TY - GEN
T1 - Analyzing Multiple-Choice-Multiple-Response Items Using Item Response Theory
AU - Smith, Trevor I.
AU - Eaton, Philip
AU - Brahmia, Suzanne White
AU - Olsho, Alexis
AU - Zimmerman, Charlotte
AU - Boudreaux, Andrew
N1 - Funding Information:
This work was supported by the National Science Foundation under Grants No. DUE-1832836, No. DUE-1832880, and No DUE-1833050. One author holds an NRC Research Associateship award at Air Force Research Laboratory.
Publisher Copyright:
© 2022, American Association of Physics Teachers. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Multiple-choice-multiple-response (MCMR) items allow students to select as many responses as they think are correct to a given question stem. Using MCMR items can provide researchers and instructors with a richer and more complete picture of what students do and do not understand about a particular topic. Interpreting students’ MCMR responses is more nuanced than it is for single-response items. Unfortunately, many typical analyses of data from multiple-choice tests assume dichotomously-scored items, which eliminates the possibility of incorporating the rich information from students’ response patterns to MCMR items. We present a novel methodology for using a combination of item response theory models to analyze data from MCMR items. These methods could be applied to inform scoring models that incorporate partial credit for various response patterns.
AB - Multiple-choice-multiple-response (MCMR) items allow students to select as many responses as they think are correct to a given question stem. Using MCMR items can provide researchers and instructors with a richer and more complete picture of what students do and do not understand about a particular topic. Interpreting students’ MCMR responses is more nuanced than it is for single-response items. Unfortunately, many typical analyses of data from multiple-choice tests assume dichotomously-scored items, which eliminates the possibility of incorporating the rich information from students’ response patterns to MCMR items. We present a novel methodology for using a combination of item response theory models to analyze data from MCMR items. These methods could be applied to inform scoring models that incorporate partial credit for various response patterns.
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U2 - 10.1119/perc.2022.pr.Smith
DO - 10.1119/perc.2022.pr.Smith
M3 - Conference contribution
AN - SCOPUS:85140442810
SN - 9781931024389
T3 - Physics Education Research Conference Proceedings
SP - 432
EP - 437
BT - Physics Education Research Conference, 2022
A2 - Frank, Brian
A2 - Jones, Dyan
A2 - Ryan, Qing
PB - American Association of Physics Teachers
T2 - Physics Education Research Conference, PERC 2022
Y2 - 13 July 2022 through 14 July 2022
ER -