Learner Core Module Abstract

High-level Purpose and Responsibility

The learner core module provides the fundamental framework for modeling individual learner states and tracking learning progress across different cognitive operations. It serves as the foundational abstraction for all learning algorithms, maintaining proficiency estimates, operation-specific performance tracking, and chunk boundary awareness for sequence learning tasks.

Key Data Structures and Relationships

  • Learner: Central learner model containing proficiency tracking and learning history
  • LearnerState: Current learning state with performance metrics and confidence estimates
  • OperationType: Enumeration of cognitive operations (successor, predecessor, segment recitation, etc.)
  • ProficiencyLevel: Quantified skill levels with confidence intervals and mastery thresholds
  • ChunkBoundary: Cognitive boundaries in sequence learning with strength and position information
  • LearningHistory: Temporal record of performance changes and skill acquisition patterns

Main Data Flows and Transformations

  1. Performance Update: Task responses → Proficiency level adjustments using learning algorithms
  2. Operation Classification: Task types → Specific operation tracking and skill specialization
  3. Confidence Estimation: Response patterns → Uncertainty quantification for proficiency estimates
  4. Boundary Detection: Sequence performance → Identification of chunk boundaries and cognitive organization
  5. Skill Transfer: Cross-operation learning → Transfer effects and skill generalization patterns

External Dependencies and Interfaces

  • Statistics Module: Statistical inference for proficiency estimation and confidence intervals
  • Tasks Module: Task classification and difficulty assessment for learning calibration
  • Experiments Module: Integration with experimental designs and outcome measurement
  • Data Module: Persistent storage of learning states and historical performance data

State Management Patterns

  • Incremental Learning Updates: Continuous refinement of proficiency estimates with new observations
  • Operation-Specific Tracking: Separate skill progression for different cognitive operations
  • Confidence-Weighted Updates: Learning rate adaptation based on estimate uncertainty
  • Boundary-Aware Learning: Special handling for chunk boundaries and hierarchical skill organization

Core Algorithms or Business Logic Abstractions

  • Proficiency Estimation: Bayesian inference for skill level with uncertainty quantification
  • Learning Rate Adaptation: Dynamic adjustment of learning parameters based on performance consistency
  • Skill Transfer Modeling: Cross-operation knowledge transfer and generalization patterns
  • Mastery Threshold Detection: Automatic identification of skill acquisition milestones
  • Chunk Boundary Learning: Detection and utilization of cognitive organization in sequence learning
  • Performance Prediction: Forward modeling of expected performance based on current learner state