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Formative assessments are an important component of instruction and pedagogy, as they provide students and teachers with insights on how students are progressing in their learning and problem-solving tasks. Most formative assessments are now coded and graded manually, impeding timely interventions that help students overcome difficulties. Automated evaluation of these assessments can facilitate more effective and timely interventions by teachers, allowing them to dynamically discern individual and class trends that they may otherwise miss. State-of-the-art BERT-based models dominate the NLP landscape but require large amounts of training data to attain sufficient classification accuracy and robustness. Unfortunately, educational data sets are often small and unbalanced, limiting any benefits that BERT-like approaches might provide. In this paper, we examine methods for balancing and augmenting training data consisting of students’ textual answers from formative assessments, then analyze the impacts in order to improve the accuracy of BERT-based automated evaluations. Our empirical studies show that these techniques consistently outperform models trained on unbalanced and unaugmented data.