handlers/analytics.rs - Learning Analytics and Reporting

Requirements and Dataflow

  • Provides comprehensive learning analytics including population-wide and individual learner metrics
  • Generates real-time performance reports and learning effectiveness measurements
  • Implements adaptive difficulty analysis and learning curve visualization
  • Supports comparative analysis between different learning strategies and conditions
  • Delivers live analytics data for dashboard and monitoring systems

High-level Purpose and Responsibilities

  • Population Analytics: Aggregate learning metrics across all learners and sessions
  • Individual Performance: Detailed analysis of specific learner progress and effectiveness
  • Learning Curves: Temporal analysis of skill acquisition and knowledge retention
  • Strategy Comparison: Comparative effectiveness analysis of different learning approaches
  • Real-time Monitoring: Live dashboard data for learning system performance
  • Bottleneck Analysis: Identification of learning challenges and improvement opportunities

Key Abstractions and Interfaces

  • Population-wide learning metrics with statistical aggregations
  • Individual learner performance analysis with detailed breakdowns
  • Adaptive difficulty effectiveness measurement and optimization recommendations
  • Learning strategy comparison with statistical significance testing
  • Real-time analytics endpoints for dashboard and monitoring integration

Data Transformations and Flow

  1. Data Aggregation: Raw session data → statistical calculations → performance metrics → analytics reports
  2. Learning Curves: Response history → temporal analysis → skill progression → visualization data
  3. Comparative Analysis: Multiple strategy data → statistical comparison → effectiveness ranking → recommendations
  4. Real-time Processing: Live session data → streaming analytics → dashboard updates → alert generation
  5. Bottleneck Detection: Performance analysis → challenge identification → improvement recommendations → action items

Dependencies and Interactions

  • Session Data: Learning session records and response histories for analysis
  • Learner Profiles: Individual learner characteristics and performance baselines
  • Statistical Engine: Advanced statistical calculations and significance testing
  • Real-time Systems: Live data streaming and dashboard integration
  • Business Metrics: Learning effectiveness and engagement measurement
  • Visualization Systems: Chart generation and dashboard data formatting

Architectural Patterns

  • Analytics Pipeline: Multi-stage data processing with statistical analysis
  • Real-time Analytics: Streaming data processing with live dashboard updates
  • Permission-Based Access: Analytics access control based on user roles and permissions
  • Statistical Processing: Advanced analytics with confidence intervals and significance testing
  • Performance Optimization: Efficient query processing for large datasets
  • Dashboard Integration: Standardized data formats for visualization and reporting systems