Discovering prerequisite relations from educational documents through word embeddings

Inferring prerequisite relations among educational documents, in terms of prior knowledge required to understand and complete assignments about certain topics, is a crucial task for instructional designers and teachers. Massive open online courses, electronic textbooks, public encyclopedias and repositories of learning objects and other forms of informative content create a huge availability of educational material, which can be exploited in online platforms for distance education, both for recommending specific resources and personalized learning paths. But public taxonomies of prerequisites, or learning object metadata useful to trace down prerequisites are not generally available. A description of a new approach for prerequisite discovering in educational documents is given. It is based on word embeddings, that is, statistical language models for the representation of text-based learning objects in low-dimensional latent spaces. It takes advantage of the latent representations to identify prerequisites in a binary classification setting. The accuracy of the approach is validated by means of an experimental benchmark covering multiple datasets of educational material.