Learning from errors: Identifying strategies in a math tutoring system

Jun Xie; Keith Shubeck; Scotty Craig; Xiangen Hu

doi:10.1007/978-3-319-61425-0_71

Learning from errors: Identifying strategies in a math tutoring system

Jun Xie, Keith Shubeck, Scotty Craig, Xiangen Hu

Human Systems Engineering (HSE)

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
Editors	Elisabeth Andre, Xiangen Hu, Ma. Mercedes T. Rodrigo, Benedict du Boulay, Ryan Baker
Publisher	Springer Verlag
Pages	590-593
Number of pages	4
ISBN (Print)	9783319614243
DOIs	https://doi.org/10.1007/978-3-319-61425-0_71
State	Published - 2017
Event	18th International Conference on Artificial Intelligence in Education, AIED 2017 - Wuhan, China Duration: Jun 28 2017 → Jul 1 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10331 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th International Conference on Artificial Intelligence in Education, AIED 2017
Country/Territory	China
City	Wuhan
Period	6/28/17 → 7/1/17

Keywords

Errors and learning
Help-seeking
Intelligent tutoring systems
Math

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-61425-0_71

Cite this

Xie, J., Shubeck, K., Craig, S., & Hu, X. (2017). Learning from errors: Identifying strategies in a math tutoring system. In E. Andre, X. Hu, M. M. T. Rodrigo, B. du Boulay, & R. Baker (Eds.), Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings (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

Learning from errors: Identifying strategies in a math tutoring system. / Xie, Jun; Shubeck, Keith; Craig, Scotty et al.
Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. ed. / Elisabeth Andre; Xiangen Hu; Ma. Mercedes T. Rodrigo; Benedict du Boulay; Ryan Baker. Springer Verlag, 2017. p. 590-593 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10331 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xie, J, Shubeck, K, Craig, S & Hu, X 2017, Learning from errors: Identifying strategies in a math tutoring system. in E Andre, X Hu, MMT Rodrigo, B du Boulay & R Baker (eds), Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10331 LNAI, Springer Verlag, pp. 590-593, 18th International Conference on Artificial Intelligence in Education, AIED 2017, Wuhan, China, 6/28/17. https://doi.org/10.1007/978-3-319-61425-0_71

Xie J, Shubeck K, Craig S, Hu X. Learning from errors: Identifying strategies in a math tutoring system. In Andre E, Hu X, Rodrigo MMT, du Boulay B, Baker R, editors, Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. Springer Verlag. 2017. p. 590-593. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61425-0_71

Xie, Jun ; Shubeck, Keith ; Craig, Scotty et al. / Learning from errors : Identifying strategies in a math tutoring system. Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings. editor / Elisabeth Andre ; Xiangen Hu ; Ma. Mercedes T. Rodrigo ; Benedict du Boulay ; Ryan Baker. Springer Verlag, 2017. pp. 590-593 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{615823bf30ae4340a645986850c8ba6d,

title = "Learning from errors: Identifying strategies in a math tutoring system",

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.",

keywords = "Errors and learning, Help-seeking, Intelligent tutoring systems, Math",

author = "Jun Xie and Keith Shubeck and Scotty Craig and Xiangen Hu",

note = "Funding Information: This research was supported by the Institute for Education Sciences (IES) Grant R305A090528 to Dr. Xiangen Hu, PI. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of IES. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th International Conference on Artificial Intelligence in Education, AIED 2017 ; Conference date: 28-06-2017 Through 01-07-2017",

year = "2017",

doi = "10.1007/978-3-319-61425-0_71",

language = "English (US)",

isbn = "9783319614243",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "590--593",

editor = "Elisabeth Andre and Xiangen Hu and Rodrigo, {Ma. Mercedes T.} and {du Boulay}, Benedict and Ryan Baker",

booktitle = "Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings",

}

TY - GEN

T1 - Learning from errors

T2 - 18th International Conference on Artificial Intelligence in Education, AIED 2017

AU - Xie, Jun

AU - Shubeck, Keith

AU - Craig, Scotty

AU - Hu, Xiangen

N1 - Funding Information: This research was supported by the Institute for Education Sciences (IES) Grant R305A090528 to Dr. Xiangen Hu, PI. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of IES. Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Errors and learning

KW - Help-seeking

KW - Intelligent tutoring systems

KW - Math

UR - http://www.scopus.com/inward/record.url?scp=85022197784&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85022197784&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-61425-0_71

DO - 10.1007/978-3-319-61425-0_71

M3 - Conference contribution

AN - SCOPUS:85022197784

SN - 9783319614243

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 590

EP - 593

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

Y2 - 28 June 2017 through 1 July 2017

ER -

Learning from errors: Identifying strategies in a math tutoring system

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this