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).
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Acknowledgments
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.
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Kopp, K.J., Johnson, A.M., Crossley, S.A., McNamara, D.S. (2017). Assessing Question Quality Using NLP. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_55
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DOI: https://doi.org/10.1007/978-3-319-61425-0_55
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