Complete guide to the comprehensive analytics system that tracks multilingual Morse code sentence training with character-level error analysis, language performance comparison, and detailed insights across all training modes and languages.
The CW Sentences Analytics system provides comprehensive insights into multilingual Morse code sentence training through detailed multi-level data collection and analysis. Every training session automatically captures character-level errors, language-specific performance, and training mode analytics across six supported languages.
Character-Level Sentence Analysis: Unlike basic accuracy tracking, this system compares expected vs received sentences character-by-character with full context awareness:
• Substitution: User types 'E' instead of 'T' in "THE QUICK BROWN FOX" → "EHE QUICK BROWN FOX"
• Omission: User omits a character "HELLO WORLD" → "HELO WORLD"
• Insertion: User adds unnecessary character "CQ CQ" → "CQQ CQ"
• Transposition: User swaps adjacent characters "PARIS" → "PARIIS"
Track sentence performance across languages with detailed context analysis
Individual character errors with sentence position and context analysis
Comprehensive tracking across English, Spanish, French, Italian, Danish, Czech
Separate analytics for Type mode and Listen mode training
The system tracks performance across six distinct languages, each optimized for specific linguistic characteristics and training objectives:
Standard English sentences with common amateur radio phrases and general communication
Spanish language sentences with unique character patterns and linguistic structures
French sentences with accented characters and unique phonetic patterns
Italian language training with melodic flow and character combinations
Danish sentences with unique Nordic character patterns and structures
Czech language with complex character combinations and diacritical marks
Each language maintains separate analytics allowing users to track improvement in specific linguistic areas. The system identifies which characters cause the most errors in each language context, enabling targeted practice recommendations and revealing language-specific challenges.
The system provides separate analytics for two distinct training modes, each designed for different skill development objectives:
Detailed character-by-character error analysis within sentence context with error type classification and position tracking.
User types wrong character
User omits a character
User adds unnecessary character
User swaps adjacent characters
Comprehensive performance tracking across all six supported languages with comparative analytics.
Separate analytics for Type mode and Listen mode with comparative performance analysis.
Comprehensive visualization and analysis tools for performance tracking and improvement recommendations.
CREATE TABLE cw_sentences_character_errors (
id INT AUTO_INCREMENT PRIMARY KEY,
username VARCHAR(255) NOT NULL,
language VARCHAR(50) NOT NULL, -- 'english', 'spanish', 'french', 'italian', 'danish', 'czech'
training_mode VARCHAR(20) NOT NULL DEFAULT 'type', -- 'type', 'listen'
expected_char CHAR(1) NOT NULL,
received_char CHAR(1) NOT NULL,
error_type VARCHAR(20) NOT NULL, -- 'substitution', 'omission', 'insertion', 'transposition'
sentence_context TEXT DEFAULT NULL, -- Full sentence context
position_in_sentence INT DEFAULT NULL, -- Character position within sentence
sentence_length INT DEFAULT NULL, -- Total sentence length
session_accuracy DECIMAL(5,2) DEFAULT NULL, -- Overall session accuracy percentage
session_total_sentences INT DEFAULT NULL, -- Total sentences in session
session_correct_sentences INT DEFAULT NULL, -- Correctly answered sentences
session_duration INT DEFAULT NULL, -- Session duration in seconds
exact_speed INT DEFAULT NULL, -- Exact speed in WPM
effective_speed INT DEFAULT NULL, -- Effective speed in WPM
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
INDEX idx_username (username),
INDEX idx_language (language),
INDEX idx_training_mode (training_mode),
INDEX idx_expected_char (expected_char),
INDEX idx_error_type (error_type),
INDEX idx_created_at (created_at)
);{
"language": "english",
"training_mode": "type",
"character_errors": [
{
"expected": "I",
"received": "E",
"type": "substitution",
"sentence": "QUICK BROWN FOX",
"position": 3
}
],
"total_sentences": 10,
"correct_sentences": 8,
"session_duration": 300,
"accuracy_percentage": 85.5,
"exact_speed": 20,
"effective_speed": 18
}Parameters:
?language=english
&training_mode=type
&timeframe=30d
Response:
{
"success": true,
"data": {
"error_types": [...],
"problematic_characters": [...],
"language_performance": [...],
"training_mode_performance": [...],
"recent_trends": [...]
}
}DELETE Request
No body required
Response:
{
"success": true,
"message": "Successfully cleared character analytics",
"data": {
"deleted_records": 42
}
}Multi-Language Character Comparison:
function analyzeCharacterErrors(expected, received, sentenceContext) {
const errors = [];
const expectedArray = expected.split('');
const receivedArray = received.split('');
// Detailed character-by-character analysis with position tracking
// Supports all error types: substitution, omission, insertion, transposition
// Preserves sentence context for better analytics
return errors; // Array of detailed error objects
}The CW Sentences Analytics Dashboard provides comprehensive visualization and analysis tools designed for multilingual Morse code sentence training optimization.
The dashboard seamlessly integrates with the CW Sentences training application, automatically capturing character errors during both Type and Listen mode sessions. All analytics update in real-time as users complete training sessions, providing immediate insights into learning progress and areas for improvement.
This documentation covers the complete CW Sentences Analytics system implementation in Morse Trainer Pro.
For technical support or feature requests, please contact the development team.