TY - GEN
T1 - Teaching iSTART to understand Spanish
AU - Dascalu, Mihai
AU - Jacovina, Matthew E.
AU - Soto, Christian M.
AU - Allen, Laura K.
AU - Dai, Jianmin
AU - Guerrero, Tricia A.
AU - McNamara, Danielle
N1 - 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.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Intelligent tutoring systems
KW - Natural language processing
KW - Optimizing score prediction
KW - Reading comprehension
UR - http://www.scopus.com/inward/record.url?scp=85022180889&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022180889&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61425-0_46
DO - 10.1007/978-3-319-61425-0_46
M3 - Conference contribution
AN - SCOPUS:85022180889
SN - 9783319614243
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 485
EP - 489
BT - Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
A2 - Andre, Elisabeth
A2 - Hu, Xiangen
A2 - Rodrigo, Ma. Mercedes T.
A2 - du Boulay, Benedict
A2 - Baker, Ryan
PB - Springer Verlag
T2 - 18th International Conference on Artificial Intelligence in Education, AIED 2017
Y2 - 28 June 2017 through 1 July 2017
ER -