Teaching iSTART to understand Spanish

Mihai Dascalu, Matthew E. Jacovina, Christian M. Soto, Laura K. Allen, Jianmin Dai, Tricia A. Guerrero, Danielle S. McNamara

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

3 Scopus citations

Abstract

iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage students to use comprehension strategies to generate self-explanations in response to challenging texts. Unsurprisingly, analyzing responses in a new language required many changes, such as implementing Spanish natural language processing tools and rebuilding lists of regular expressions used to flag responses. We also describe our use of an algorithm inspired from genetics to optimize the Fischer Discriminant Function Analysis coefficients used to determine self-explanation scores.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
EditorsElisabeth Andre, Xiangen Hu, Ma. Mercedes T. Rodrigo, Benedict du Boulay, Ryan Baker
PublisherSpringer Verlag
Pages485-489
Number of pages5
ISBN (Print)9783319614243
DOIs
StatePublished - 2017
Externally publishedYes
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

Conference

Conference18th International Conference on Artificial Intelligence in Education, AIED 2017
Country/TerritoryChina
CityWuhan
Period6/28/177/1/17

Bibliographical note

Funding Information:
This work was partially funded by the FP7 2008-212578 LTfLL project, by University Politehnica of Bucharest through the “Excellence Research Grants” Program UPB–GEX 12/26.09.2016, as well as by the Institute for the Science of Teaching & Learning (IES R305A130124) and the Office of Naval Research (ONR N000141410343 and ONR N00014-17-1-2300).

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

  • Intelligent tutoring systems
  • Natural language processing
  • Optimizing score prediction
  • Reading comprehension

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