@InProceedings{roy-dandapat-narahari:2016:NLPTEA2016,
  author    = {Roy, Shourya  and  Dandapat, Sandipan  and  Narahari, Y.},
  title     = {A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {82--91},
  abstract  = {We offer a fluctuation smoothing computational approach for unsupervised
	automatic short answer
	grading (ASAG) techniques in the educational ecosystem. A major drawback of the
	existing
	techniques is the significant effect that variations in model answers could
	have on their
	performances. The proposed fluctuation smoothing approach, based on classical
	sequential pattern
	mining, exploits lexical overlap in students’ answers to any typical
	question. We empirically
	demonstrate using multiple datasets that the proposed approach improves the
	overall performance
	and significantly reduces (up to 63%) variation in performance (standard
	deviation) of unsupervised
	ASAG techniques. We bring in additional benchmarks such as (a) paraphrasing of
	model
	answers and (b) using answers by k top performing students as model answers, to
	amplify the
	benefits of the proposed approach.},
  url       = {http://aclweb.org/anthology/W16-4911}
}