@inproceedings{fe54aa2312b9470d936d910e231aac41,
title = "Assessing question quality using NLP",
abstract = "An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict question quality. NLP indices related to lexical sophistication modestly predicted question type. Accuracies improved when predicting two levels (shallow versus deep).",
keywords = "Artificial intelligence, Educational technology design, Intelligent tutoring systems, Natural language processing, Question classification",
author = "Kopp, {Kristopher J.} and Amy Johnson and Crossley, {Scott A.} and Danielle McNamara",
note = "Funding Information: This research was supported in part by the Institute for Educational Sciences (IES R305A130124) and the Office of Naval Research (ONR N00014-14-1-0343 and ONR N00014-17-1-2300). Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of IES or ONR. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th International Conference on Artificial Intelligence in Education, AIED 2017 ; Conference date: 28-06-2017 Through 01-07-2017",
year = "2017",
doi = "10.1007/978-3-319-61425-0_55",
language = "English (US)",
isbn = "9783319614243",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "523--527",
editor = "Elisabeth Andre and Xiangen Hu and Rodrigo, {Ma. Mercedes T.} and {du Boulay}, Benedict and Ryan Baker",
booktitle = "Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings",
}