The Use of IRT for Adaptive Item Selection in Item-Based Learning Environments
Kelly Wauters, Wim Van Den Noortgate, Desmet Piet · Frontiers in artificial intelligence and applications · 2009
The popularity of learning environments is increasing rapidly. In order to make learning environments more efficient, researchers have been matching the item difficulty to the learner's proficiency, as is done in computerized adaptive testing (CAT) by means of the item response theory (IRT). Even though some researchers have already implemented ideas of CAT and IRT for adaptive item selection in learning environments, some differences between testing and learning environments have been overlooked. In this study we focus on those differences that may require an adaptation of these existing CAT and IRT methods.