Abstract
Accessing college course content data at scale is often challenging due to a variety of legal and technical reasons. In this study, we classify college courses into course categories using only a college course name as an input. We describe our training data design, training process and report performance and evaluation metrics on two deep learning models– an LSTM and a word sequence-to-sequence models – trained on a three-level hierarchical course taxonomy with a number of course categories ranging from 58 to 2322. Despite scarce input data, the best performing models reach 0.91 accuracy and 88% relevance in quantitative and qualitative evaluations respectively.
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Borisova, I. (2018). College Course Name Classification at Scale. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_78
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DOI: https://doi.org/10.1007/978-3-319-93846-2_78
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