@InProceedings{nadeem-ostendorf:2017:BEA,
  author    = {Nadeem, Farah  and  Ostendorf, Mari},
  title     = {Language Based Mapping of Science Assessment Items to Skills},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {319--326},
  abstract  = {Knowledge of the association between assessment questions and the skills
	required to solve them is necessary for analysis of student learning. This
	association, often represented as a Q-matrix, is either hand-labeled by domain
	experts or learned as latent variables given a large student response data set.
	As a means of automating the match to formal standards, this paper uses neural
	text classification methods, leveraging the language in the standards documents
	to identify online text for a proxy training task. Experiments involve
	identifying the topic and crosscutting concepts of middle school science
	questions leveraging multi-task training. Results show that it is possible to
	automatically build a Q-matrix without student response data and using a modest
	number of hand-labeled questions.},
  url       = {http://www.aclweb.org/anthology/W17-5036}
}