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iSTART-Early: Interactive Strategy Training for Early Readers

Authors :
Panayiota Kendeou
Ellen Orcutt
Tracy Arner
Tong Li
Renu Balyan
Reese Butterfuss
Micah Watanabe
Danielle McNamara
Source :
Grantee Submission. 2022.
Publication Year :
2022

Abstract

In this paper, we present iSTART-Early, an intelligent tutoring system that provides automated instruction and practice on higher-order reading comprehension strategies to 3rd and 4th grade students. iSTART-Early provides personalized, interactive, game-based strategy instruction and practice on comprehension strategies (i.e., Ask It, Reword It, Find It, Explain It, and Summarize It) with grade-appropriate informational texts. Natural language processing (NLP) combined with automated speech recognition (ASR) and text-to-speech technologies enable immediate formative and summative feedback. A teacher interface allows teachers to assign texts and monitor students' performance so that they can provide additional support and feedback when necessary, creating blended-learning opportunities. We describe the interface and the development of iSTART-Early, as well as our plans to examine the intelligent tutoring system for usability, feasibility and promise in improving reading comprehension strategies and outcomes for young readers. [This paper was published in: S. Crossley and E. Popescu, Eds., "ITS 2022, LNCS 13284," Springer Nature, 2022, pp.371-379.]

Details

Language :
English
Database :
ERIC
Journal :
Grantee Submission
Publication Type :
Conference
Accession number :
ED637291
Document Type :
Speeches/Meeting Papers<br />Reports - Descriptive
Full Text :
https://doi.org/10.1007/978-3-031-09680-8_35