Automated Scoring of Self-explanations Using Recurrent Neural Networks

Marilena Panaite, Stefan Ruseti, Mihai Dascalu, Renu Balyan, Danielle S. McNamara, Stefan Trausan-Matu

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

Abstract

Intelligent Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students’ practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring student self-explanations that are targeted at specific sentences. The first stage of the processing pipeline performs an initial text cleaning and applies a set of predefined rules established by human experts in order to identify specific cases (e.g., students who do not understand the text, or students who simply copy and paste their self-explanations from the given input text). The second step uses a Recurrent Neural Network with pre-trained Glove word embeddings to predict self-explanation scores on a scale of 1 to 3. In contrast to previous SVM models trained on the same dataset of 4109 self-explanations, we obtain a significant increase of accuracy from 59% to 73%. Moreover, the new pipeline can be integrated in learning scenarios requiring near real-time responses from the ITS, thus addressing a major limitation in terms of processing speed exhibited by the previous approach.

Original languageEnglish (US)
Title of host publicationTransforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings
EditorsMaren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider
PublisherSpringer Verlag
Pages659-663
Number of pages5
ISBN (Print)9783030297350
DOIs
StatePublished - Jan 1 2019
Event14th European Conference on Technology Enhanced Learning, EC-TEL 2019 - Delft, Netherlands
Duration: Sep 16 2019Sep 19 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11722 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Technology Enhanced Learning, EC-TEL 2019
CountryNetherlands
CityDelft
Period9/16/199/19/19

Keywords

  • Comprehensive tutoring system
  • Natural Language Processing
  • Recurrent Neural Network
  • Self-explanations

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Panaite, M., Ruseti, S., Dascalu, M., Balyan, R., McNamara, D. S., & Trausan-Matu, S. (2019). Automated Scoring of Self-explanations Using Recurrent Neural Networks. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings (pp. 659-663). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11722 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-29736-7_61