BACKGROUND Despite the widespread use of multiple-choice assessments in medical education assessment, current practice and published advice concerning the number of response options remains equivocal. This article describes an empirical study contrasting the quality of three 60 item multiple-choice test forms within the Royal Australian and New Zealand College of Obstetricians and Gynaecologist...

The bifactor structure has been considered a credible model for various types of data. Research purposes were: (a) to test the utility of the bifactor IRT model for dimensionality assessment and (b) to confirmatory factor analyze the structure of the newly-developed scale, the Job Burnout Scale for Chinese College Teachers. 1331 teachers were chosen from 11 various Universities in China. 1111(8...

Reliable student models are vital for the correct functioning of Intelligent Tutoring Systems. This means that diagnosis tools used to update the student models must be also reliable. Through adaptive testing, student knowledge can be inferred. The tests are based on a psychometric theory, the Item Response Theory. In this theory, each question has a function assigned that is essential for dete...

Computer adaptive testing (CAT) is theoretically sound and efficient, and is commonly seen in larger testing programs. It is, however, rarely seen in a smaller-scale scenario, such as in classrooms or business daily routines, because of the complexity of most adopted Item Response Theory (IRT) models. While the Sequential Probability Ratio Test (SPRT) model is less complicated, it only provides...

As many states are slated to soon use scores derived from classroom observation instruments in high-stakes decisions, developers must cultivate methods for improving the functioning of these instruments. We show how multidimensional, multilevel item response theory models can yield information critical for improving the performance of observational instruments.

We develop T-SKIRT: a temporal, structuredknowledge, IRT-based method for predicting student responses online. By explicitly accounting for student learning and employing a structured, multidimensional representation of student proficiencies, the model outperforms standard IRTbased methods on an online response prediction task when applied to real responses collected from students interacting w...