Nonparametric Classification Method for Multiple-Choice Items in Cognitive Diagnosis

Autor/inn/en	Wang, Yu; Chiu, Chia-Yi; Köhn, Hans Friedrich
Titel	Nonparametric Classification Method for Multiple-Choice Items in Cognitive Diagnosis
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 2, S.189-219 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221133088
Schlagwörter	Multiple Choice Tests; Nonparametric Statistics; Test Format; Educational Assessment; Test Items; Responses; Diagnostic Tests; Classification; Algorithms; Student Evaluation; Learning Processes; Sample Size; Test Reliability; Item Response Theory + Suchen Sie Ihr Suchwort? Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Testentwicklung; Education; assessment; Bewertungssystem; Test content; Testaufgabe; Diagnostic test; Diagnostischer Test; Classification system; Klassifikation; Klassifikationssystem; Algorithm; Algorithmus; Schulnote; Studentische Bewertung; Learning process; Lernprozess; Testreliabilität; Item-Response-Theorie
Abstract	The multiple-choice (MC) item format has been widely used in educational assessments across diverse content domains. MC items purportedly allow for collecting richer diagnostic information. The effectiveness and economy of administering MC items may have further contributed to their popularity not just in educational assessment. The MC item format has also been adapted to the cognitive diagnosis (CD) framework. Early approaches simply dichotomized the responses and analyzed them with a CD model for binary responses. Obviously, this strategy cannot exploit the additional diagnostic information provided by MC items. De la Torre's MC Deterministic Inputs, Noisy "And" Gate (MC-DINA) model was the first for the explicit analysis of items having MC response format. However, as a drawback, the attribute vectors of the distractors are restricted to be nested within the key and each other. The method presented in this article for the CD of DINA items having MC response format does not require such constraints. Another contribution of the proposed method concerns its implementation using a nonparametric classification algorithm, which predestines it for use especially in small-sample settings like classrooms, where CD is most needed for monitoring instruction and student learning. In contrast, default parametric CD estimation routines that rely on EM- or MCMC-based algorithms cannot guarantee stable and reliable estimates--despite their effectiveness and efficiency when samples are large--due to computational feasibility issues caused by insufficient sample sizes. Results of simulation studies and a real-world application are also reported. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01