FoL 2026
EDM

Synthetic Data & Privacy

Mon Jun 29, 3:45 PM–5:00 PM · North 210
Synthetic Data & Simulated Learners Privacy & Data Governance
★ Notable speakers
René Kizilcec ★★ — Learning at scale; algorithmic fairness and equity in digital education
Richard Baraniuk ★★ — Founder of OpenStax open educational resources; open education movement; signal processing and compressive sensing
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
Kirk Vanacore ★ — Learning analytics; educational data mining; causal inference in tutoring; AI annotation of learning discourse at scale
Debshila Basu Mallick ★ — AI in education research and practice; learning context frameworks; adaptive learning at scale via OpenStax/SafeInsights

EDM #4 | Long Paper Session

3 talks in this session

Real Enough to Matter? Implications of Synthetic Data for Reproducible Learning Analytics
Elena Tiukhova, Grzegorz Meller, Dimitri Van Landuyt, Tinne De Laet, Bart Baesens
Educational Data Mining Methods Synthetic Data & Simulated Learners Privacy & Data Governance
Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach
Gabriel Diaz Ramos, Lorenzo Luzi, Debshila Basu Mallick, Richard Baraniuk
Educational Data Mining Methods Synthetic Data & Simulated Learners Privacy & Data Governance
Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset
Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham, Doug Pietrzak, Daryl Hedley
Intelligent Tutoring Systems Synthetic Data & Simulated Learners Mathematics Education