Generating Sequences for Online Courses using a GAN based on a small Sample Set

Abstract

In this paper, we use a Generative Adversarial Network (GAN) as a sequence generator for language learning online courses. Therefore, we cluster a very small dataset of manually created training samples to derive rules. Then, we train a GAN that can mimic rule-based sequences, where we use our derived rules to evaluate generated samples. We enhance our approach by a parameter that course creators can select deviations they want to have in new sequences without manual adjustments. The resulting sequences follow the core structure of the small sample set. Based on deviations of the generated new learning paths, new combinations of methods can be used that course creators did not previously have in mind. This opens up a new way to generate course sequences without the need to model many alternative learning paths for adaptions.