Negative Binomial Models for Visual Fixation Counts on Test Items

Autor/inn/en	Man, Kaiwen; Harring, Jeffrey R.
Titel	Negative Binomial Models for Visual Fixation Counts on Test Items
Quelle	In: Educational and Psychological Measurement, 79 (2019) 4, S.617-635 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164418824148
Schlagwörter	Test Items; Eye Movements; Models; Regression (Statistics); Reaction Time; Item Response Theory; Response Style (Tests); Markov Processes; Monte Carlo Methods; Bayesian Statistics; Computation + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Augenbewegung; Analogiemodell; Regression; Regressionsanalyse; Reaktionsvermögen; Item-Response-Theorie; Antwortverhalten; Markowscher Prozess; Monte-Carlo-Methode
Abstract	With the development of technology-enhanced learning platforms, eye-tracking biometric indicators can be recorded simultaneously with students item responses. In the current study, visual fixation, an essential eye-tracking indicator, is modeled to reflect the degree of test engagement when a test taker solves a set of test questions. Three negative binomial regression models are proposed for modeling visual fixation counts of test takers solving a set of items. These models follow a similar structure to the lognormal response time model and the two-parameter logistic item response model. The proposed modeling structures include individualized latent person parameters reflecting the level of engagement of each test taker and two item parameters indicating the visual attention intensity and discriminating power of each test item. A Markov chain Monte Carlo estimation method is implemented for parameter estimation. Real data are fitted to the three proposed models, and the results are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01