Motivations for Using the Item Response Theory Nominal Response Model to Rank Responses to Multiple-Choice Items

Autor/inn/en	Smith, Trevor I.; Bendjilali, Nasrine
Titel	Motivations for Using the Item Response Theory Nominal Response Model to Rank Responses to Multiple-Choice Items
Quelle	In: Physical Review Physics Education Research, 18 (2022) 1, Artikel 010133 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Smith, Trevor I.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Item Response Theory; Test Items; Multiple Choice Tests; Science Tests; Responses; Evaluation Methods; Item Analysis; Knowledge Level; Probability; Physics; Correlation; Thermodynamics; Energy; Figurative Language; Equations (Mathematics) + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Itemanalyse; Wissensbasis; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Physik; Korrelation; Thermodynamik; Energie; Equations; Mathematics; Gleichungslehre
Abstract	Several recent studies have employed item response theory (IRT) to rank incorrect responses to commonly used research-based multiple-choice assessments. These studies use Bock's nominal response model (NRM) for applying IRT to categorical (nondichotomous) data, but the response rankings only utilize half of the parameters estimated by the model. We present a mathematical argument for why this practice of using half of the NRM parameters when ranking responses is appropriate based on the primary question of multiple-choice tests: How can we use students' responses to test items to estimate their overall knowledge levels? We provide additional motivation for this practice by recognizing the similarities between Bock's NRM and the probability function of the canonical ensemble with degenerate energy states. As physicists often do, we exploit these mathematical similarities to gain new insights into the meaning of the IRT parameters and a richer understanding of the relationship between these parameters and student knowledge. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01