Analysis Test of Understanding of Vectors with the Three-Parameter Logistic Model of Item Response Theory and Item Response Curves Technique

Autor/inn/en	Rakkapao, Suttida; Prasitpong, Singha; Arayathanitkul, Kwan
Titel	Analysis Test of Understanding of Vectors with the Three-Parameter Logistic Model of Item Response Theory and Item Response Curves Technique
Quelle	In: Physical Review Physics Education Research, 12 (2016) 2, S.020135-1 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2469-9896
DOI	10.1103/PhysRevPhysEducRes.12.020135
Schlagwörter	Item Response Theory; Multiple Choice Tests; Difficulty Level; Test Items; Guessing (Tests); College Freshmen; Foreign Countries; Science Tests; Physics; Scientific Concepts; Thailand + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Schwierigkeitsgrad; Test content; Testaufgabe; Erraten; Studienanfänger; Ausland; Physik
Abstract	This study investigated the multiple-choice test of understanding of vectors (TUV), by applying item response theory (IRT). The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming unidimensionality and local independence. Moreover, all distractors of the TUV were analyzed from item response curves (IRC) that represent simplified IRT. Data were gathered on 2,392 science and engineering freshmen, from three universities in Thailand. The results revealed IRT analysis to be useful in assessing the test since its item parameters are independent of the ability parameters. The IRT framework reveals item-level information, and indicates appropriate ability ranges for the test. Moreover, the IRC analysis can be used to assess the effectiveness of the test's distractors. Both IRT and IRC approaches reveal test characteristics beyond those revealed by the classical analysis methods of tests. Test developers can apply these methods to diagnose and evaluate the features of items at various ability levels of test takers. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01