysis
- Pattern recognition in errors (systematic vs. random)
- Knowledge graph mapping (what connects to what)
- Gap prioritization for remediation

**3. Remediation Pathways**
- Automatic intervention triggers
- Scaffolded review sequences
- Alternative explanation presentation
- Worked example insertion
- Peer support matching

### Implementation Design

**Question Types:**
- Multiple choice with diagnostic distractors
- Free recall with fuzzy matching
- Cloze deletion (fill-in-the-blank)
- Image-based identification
- Sequencing and ordering
- Matching and categorization
- Short answer with rubric-based evaluation
- Audio/pronunciation (language learning)

**Student Interface:**
- Daily review session design (duration: 10-15 minutes)
- Progress visualization (retention curves, mastery levels)
- Session summary with learning insights
- Streak tracking and motivation mechanics
- Difficulty preference settings
- Study session scheduling recommendations

**Teacher Dashboard:**
- Class-wide retention analytics
- Individual student progress tracking
- Content difficulty analysis (which items are hardest)
- Upcoming assessment readiness prediction
- Intervention alert system
- Content effectiveness metrics

### Integration Strategy
- LMS integration approach
- Standalone app recommendations
- Existing tool comparison (Anki, Quizlet, Brainscape)
- Custom development requirements
- Data export and interoperability
- Privacy and data security considerations

### Evidence-Based Design Principles
- Testing effect: retrieve before reviewing
- Interleaving: mix topics, do not block
- Desirable difficulty: challenge at the right level
- Elaborative interrogation: ask "why" and "how"
- Dual coding: combine verbal and visual
- Generation effect: create before consuming
- Metacognitive monitoring: predict before answering