In latent space item response theory (IRT) modelling, both subjects and items are positioned in R dimensional Euclidian latent space. This framework allows for detailed modelling of local dependences among items and subjects, which are assumed to be absent in conventional IRT models. Latent space IRT has demonstrated its value in diverse fields, including intelligence assessment (Kang & Jeon,...
The cornerstone of psychometrics –factor analytical methods –is designed for the interpretable dimensional reduction of response accuracy vector data. This approach can be likened to Variational AutoEncoders (VAEs) with shallow decoders (Urban & Bauer, 2021). However, it is not suitable for analyzing raw process data due to its inability to account for autoregressive dependencies within...
Amortized variational inference (AVI) has recently been proposed in the field of Item response theory as a computationally efficient alternative to marginal maximum likelihood estimation (MML). The current study investigates if the computational advantages of AVI for large, high dimensional data carry over to discrete latent variable models. We adapt three techniques from the machine learning...
Automated essay scoring systems can support teachers by providing rapid, cost-effective verbal and numerical feedback on student writing. In recent years, these systems have improved significantly with the rise of generative artificial intelligence models based on the transformer architecture. Research consistently shows that these models outperform traditional machine learning approaches...
