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The present study seeks to investigate the determinants and intercorrelations between teachers’ and pupils’ well-being and motivation in school context. Based on self-determination theory (SDT ; Gagné et al., 2010) and job demands-resources theory (JD-R ; Bakker & Demerouti, 2014), we hypothesize that (a) teachers' self-concordant motivation and work-related well-being, and pupils’ school-related well-being fluctuate on a within-person level, (b) these fluctuations in teachers’ and pupils’ well-being are positively related, and (c) there is an upward spiral between teachers' and pupils' motivation and work/school-related well-being on a within-person level. A quantitative weekly diary methodology is employed. Participants are primary school pupils and their teachers from six European countries.

Funder: Radboud Universiteit, Within predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning).

The current and future capabilities of Artificial Intelligence (AI) are typically assessed with an ever increasing number of benchmarks, competitions, tests and evaluation standards, which are meant to work as AI evaluation instruments (EI). These EIs are not only increasing in number, but also in complexity and diversity, making it hard to understand this evaluation landscape in a meaningful way. In this paper we present an approach for categorising EIs using a set of 18 facets, accompanied by a rubric to allow anyone to apply the framework to any existing or new EI. We apply the rubric to 23 EIs in different domains through a team of raters, and analyse how consistent the rubric is and how well it works to distinguish between EIs and map the evaluation landscape in AI.