  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.
