@InProceedings{madnani-loukina-cahill:2017:BEA,
  author    = {Madnani, Nitin  and  Loukina, Anastassia  and  Cahill, Aoife},
  title     = {A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring},
  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     = {457--467},
  abstract  = {We explore various supervised learning strategies for automated scoring of
	content knowledge for a large corpus of 130 different content-based questions
	spanning four subject areas (Science, Math, English Language Arts, and Social
	Studies) and containing over 230,000 responses scored by human raters. Based on
	our analyses, we provide specific recommendations for content scoring. These
	are based on patterns observed across multiple questions and assessments and
	are, therefore, likely to generalize to other scenarios and prove useful to the
	community as automated content scoring becomes more popular in schools and
	classrooms.},
  url       = {http://www.aclweb.org/anthology/W17-5052}
}