@InProceedings{pan-EtAl:2017:Long1,
  author    = {Pan, Liangming  and  Li, Chengjiang  and  Li, Juanzi  and  Tang, Jie},
  title     = {Prerequisite Relation Learning for Concepts in MOOCs},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1447--1456},
  abstract  = {What prerequisite knowledge should students achieve a level of mastery before
	moving forward to learn subsequent coursewares? We study the extent to which
	the prerequisite relation between knowledge concepts in Massive Open Online
	Courses (MOOCs) can be inferred automatically. In particular, what kinds of
	information can be leverage to uncover the potential prerequisite relation
	between knowledge concepts. We first propose a representation learning-based
	method for learning latent representations of course concepts, and then
	investigate how different features capture the prerequisite relations between
	concepts. Our experiments on three datasets form Coursera show that the
	proposed method achieves significant improvements (+5.9-48.0% by F1-score)
	comparing with existing methods.},
  url       = {http://aclweb.org/anthology/P17-1133}
}