Experiment Design Module Abstract
High-level Purpose and Responsibility
The experiment design module implements various experimental design paradigms for learning research. It provides structured frameworks for organizing participants, treatments, and conditions across different experimental methodologies, ensuring proper randomization, control, and statistical validity in learning experiments.
Key Data Structures and Relationships
- ExperimentDesign: Trait defining common behavior for all experimental designs
- WithinSubjectDesign: All participants experience all conditions (repeated measures)
- BetweenSubjectDesign: Participants are assigned to single conditions (independent groups)
- MixedDesign: Combines within and between-subject factors in factorial arrangements
- FactorialDesign: Full factorial crossing of multiple independent variables
- TreatmentAssignment: Maps participants to specific experimental conditions
- ConditionDefinition: Specifies parameters and constraints for each experimental condition
Main Data Flows and Transformations
- Participant Assignment: Participant IDs → Condition assignments through randomization algorithms
- Condition Ordering: Design specifications → Counterbalanced condition sequences
- Treatment Application: Assigned conditions → Specific task parameters and learning contexts
- Balance Validation: Assignment outcomes → Statistical balance checks across conditions
- Design Compliance: Runtime monitoring → Adherence to design constraints and protocols
External Dependencies and Interfaces
- Random Number Generation: Deterministic randomization using controlled seeds
- Statistics Module: Balance testing and power analysis for design validation
- Learning Module: Integration with adaptive learning parameters per condition
- Protocol Module: Version tracking for design specifications and changes
State Management Patterns
- Immutable Design Specifications: Design parameters fixed at instantiation
- Dynamic Assignment State: Participant assignments updated during enrollment
- Condition Tracking: Real-time monitoring of participant progress across conditions
- Balance Maintenance: Automatic rebalancing when participants drop out or are excluded
Core Algorithms or Business Logic Abstractions
- Randomization Algorithms: Block randomization, stratified randomization, and minimization procedures
- Counterbalancing Logic: Systematic ordering of conditions to control for sequence effects
- Power Analysis Integration: Sample size calculations based on expected effect sizes and design complexity
- Factorial Expansion: Automatic generation of all condition combinations in factorial designs
- Assignment Validation: Ensures proper distribution of participants across conditions
- Crossover Sequence Generation: Latin square and other systematic ordering schemes for within-subject designs