Predicting Course Transferability Using Deep Embeddings and Traditional Classifiers

In this paper, we introduce a novel approach to automate course equivalency evaluation across multiple colleges using publicly available data, deep embedding models, and traditional machine learning. The current process of determining course equivalency is labor-intensive, requiring manual assessment of course descriptions or syllabi, which is inefficient and could cause delays for students matriculating into a school. We leverage deep learning to generate semantic embeddings from raw course descriptions retrieved from school websites and then apply traditional machine learning to classify course equivalence. Our findings demonstrate that this automated approach can significantly improve upon existing manual processes, achieving an f1-score between 0.971 and 0.996. Moreover, the flexibility of embeddings permits expanded applications such as semantic search and retrieval-augmented generation while reducing computational cost.