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.
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Acknowledgements
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).
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Dascalu, M. et al. (2017). Teaching iSTART to Understand Spanish. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_46
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DOI: https://doi.org/10.1007/978-3-319-61425-0_46
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