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A generalized longitudinal mixture IRT model for measuring differential growth in learning environments

Journal Contribution - Journal Article

This paper describes a generalized longitudinal mixture item response theory (IRT)model that allows for detecting latent group differences in item response data obtained from electronic learning (e-learning) environments or other learning environments that result in a large number of items. The described model can be viewed as a combination of a longitudinal Rasch model, a mixture Rasch model, and a random item IRT model, and it includes some features of the explanatory IRT modeling framework. The model assumes the possible presence of latent classes in item response patterns either due to initial person level differences before learning takes place, or as a result of latent class-specific learning trajectories, or due to a combination of both. Moreover, it allows for differential item functioning over the classes. A Bayesian model estimation procedure is described and results of a simulation study are presented that indicate that the parameters are recovered well, particularly for conditions with large item sample sizes. The model is also illustrated with an empirical sample data set from a web-based e-learning environment.
Journal: Behavior Research Methods
ISSN: 1554-351X
Issue: 3
Volume: 46
Pages: 823 - 840
Publication year:2014
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:3
CSS-citation score:1
Authors from:Higher Education