Learning from errors: Identifying strategies in a math tutoring system

Jun Xie, Keith Shubeck, Scotty Craig, Xiangen Hu

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

Abstract

This study attempts to investigate how students gain knowledge by utilizing help and practice after making errors. We define three types of strategies used by students after errors: help-seeking (requesting two worked examples in the next attempts after an error), practice (solving the problems in the next two attempts after an error), and mixed (first requesting a worked example or first solving a problem in the next two attempts after an error). Our results indicate that the most frequently used strategies are help and mixed strategies. However, the practice strategy and mixed strategies facilitate immediate performance improvement. Additionally, the help strategy was found to interfere with delayed performance.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
PublisherSpringer Verlag
Pages590-593
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

  • Errors and learning
  • Help-seeking
  • Intelligent tutoring systems
  • Math

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xie, J., Shubeck, K., Craig, S., & Hu, X. (2017). Learning from errors: Identifying strategies in a math tutoring system. In Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings (Vol. 10331 LNAI, pp. 590-593). (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_71