Concept Graph Learning from Educational Data

This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite re-lations among courses to learn a directed universal con-cept graph, and using the induced graph to predict un-observed prerequisite relations among a broader range of courses. This is particularly useful to induce prerequisite relations among courses from different providers (universi-ties, MOOCs, etc.). We propose a new framework for in-ference within and across two graphs—at the course level and at the induced concept level—which we call Concept Graph Learning (CGL). In the training phase, our system projects the course-level links onto the concept space to in-duce directed concept links; in the testing phase, the concept links are used to predict (unobserved) prerequisite links for test-set courses within the same institution or across insti-tutions. The dual mappings enable our system to perform an interlingua-style transfer learning, e.g. treating the con-cept graph as the interlingua, and inducing prerequisite links in a transferable manner across different universities. Ex-periments on our newly collected data sets of courses fro