  Given the inherent class imbalance issue within student performance datasets,
samples belonging to the edges of the target class distribution pose a
challenge for predictive machine learning algorithms to learn. In this paper,
we introduce a general framework for synthetic embedding-based data generation
(SEDG), a search-based approach to generate new synthetic samples using
embeddings to correct the detriment effects of class imbalances optimally. We
compare the SEDG framework to past synthetic data generation methods, including
deep generative models, and traditional sampling methods. In our results, we
find SEDG to outperform the traditional re-sampling methods for deep neural
networks and perform competitively for common machine learning classifiers on
the student performance task in several standard performance metrics.
