Günter Nold

An Application of Explanatory Item Response Modeling for Model-Based Proficiency Scaling.

The article compares three different methods to estimate effects of task characteristics and to use these estimates for model-based proficiency scaling: prediction of item difficulties from the Rasch model, the linear logistic test model (LLTM), and an LLTM including random item effects (LLTM+e). The methods are applied to empirical data from a German large-scale study of reading comprehension in English as a foreign language (N = 10,543). A priori defined task characteristics were used as predictors for item difficulty; the estimated effects were used to define thresholds between proficiency levels. The comparison of results indicates that the LLTM is too restrictive; the Rasch model and the LLTM+e yield similar results in terms of implications for scale anchoring.

Teachers’ Professional Knowledge for Teaching English as a Foreign Language Assessing the Outcomes of Teacher Education

Despite an increasing research interest in subject-specific teacher knowledge, the scientific understanding regarding teachers’ professional knowledge for teaching English as a foreign language (TEFL) is very limited. This study therefore applies standardized tests to directly assess content knowledge (CK), pedagogical content knowledge (PCK), and general pedagogical knowledge (GPK) of preservice teachers for TEFL in Germany from different programs and stages during initial teacher education (during their master’s studies at university and at the end of their induction phase). Structural analysis provides evidence that teacher knowledge with respect to TEFL is a multidimensional construct and PCK is closely related to both CK and GPK. Test scores vary across preservice teachers from different programs and stages, which adequately reflects differences in the learning opportunities they had during teacher education.