Data Export Module - Abstract Documentation

Purpose and Responsibility

Provides comprehensive data export and statistical analysis preparation functionality. Transforms learning session data into analysis-ready formats for R, Python, SPSS, and other statistical software packages, with automated script generation and population-level analytics.

Key Data Structures and Relationships

Core Export Architecture

  • LearnerDataExport: Complete participant dataset with sessions, performance, and model parameters
  • PopulationAnalyzer: Cross-participant analysis and group comparison functionality
  • SessionExporter: Individual session data formatting and summary generation

Data Hierarchy

Population → Participants → Sessions → Trials → Responses

Analysis Structure

  • PerformancePoint: Learning trajectory data points with accuracy and timing
  • ErrorAnalysis: Systematic error pattern identification and confusion matrices
  • ModelSnapshot: Cognitive model parameters at specific time points
  • SessionSummary: Aggregated performance metrics per session

Main Data Flows and Transformations

Individual Export Pipeline

  1. Data Aggregation: Session history, model parameters, performance trajectories
  2. Quality Assessment: Data completeness, outlier detection, validation checks
  3. Format Conversion: JSON, CSV, and platform-specific formats
  4. Script Generation: Automated analysis code for target platforms

Population Export Pipeline

  1. Data Consolidation: Multi-participant dataset assembly
  2. Group Analysis: Between-subjects comparisons and statistical testing
  3. Norm Generation: Population-based difficulty metrics and item analysis
  4. Collaborative Export: Comprehensive analysis packages with documentation

Statistical Analysis Preparation

  • Mixed-Effects Modeling: Hierarchical data structure for multilevel analysis
  • Learning Curve Analysis: Time-series data with individual and group trajectories
  • Transfer Testing: Phase-based performance comparison and gain scoring
  • Strategy Classification: Behavioral pattern identification and clustering

External Dependencies and Interfaces

Statistical Software Integration

  • R Integration: tidyverse, lme4, lmerTest compatibility with automated scripts
  • Python Integration: pandas, scipy, statsmodels compatibility with Jupyter notebooks
  • SPSS Integration: Syntax generation and data import procedures

Export Format Support

  • Structured Data: JSON for programmatic access and API integration
  • Tabular Data: CSV with research-standard variable naming conventions
  • Analysis Scripts: Generated code with embedded documentation and interpretation
  • Codebooks: Human-readable variable descriptions and analysis guidance

State Management Patterns

Export Session Management

  • Incremental Export: Progressive data export without complete session reprocessing
  • Version Control: Export timestamp tracking and data provenance
  • Error Recovery: Partial export completion with resume capability

Data Integrity

  • Validation Pipeline: Multi-level data quality checks and missing data handling
  • Consistency Verification: Cross-session validation and participant matching
  • Privacy Preservation: Automatic anonymization and PII removal

Core Algorithms and Business Logic Abstractions

Performance Analysis

  • Learning Rate Estimation: Individual trajectory fitting and slope calculation
  • Strategy Detection: Response pattern analysis and behavioral classification
  • Error Pattern Mining: Systematic mistake identification and categorization
  • Difficulty Calibration: Item response theory and empirical difficulty estimation

Statistical Preparation

  • Data Transformation: Long/wide format conversion for analysis requirements
  • Variable Engineering: Derived measures and composite scores
  • Group Assignment: Experimental condition inference and validation
  • Phase Detection: Training/test/transfer phase identification

Population Analytics

  • Group Comparisons: Effect size calculation and statistical significance testing
  • Learning Curve Analysis: Multi-level modeling with individual and group trajectories
  • Transfer Analysis: Cross-phase performance comparison and retention measurement
  • Individual Differences: Cognitive profile analysis and strategy clustering

Quality Assurance

  • Data Completeness: Missing data pattern analysis and reporting
  • Outlier Detection: Statistical and domain-based anomaly identification
  • Validity Checks: Logical consistency and temporal ordering validation

Performance Considerations

  • Memory Efficiency: Streaming export for large datasets without full memory loading
  • Processing Speed: Optimized algorithms for population-scale data processing
  • Disk I/O: Efficient file writing with compression for large exports
  • Scalability: Population-level analysis capability for research consortium data

Research Methodology Support

Experimental Design Integration

  • Between-Subjects Analysis: Group comparison and randomization validation
  • Within-Subjects Analysis: Repeated measures and learning progression
  • Mixed Design: Combined between/within factors with proper error terms

Statistical Analysis Support

  • Power Analysis: Sample size estimation and effect size reporting
  • Multiple Comparisons: Family-wise error correction and post-hoc testing
  • Model Selection: Automated model comparison and fit statistics
  • Assumption Testing: Normality, homoscedasticity, and independence verification

Publication Ready Output

  • APA Format: Statistical reporting aligned with publication standards
  • Effect Sizes: Cohen's d, eta-squared, and confidence interval reporting
  • Visualization: Publication-quality plots with appropriate statistical annotations
  • Reproducibility: Complete analysis scripts with version control and documentation

Data Privacy and Ethics

  • Anonymization: Systematic PII removal with participant ID scrambling
  • Consent Tracking: Data use permission verification and scope limitation
  • Data Minimization: Export scope limitation to research-necessary variables
  • Retention Policies: Automatic data lifecycle management and deletion scheduling