Computing exposition coherence across learning resources

Abstract

With increasing numbers of open learning resources on the web that are created and published independently by different sources, stringing together coherent learning pathways is a challenging task. Coherence in this context means the semantic “smoothness” of transition from one learning resource to the next, i.e., the change in topic distribution and exposition styles between consecutive resources is minimal, and the overall sequence of resources together provides a good learning experience. Towards this end, we present a model to compute exposition coherence between a pair of learning resources, based on representing exposition styles in the form of a random walk. It is based on an underlying hypothesis about exposition styles modelled as a sequence of topical entailments. Evaluation of the presented model on the dataset of learning pathways curated by the teachers of the educational platform Gooru.org, show promising results.