TY - GEN
T1 - Proper and Useful Distractors in Multiple-Choice Diagnostic Classification Models
AU - Köhn, Hans Friedrich
AU - Chiu, Chia Yi
AU - Wang, Yu
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The multiple-choice (MC) item format has been implemented in educational assessments that are used across diverse content domains. MC items comprise two components: the stem that provides the context with a motivating narrative, and the collection of response options consisting of the correct answer, called the “key,” and several incorrect alternatives, the “distractors.” The MC-DINA model was the first diagnostic classification model for MC items that used distractors explicitly as potential sources of diagnostic information. However, the MC-DINA model requires that the q-vectors of the distractors are nested within each other and that of the key, which poses a serious constraint on item development. Consequently, later adaptations of the MC item format to cognitive diagnosis dropped the nestedness condition. The relaxation of the nestedness-condition, however, comes at a price: distractors may become redundant (i.e., they do not contribute to any further diagnostic differentiation between examinees), and they may induce undesirable diagnostic ambiguity (i.e., they are equally likely to be chosen by an examinee, but their q-vectors point at different diagnostic classifications). In this article, two criteria, useful and proper, are proposed to identify redundant and diagnostically ambiguous distractors.
AB - The multiple-choice (MC) item format has been implemented in educational assessments that are used across diverse content domains. MC items comprise two components: the stem that provides the context with a motivating narrative, and the collection of response options consisting of the correct answer, called the “key,” and several incorrect alternatives, the “distractors.” The MC-DINA model was the first diagnostic classification model for MC items that used distractors explicitly as potential sources of diagnostic information. However, the MC-DINA model requires that the q-vectors of the distractors are nested within each other and that of the key, which poses a serious constraint on item development. Consequently, later adaptations of the MC item format to cognitive diagnosis dropped the nestedness condition. The relaxation of the nestedness-condition, however, comes at a price: distractors may become redundant (i.e., they do not contribute to any further diagnostic differentiation between examinees), and they may induce undesirable diagnostic ambiguity (i.e., they are equally likely to be chosen by an examinee, but their q-vectors point at different diagnostic classifications). In this article, two criteria, useful and proper, are proposed to identify redundant and diagnostically ambiguous distractors.
KW - Cognitive diagnosis
KW - MC-DINA
KW - MC-NPC
KW - Nonparametric cognitive diagnosis
KW - Polytomous items
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U2 - 10.1007/978-3-031-27781-8_9
DO - 10.1007/978-3-031-27781-8_9
M3 - Conference contribution
AN - SCOPUS:85164657659
SN - 9783031277801
T3 - Springer Proceedings in Mathematics and Statistics
SP - 97
EP - 106
BT - Quantitative Psychology - The 87th Annual Meeting of the Psychometric Society, 2022
A2 - Wiberg, Marie
A2 - Molenaar, Dylan
A2 - González, Jorge
A2 - Kim, Jee-Seon
A2 - Hwang, Heungsun
PB - Springer
T2 - 87th Annual Meeting of the Psychometric Society, IMPS 2022
Y2 - 11 July 2022 through 15 July 2022
ER -