A mixture IRT growth model for longitudinal data from e-learning environments
Damazo Twebaze Kadengye, Eva Ceulemans, Wim Van Den Noortgate · Lirias · 2013
This poster describes a generalized longitudinal mixture item response theory (IRT) model that allows for detecting latent group differences for item response data obtained from electronic learning (e-learning) environments or other 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, a random item IRT model, and includes some features of the explanatory IRT modeling framework. The model assumes 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, and 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, as well as for balanced sample designs. Keywords: Item Response Theory, e-Learning, Modelling of Growth, Mixture Models.