mlsanityData to 14 Oct 2026

Privacy-Preserving Synthetic Educational Data Generation

arXiv 2207.03202 ↗First posted 7 Jul 2022cs.CY cs.AI cs.CRcited by 9

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Abstract (arXiv)

Institutions collect massive learning traces but they may not disclose it for privacy issues. Synthetic data generation opens new opportunities for research in education. In this paper we present a generative model for educational data that can preserve the privacy of participants, and an evaluation framework for comparing synthetic data generators. We show how naive pseudonymization can lead to re-identification threats and suggest techniques to guarantee privacy. We evaluate our method on existing massive educational open datasets.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 7 Jul 2022.

Builds on

7 of 32 references.

References · 7

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  3. 2015Deep Knowledge Tracingcited by 332
  4. 2019DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skillscited by 27
  5. 2016Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimationcited by 17
  6. 2020Hide-and-Seek Privacy Challengecited by 8
  7. 2018Probabilistic Inference Using Generators - The Statues Algorithmcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 2.

9 papers.

  1. 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analyticscited by 19
  2. 2024Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancementcited by 7
  3. 2025Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocolscited by 6
  4. 2025A Survey on Current Trends and Recent Advances in Text Anonymizationcited by 5
  5. 2023A review of clustering models in educational data science towards fairness-aware learningcited by 3
  6. 2025Learning to Attack: Uncovering Privacy Risks in Sequential Data Releasescited by 1
  7. 2026DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoningcited by 1
  8. 2026BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulationcited by 1
  9. 2024Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guaranteescited by 0