Abstract

Automatic rule-based generation of items is a promising method for item cloning that saves a considerable amount of time and money. Items are grouped in families that are created through the application of common rules and cloned from the same parent item. Within a family, the items are assumed to differ only in surface features. Infinitely many items may be developed on the fly, especially suited for adaptive testing. Only the item families have to be calibrated, content validity and test quality criteria of item response models are usually increased. In this presentation, a short overview of the generation of rule-based items is given. Software programs will be shown which adaptively generate tailor-made test items of intelligence and probability word problems. Optimal test designs and hierarchical item response models for rule-based generated items will be introduced. Results from several empirical studies show a good fit to these item response models. Practical implications of these findings for future test development in the assessment of intelligence and probability competencies are discussed.