Using structured knowledge and traditional word embeddings to generate concept representations in the educational domain

To capitalize on the benefits associated with word embeddings, researchers working with data from domains such as medicine, sentiment analysis, or finance, have dedicated efforts to either taking advantage of popular, general-purpose embedding-learning strategies, such as Word2Vec, or developing new ones that explicitly consider domain knowledge in order to generate new domain-specific embeddings. In this manuscript, we instead propose a mixed strategy to generate enriched embeddings specifically designed for the educational domain. We do so by leveraging FastText embeddings pre-trained using Wikipedia, in addition to established educational standards that serve as structured knowledge sources to identify terms, topics, and subjects for each school grade. The results of an initial empirical analysis reveal that the proposed embedding-learning strategy, which infuses limited structured knowledge currently available for education into pre-trained embeddings, can better capture relationships and proximity among education-related terminology. Further, these results demonstrate the advantages of using domain-specific embeddings over general-purpose counterparts for capturing information that pertains to the educational area, along with potential applicability implications when it comes to text processing and analysis for K-12 curriculum-related tasks.