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Erroneous examples as desirable difficulty. / Adams, Deanne M.; McLaren, Bruce M.; Mayer, Richard E. et al.
Artificial Intelligence in Education: 16th international conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013 ; proceedings. ed. / H.C. Lane; K. Yacef; J. Mostow; P. Pavlik. Berlin: Springer, 2013. p. 803-806 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Adams, DM, McLaren, BM, Mayer, RE
, Goguadze, G & Isotani, S 2013,
Erroneous examples as desirable difficulty. in HC Lane, K Yacef, J Mostow & P Pavlik (eds),
Artificial Intelligence in Education: 16th international conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013 ; proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7926 LNAI, Springer, Berlin, pp. 803-806, 16th International Conference on Artificial Intelligence in Education - AIED 2013, Memphis, United States,
09.07.13.
https://doi.org/10.1007/978-3-642-39112-5_117APA
Adams, D. M., McLaren, B. M., Mayer, R. E.
, Goguadze, G., & Isotani, S. (2013).
Erroneous examples as desirable difficulty. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.),
Artificial Intelligence in Education: 16th international conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013 ; proceedings (pp. 803-806). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). Springer.
https://doi.org/10.1007/978-3-642-39112-5_117Vancouver
Adams DM, McLaren BM, Mayer RE
, Goguadze G, Isotani S.
Erroneous examples as desirable difficulty. In Lane HC, Yacef K, Mostow J, Pavlik P, editors, Artificial Intelligence in Education: 16th international conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013 ; proceedings. Berlin: Springer. 2013. p. 803-806. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-39112-5_117
Bibtex
@inbook{29a8aa262d984e3c88021067fbdacd6e,
title = "Erroneous examples as desirable difficulty",
abstract = "Erroneous examples, an unusual and challenging form of learning material, are arguably a type of desirable difficulty for students that could lead to deeper learning. In a series of studies we have done over the past three years involving web-based math instruction, the learning benefits of erroneous examples we have observed occured on delayed tests, as occurs in the desirable difficulties literature. This short paper briefly reviews the literature, summarizes our results, and speculates on how an adaptive version of our materials could better leverage desirable difficulties theory and lead to deeper student learning.",
keywords = "Mathematics, Adaptation of problems, Decimals, Erroneous examples, Interactive problem solving, Mathematics education, Self-explanation",
author = "Adams, {Deanne M.} and McLaren, {Bruce M.} and Mayer, {Richard E.} and George Goguadze and Seiji Isotani",
year = "2013",
doi = "10.1007/978-3-642-39112-5_117",
language = "English",
isbn = "978-3-642-39111-8",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "803--806",
editor = "H.C. Lane and K. Yacef and J. Mostow and P. Pavlik",
booktitle = "Artificial Intelligence in Education",
address = "Germany",
note = "16th International Conference on Artificial Intelligence in Education - AIED 2013, AIED Conference 2013 ; Conference date: 09-07-2013 Through 13-07-2013",
url = "https://sites.google.com/a/iis.memphis.edu/aied-2013-conference/",
}
RIS
TY - CHAP
T1 - Erroneous examples as desirable difficulty
AU - Adams, Deanne M.
AU - McLaren, Bruce M.
AU - Mayer, Richard E.
AU - Goguadze, George
AU - Isotani, Seiji
N1 - Conference code: 16
PY - 2013
Y1 - 2013
N2 - Erroneous examples, an unusual and challenging form of learning material, are arguably a type of desirable difficulty for students that could lead to deeper learning. In a series of studies we have done over the past three years involving web-based math instruction, the learning benefits of erroneous examples we have observed occured on delayed tests, as occurs in the desirable difficulties literature. This short paper briefly reviews the literature, summarizes our results, and speculates on how an adaptive version of our materials could better leverage desirable difficulties theory and lead to deeper student learning.
AB - Erroneous examples, an unusual and challenging form of learning material, are arguably a type of desirable difficulty for students that could lead to deeper learning. In a series of studies we have done over the past three years involving web-based math instruction, the learning benefits of erroneous examples we have observed occured on delayed tests, as occurs in the desirable difficulties literature. This short paper briefly reviews the literature, summarizes our results, and speculates on how an adaptive version of our materials could better leverage desirable difficulties theory and lead to deeper student learning.
KW - Mathematics
KW - Adaptation of problems
KW - Decimals
KW - Erroneous examples
KW - Interactive problem solving
KW - Mathematics education
KW - Self-explanation
UR - http://www.scopus.com/inward/record.url?scp=84880034888&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39112-5_117
DO - 10.1007/978-3-642-39112-5_117
M3 - Article in conference proceedings
AN - SCOPUS:84880034888
SN - 978-3-642-39111-8
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 803
EP - 806
BT - Artificial Intelligence in Education
A2 - Lane, H.C.
A2 - Yacef, K.
A2 - Mostow, J.
A2 - Pavlik, P.
PB - Springer
CY - Berlin
T2 - 16th International Conference on Artificial Intelligence in Education - AIED 2013
Y2 - 9 July 2013 through 13 July 2013
ER -
