Stairstepper: An adaptive remedial iSTART module

Cecile A. Perret, Amy Johnson, Kathryn S. McCarthy, Tricia A. Guerrero, Jianmin Dai, Danielle McNamara

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

5 Scopus citations

Abstract

This paper introduces StairStepper, a new addition to Interactive Strategy Training for Active Reading and Thinking (iSTART), an intelligent tutoring system (ITS) that provides adaptive self-explanation training and practice. Whereas iSTART focuses on improving comprehension at levels geared toward answering challenging questions associated with complex texts, StairStepper focuses on improving learners’ performance when reading grade-level expository texts. StairStepper is designed as a scaffolded practice activity wherein text difficulty level and task are adapted according to learners’ performance. This offers a unique module that provides reading comprehension tutoring through a combination of self-explanation practice and answering of multiple-choice questions representative of those found in standardized tests.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
PublisherSpringer Verlag
Pages557-560
Number of pages4
Volume10331 LNAI
ISBN (Print)9783319614243
DOIs
StatePublished - 2017
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

Other

Other18th International Conference on Artificial Intelligence in Education, AIED 2017
CountryChina
CityWuhan
Period6/28/177/1/17

Keywords

  • Game-based learning
  • Intelligent tutoring systems
  • Reading assessment
  • Reading comprehension
  • Strategy based learning
  • System adaptivity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Perret, C. A., Johnson, A., McCarthy, K. S., Guerrero, T. A., Dai, J., & McNamara, D. (2017). Stairstepper: An adaptive remedial iSTART module. In Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings (Vol. 10331 LNAI, pp. 557-560). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-61425-0_63