2 Citations (Scopus)

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).

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
PublisherSpringer Verlag
Pages523-527
Number of pages5
Volume10331 LNAI
ISBN (Print)9783319614243
DOIs
StatePublished - 2017
Event18th International Conference on Artificial Intelligence in Education, AIED 2017 - Wuhan, China
Duration: Jun 28 2017Jul 1 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10331 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Artificial Intelligence in Education, AIED 2017
CountryChina
CityWuhan
Period6/28/177/1/17

Fingerprint

Taxonomies
Intelligent systems
Learning systems
Students
Feedback
Intelligent Tutoring Systems
Taxonomy
Machine Learning
Predict
Strategy
Teaching
Corpus

Keywords

  • Artificial intelligence
  • Educational technology design
  • Intelligent tutoring systems
  • Natural language processing
  • Question classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kopp, K. J., Johnson, A., Crossley, S. A., & McNamara, D. (2017). Assessing question quality using NLP. In Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings (Vol. 10331 LNAI, pp. 523-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-61425-0_55

Assessing question quality using NLP. / Kopp, Kristopher J.; Johnson, Amy; Crossley, Scott A.; McNamara, Danielle.

Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. Vol. 10331 LNAI Springer Verlag, 2017. p. 523-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kopp, KJ, Johnson, A, Crossley, SA & McNamara, D 2017, Assessing question quality using NLP. in Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. vol. 10331 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10331 LNAI, Springer Verlag, pp. 523-527, 18th International Conference on Artificial Intelligence in Education, AIED 2017, Wuhan, China, 6/28/17. https://doi.org/10.1007/978-3-319-61425-0_55
Kopp KJ, Johnson A, Crossley SA, McNamara D. Assessing question quality using NLP. In Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. Vol. 10331 LNAI. Springer Verlag. 2017. p. 523-527. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-61425-0_55
Kopp, Kristopher J. ; Johnson, Amy ; Crossley, Scott A. ; McNamara, Danielle. / Assessing question quality using NLP. Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. Vol. 10331 LNAI Springer Verlag, 2017. pp. 523-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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