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This study explores the relation between risk of job automation and participation in adult education and training (AET) and examines variation in that relation across welfare regimes distinguishing between situational and institutional barriers. Using microdata of PIAAC, we analyze participation in formal or nonformal AET for job-related reasons in relation to the risk of automation of the respondents' occupation after controlling for main sociodemographic characteristics. Logistic regression models are run on respondents from 14 European countries representing different welfare regimes: Denmark, Norway, and Sweden (Scandinavian countries); Italy, Greece, and Spain (Southern European); Czech Republic, Slovakia, and Poland (Central and Eastern Europe), Belgium, France, and Germany (Continental); and United Kingdom and Ireland (Anglo-Saxon countries). Our findings confirm that workers in occupations at high risk of automation were found to be consistently less likely to participate in job-related AET, quite irrespective of welfare regime. [The paper was presented at XIII Conferenza Espanet Italia--Il welfare state di fronte alle sfide globali (Venezia, 17 September 2020).]

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.