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Predicting Question Quality Using Recurrent Neural Networks

  • Stefan Ruseti
  • Mihai DascaluEmail author
  • Amy M. Johnson
  • Renu Balyan
  • Kristopher J. Kopp
  • Danielle S. McNamara
  • Scott A. Crossley
  • Stefan Trausan-Matu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10947)

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.

Keywords

Question asking Recurrent neural network Word embeddings 

Notes

Acknowledgments

This research was partially supported by the 644187 EC H2020 Realising an Applied Gaming Eco-system (RAGE) project, the FP7 2008-212578 LTfLL project, the Department of Education, Institute of Education Sciences - Grant R305A130124, as well as the Department of Defense, Office of Naval Research - Grants N00014140343 and N000141712300.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stefan Ruseti
    • 1
  • Mihai Dascalu
    • 1
    • 2
    Email author
  • Amy M. Johnson
    • 3
  • Renu Balyan
    • 3
  • Kristopher J. Kopp
    • 3
  • Danielle S. McNamara
    • 3
  • Scott A. Crossley
    • 4
  • Stefan Trausan-Matu
    • 1
    • 2
  1. 1.Faculty of Automatic Control and ComputersUniversity “Politehnica” of BucharestBucharestRomania
  2. 2.Academy of Romanian ScientistsBucharestRomania
  3. 3.Institute for the Science of Teaching and LearningArizona State UniversityTempeUSA
  4. 4.Department of Applied Linguistics/ESLGeorgia State UniversityAtlantaUSA

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