Predicting question quality using recurrent neural networks

Stefan Ruseti, Mihai Dascalu, Amy Johnson, Renu Balyan, Kristopher J. Kopp, Danielle McNamara, Scott A. Crossley, Stefan Trausan-Matu

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

3 Scopus citations

Abstract

This study assesses the extent to which machine learning techniques can be used to predict question quality. An algorithm based on textual complexity indices was previously developed to assess question quality to provide feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). In this study, 4,575 questions were coded by human raters based on their corresponding depth, classifying questions into four categories: 1-very shallow to 4-very deep. Here we propose a novel approach to assessing question quality within this dataset based on Recurrent Neural Networks (RNNs) and word embeddings. The experiments evaluated multiple RNN architectures using GRU, BiGRU and LSTM cell types of different sizes, and different word embeddings (i.e., FastText and Glove). The most precise model achieved a classification accuracy of 81.22%, which surpasses the previous prediction results using lexical sophistication complexity indices (accuracy = 41.6%). These results are promising and have implications for the future development of automated assessment tools within computer-based learning environments.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
EditorsManolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, H. Ulrich Hoppe, Rose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Roberto Martinez-Maldonado
PublisherSpringer Verlag
Pages491-502
Number of pages12
ISBN (Print)9783319938424
DOIs
StatePublished - 2018
Event19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom
Duration: Jun 27 2018Jun 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10947 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Artificial Intelligence in Education, AIED 2018
CountryUnited Kingdom
CityLondon
Period6/27/186/30/18

Keywords

  • Question asking
  • Recurrent neural network
  • Word embeddings

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ruseti, S., Dascalu, M., Johnson, A., Balyan, R., Kopp, K. J., McNamara, D., Crossley, S. A., & Trausan-Matu, S. (2018). Predicting question quality using recurrent neural networks. In M. Mavrikis, C. Penstein Rosé, B. McLaren, H. U. Hoppe, R. Luckin, K. Porayska-Pomsta, B. du Boulay, & R. Martinez-Maldonado (Eds.), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings (pp. 491-502). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10947 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93843-1_36