# Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases

arXiv: https://arxiv.org/abs/2210.08528 · HTML: https://mlsanity.com/paper/2210.08528/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [A 3D GAN for Improved Large-pose Facial Recognition](https://mlsanity.com/paper/2012.10545/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [Causal Synthetic Data Generation in Recruitment](https://mlsanity.com/paper/2511.16204/) (2025)
- [Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models](https://mlsanity.com/paper/2604.21031/) (2026)

## Abstract (arXiv)

The ability to generate synthetic data has a variety of use cases across different domains. In education research, there is a growing need to have access to synthetic data to test certain concepts and ideas. In recent years, several deep learning architectures were used to aid in the generation of synthetic data but with varying results. In the education context, the sophistication of implementing different models requiring large datasets is becoming very important. This study aims to compare the application of synthetic tabular data generation between a probabilistic model specifically a Bayesian Network, and a deep learning model, specifically a Generative Adversarial Network using a classification task. The results of this study indicate that synthetic tabular data generation is better suited for the education context using probabilistic models (overall accuracy of 75%) than deep learning architecture (overall accuracy of 38%) because of probabilistic interdependence. Lastly, we recommend that other data types, should be explored and evaluated for their application in generating synthetic data for education use cases.