Implementation of VR learning activities for assessment purposes in teacher education: an evaluation approach using converging item response tree models

Although virtual reality (VR) classroom simulations are recognised as an effective way of promoting teaching skills, they are rarely used for assessment purposes in teacher training. This study examines an analysis strategy rooted in Item Response Tree (IRTree) modelling for estimating the psychometric quality of VR classroom simulations and to identify its implications for designing VR classroom simulations in teacher education, thereby making explicit how the structural properties of a VR classroom simulation determine the requirements of its psychometric evaluation. Data from 200 student teachers who participated in the VR classroom simulation CLASIVIR 1.0 were analysed. The results revealed good model fit and stable estimates, demonstrating a high level of measurement accuracy and strong potential of IRTree models replicating the expected structures of psychological constructs. Thus, the applicability of the proposed analysis strategy was proven, indicating that CLASIVIR 1.0 can be used to reliably measure classroom management competence. Implications derived from the study include suggestions regarding the structure of decision tree-based classroom simulations, sustainability considerations, and issues relating to digital skills.