Task Generation Core Module Abstract

High-level Purpose and Responsibility

The task generation core module provides the fundamental framework for creating and managing learning tasks based on topological structures. It serves as the central task generation engine, creating diverse cognitive challenges including sequence learning, spatial navigation, and logical reasoning tasks while maintaining consistent difficulty calibration and performance tracking.

Key Data Structures and Relationships

  • Task: Core task representation with prompt, correct answer, options, and difficulty rating
  • TaskType: Enumeration of different cognitive task categories (sequence, navigation, reasoning)
  • TaskGenerator: Central factory for creating tasks based on topological structures and parameters
  • TaskResponse: Response tracking with accuracy, timing, and metadata for performance analysis
  • TaskSession: Session management for task sequences with history and statistical tracking
  • SessionStatistics: Comprehensive performance analytics across task types and sessions

Main Data Flows and Transformations

  1. Task Generation: Topology + Task type specifications → Algorithmic task creation → Formatted learning challenges
  2. Response Collection: User interactions → Task responses → Performance data with timing information
  3. Difficulty Calibration: Task parameters → Dynamic difficulty assessment → Appropriately challenging tasks
  4. Performance Tracking: Response history → Statistical analysis → Learner proficiency assessment
  5. Session Management: Task sequences → Progress tracking → Comprehensive session analytics

External Dependencies and Interfaces

  • Topology Module: Structural foundations for task generation based on graph relationships
  • Learning Module: Integration with learner proficiency tracking and adaptive difficulty
  • Statistics Module: Performance analysis and statistical assessment of task outcomes
  • Experiments Module: Task provision for experimental studies and controlled comparisons

State Management Patterns

  • Immutable Task Definitions: Task specifications remain constant after generation
  • Session State Tracking: Maintains current task, timing, and historical performance data
  • Random Generation State: Controlled randomization for reproducible task sequences
  • Performance History Management: Accumulates response data for longitudinal analysis

Core Algorithms or Business Logic Abstractions

  • Topological Task Generation: Systematic creation of tasks based on graph structure and relationships
  • Difficulty Assessment: Dynamic calculation of task difficulty based on cognitive load and complexity
  • Random Task Selection: Balanced selection algorithms ensuring diverse task coverage
  • Performance Analysis: Real-time computation of accuracy rates, response times, and learning trends
  • Adaptive Task Sequencing: Intelligent ordering of tasks based on learner progress and proficiency
  • Multi-Modal Task Support: Generation framework supporting various task types and interaction modalities