@InProceedings{burstein-EtAl:2017:BEA,
  author    = {Burstein, Jill  and  McCaffrey, Dan  and  Beigman Klebanov, Beata  and  Ling, Guangming},
  title     = {Exploring Relationships Between Writing \& Broader Outcomes With Automated Writing Evaluation},
  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     = {101--108},
  abstract  = {Writing is a challenge, especially for at-risk students who may lack the
	prerequisite writing skills required to persist in U.S. 4-year postsecondary
	(college) institutions. Educators teaching postsecondary courses requiring
	writing could benefit from a better understanding of writing achievement and
	its role in postsecondary success. In this paper, novel exploratory work
	examined how automated writing evaluation (AWE) can inform our understanding of
	the relationship between postsecondary writing skill and broader success
	outcomes. An exploratory study was conducted using test-taker essays from a
	standardized writing assessment of postsecondary student learning outcomes.
	Findings showed that for the essays, AWE features were found to be predictors
	of broader outcomes measures: college success and learning outcomes measures.
	Study findings illustrate AWE’s potential to support educational analytics --
	i.e., relationships between writing skill and broader outcomes -- taking a
	step toward moving AWE beyond writing assessment and instructional use cases.},
  url       = {http://www.aclweb.org/anthology/W17-5011}
}