# SynEdu‑HEDL: A Synthetic Dataset for Early Warning Prediction of Student Success

SynEdu‑HEDL is a large‑scale, high‑fidelity synthetic dataset designed to accelerate research in privacy‑preserving learning analytics. It provides a realistic simulation of student demographics, course characteristics, digital engagement patterns, and academic outcomes in higher education. The dataset contains **20,000 synthetic students**, **180 courses**, and over **120,000 LMS interaction events**, structured across six interconnected tables with referential integrity.

Key features:
- **Privacy‑preserving**: 100% synthetic – no real student data, enabling open sharing and collaboration.
- **Realistic correlations**: Patterns mirror actual educational environments, including relationships between socioeconomic status, digital literacy, engagement, and performance.
- **Multi‑table design**: STUDENT_PROFILE, COURSE_METADATA, LMS_INTERACTION_LOG, ASSESSMENT_PERFORMANCE, ENGAGEMENT_METRICS, and OUTCOME_LABELS.
- **Early warning ready**: Includes temporal snapshots and outcome labels (dropout risk, final grades) for developing and evaluating predictive models at different prediction horizons.

SynEdu‑HEDL supports research in:
- Early warning systems for student success
- Predictive modeling of academic performance
- Fairness and bias analysis in educational AI
- Benchmarking generative models for synthetic educational data