Use of Generative Adversarial Networks (GANs) in Educational Technology Research

Abstract

In the context of Artificial Intelligence, Generative Adversarial Nets (GANs) allow the creation and reproduction of artificial data from real datasets. The aims of this work are to seek to verify the equivalence of synthetic data with real data and to verify the possibilities of GAN in educational research. The research methodology begins with the creation of a survey that collects data related to the self-perceptions of university teachers regarding their digital competence and technological-pedagogical knowledge of the content (TPACK model). Once the original dataset is generated, twenty-nine different synthetic samples are created (with an increasing N) using the COPULA-GAN procedure. Finally, a two-stage cluster analysis is applied to verify the interchangeability of the synthetic samples with the original, in addition to extracting descriptive data of the distribution characteristics, thereby checking the similarity of the qualitative results. In the results, qualitatively very similar cluster structures have been obtained in the 150 tests carried out, with a clear tendency to identify three types of teaching profiles, based on their level of technical-pedagogical knowledge of the content. It is concluded that the use of synthetic samples is an interesting way of improving data quality, both for security and anonymization and for increasing sample sizes.