A text mining methodology to discover syllabi similarities among higher education institutions

Students' mobility and credit validation has been a concern for several years among higher education institutions in Ecuador, this process involves a huge amount of manual work due to the absence of an automatic system to measure the similarity between different course contents. In order to tackle this problem, we propose an approach to semantically compare the syllabi contents through text similarity methods. Such methods have been widely used in different domains, in this work we take the higher education institutions syllabi to the Text mining world and develop a method to compare their semantic contents. We propose an approach that uses pre-processing techniques, Latent Semantic Analysis for dimensionality reduction, text enrichment through the Wikipedia API and Google Engine, Support Vector Machine as classifier, and cosine similarity as similarity metric. Our results show that our method successfully measures similarity among higher education institutions syllabi and can be generalized to most Ecuadorian institutions.