EDM

Real Enough to Matter? Implications of Synthetic Data for Reproducible Learning Analytics

Mon Jun 29, 3:45 PM–4:10 PM · North 210

Synthetic Data & Simulated Learners Educational Data Mining Methods Privacy & Data Governance

★ Notable speakers

Tinne De Laet ★ — Learning analytics dashboards for student advising/success in STEM; explainable AI for education

Bart Baesens ★ — Machine learning; big data analytics; credit risk modeling; fraud detection

Monique Snoeck ★ — Information systems engineering; learning analytics; process mining for learning; model-driven engineering

Evaluates whether synthetic educational data is realistic enough for reproducible learning analytics, examining implications for study replication and generalizability.

Authors

Elena Tiukhova, Grzegorz Meller, Dimitri Van Landuyt, Tinne De Laet, Bart Baesens, Monique Snoeck