Multi-Session Experiment Module Abstract

High-level Purpose and Responsibility

The multi-session experiment module manages longitudinal learning studies spanning multiple training sessions over extended time periods. It implements spaced repetition algorithms, session scheduling optimization, and cross-session performance tracking to study long-term learning dynamics and retention patterns.

Key Data Structures and Relationships

  • MultiSessionExperiment: Orchestrates multiple learning sessions with scheduling and progress tracking
  • SessionPlan: Defines session sequence, timing intervals, and content progression
  • CrossSessionTracker: Monitors learning transfer and retention across session boundaries
  • SpacedRepetitionScheduler: Implements forgetting curve-based scheduling algorithms
  • RetentionAnalysis: Analyzes memory consolidation and forgetting patterns
  • ProgressionMetrics: Tracks learning velocity and mastery development over time

Main Data Flows and Transformations

  1. Session Scheduling: Learning history → Optimal session timing using spaced repetition algorithms
  2. Content Selection: Previous performance → Adaptive content difficulty and review priority
  3. Cross-Session Analysis: Multi-session data → Retention curves and learning trajectory modeling
  4. Performance Aggregation: Session-level metrics → Longitudinal performance trends and patterns
  5. Intervention Timing: Real-time performance → Dynamic session adjustments and schedule optimization

External Dependencies and Interfaces

  • Learning Module: Adaptive learning algorithms and mastery criteria integration
  • Statistics Module: Time series analysis, retention modeling, and longitudinal statistics
  • Tasks Module: Task generation adapted for multi-session progression and review
  • Protocol Module: Session versioning and consistency across experimental timeframes

State Management Patterns

  • Session State Persistence: Maintains learning state across session boundaries
  • Schedule State Management: Tracks upcoming sessions and completion status
  • Performance History: Immutable record of cross-session learning progression
  • Adaptive State Updates: Dynamic schedule adjustments based on performance patterns

Core Algorithms or Business Logic Abstractions

  • Spaced Repetition Algorithms: SM-2, Anki-style scheduling, and forgetting curve optimization
  • Retention Modeling: Exponential decay models and memory strength estimation
  • Learning Curve Fitting: Power law and exponential models for skill acquisition trajectories
  • Optimal Scheduling: Reinforcement learning approaches to session timing optimization
  • Transfer Analysis: Cross-session skill transfer detection and measurement
  • Mastery Threshold Adaptation: Dynamic criteria adjustment based on long-term retention performance