Computing Exposition Coherence Across Learning Resources

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11230)


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, show promising results.


Graph kernels Co-occurrence graphs Random walk Word embedding Technology enhanced learning Semantic coherence Open corpus educational resources 



The authors would like to acknowledge and thank the contributions of the project associates P. Srinivasan, Karan Kumar Gupta, and Sanket Kutumbe.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.IIIT BangaloreBengaluruIndia
  2. 2.Gooru IncRedwood CityUSA

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