A comprehensive standard for educational data analytics, enabling student performance tracking, engagement metrics, predictive analytics, learning outcome measurement, and privacy-preserving educational data mining to enhance learning experiences and outcomes.
弘益人間 (홍익인간) · Benefit All Humanity
WIA-EDU-004 establishes a universal framework for learning analytics in educational institutions. This standard enables comprehensive tracking of student performance, engagement patterns, learning outcomes, and predictive modeling while ensuring student privacy, data ethics, and actionable insights for educators, administrators, and learners themselves.
Comprehensive collection of learning data from multiple sources: LMS interactions, assessment results, attendance records, discussion participation, assignment submissions, and behavioral patterns. All data collection is consent-based and privacy-preserving, following GDPR and educational data protection standards.
Advanced analytics processing using machine learning and statistical methods to identify patterns, trends, and insights. Includes performance metrics, engagement scoring, learning path analysis, peer comparisons, and early warning systems for at-risk students. Supports real-time and batch processing modes.
AI-powered predictive analytics to forecast student outcomes, identify struggling learners early, recommend personalized interventions, and optimize learning pathways. Models are continuously trained and validated for accuracy, fairness, and bias mitigation across diverse student populations.
User-friendly dashboards and reports delivering actionable insights to teachers, students, parents, and administrators. Provides personalized recommendations, intervention strategies, curriculum improvements, and institutional analytics. All insights are explainable and include clear next steps.
Comprehensive tracking of student academic performance across subjects, assignments, assessments, and learning objectives with trend analysis and progress monitoring.
Deep analysis of student engagement patterns including participation, time-on-task, discussion contributions, resource access, and collaborative learning behaviors.
Measurement and analysis of learning outcomes aligned with educational standards, competencies, and institutional goals using evidence-based assessment methods.
AI-powered predictions for student success, early identification of at-risk learners, dropout prevention, and personalized intervention recommendations.
Advanced privacy protection using differential privacy, data anonymization, consent management, and secure multi-party computation for sensitive educational data.
Adaptive learning recommendations, customized learning paths, resource suggestions, and personalized feedback based on individual student analytics profiles.
npm install @wia/learning-analyticsimport { LearningAnalytics } from '@wia/learning-analytics';
// Initialize analytics engine
const analytics = new LearningAnalytics({
institutionId: 'university-123',
privacyMode: 'strict',
enablePredictive: true,
dataRetention: '7-years'
});
// Track student performance
const performance = await analytics.trackPerformance({
studentId: 'student-456',
courseId: 'math-101',
assessments: [
{ type: 'quiz', score: 85, maxScore: 100, date: '2025-01-15' },
{ type: 'homework', score: 92, maxScore: 100, date: '2025-01-18' },
{ type: 'exam', score: 78, maxScore: 100, date: '2025-01-22' }
]
});
// Analyze engagement patterns
const engagement = await analytics.analyzeEngagement({
studentId: 'student-456',
courseId: 'math-101',
timeframe: 'last-30-days',
metrics: ['participation', 'resource-access', 'time-on-task', 'collaboration']
});
// Generate predictive insights
const prediction = await analytics.predictOutcome({
studentId: 'student-456',
courseId: 'math-101',
modelType: 'final-grade-prediction',
includeInterventions: true
});
// Get actionable recommendations
const recommendations = await analytics.getRecommendations({
studentId: 'student-456',
courseId: 'math-101',
includeResources: true,
personalized: true
});
console.log('Analytics Report:', {
performance,
engagement,
prediction,
recommendations
});