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This study aims to identify the literacy skills of individuals whose highest level of education was in the field 'teacher training and educational sciences'. The study sample comprised 10,618 individuals in the field of teacher training and educational sciences, selected from 31 countries (participating in the International Adult Skills Assessment Programme during the 2014-2015 survey) using a multi-stage sampling method. The study employed multi-level latent class analysis and three-step analysis in order to determine both the number of multi-level latent classes of educators' literacy scores as well as the selected independent variables' success in predicting those latent classes. The analysis revealed that educators in Germany constituted the group with the highest literacy skills while educators from Singapore comprised the group with the lowest literacy skills. [This study was presented at the 9th International Congress of Educational Research. Ordu University, Ordu, Turkey.]

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

Wood dust is an established carcinogen also linked to several non malignant respiratory disorders. A major limitation in research on wood dust and its health effects is the lack of (historical) quantitative estimates of occupational exposure for use in general population-based case-control or cohort studies. The present study aimed to develop a multinational quantitative Job Exposure Matrix (JEM) for wood dust exposure using exposure data from several Northern and Central European countries. For this, an occupational exposure database containing 12653 personal wood dust measurements collected between 1978 and 2007 in Denmark, Finland, France, The Netherlands, Norway, and the United Kingdom (UK) was established. Measurement data were adjusted for differences in inhalable dust sampling efficiency resulting from the use of different dust samplers and analysed using linear mixed effect regression with job codes (ISCO-88) and country treated as random effects. Fixed effects were the year of measurement, the expert assessment of exposure intensity (no, low, and high exposure) for every ISCO-88 job code from an existing wood dust JEM and sampling duration. The results of the models suggest that wood dust exposure has declined annually by approximately 8%. Substantial differences in exposure levels between countries were observed with the highest levels in the United Kingdom and the lowest in Denmark and Norway, albeit with similar job rankings across countries. The jobs with the highest predicted exposure are floor layers and tile setters, wood-products machine operators, and building construction labourers with geometric mean levels for the year 1997 between 1.7 and 1.9 mg/m3. The predicted exposure estimates by the model are compared with the results of wood dust measurement data reported in the literature. The model predicted estimates for full-shift exposures were used to develop a time-dependent quantitative JEM for exposure to wood dust that can be used to estimate exposure for participants of general population studies in Northern European countries on the health effects from occupational exposure to wood dust. [ABSTRACT FROM AUTHOR]