No abstract

State of emergency affects many areas of our life, including education. Due to school closure during COVID-19 pandemic as a case of a long-term emergency, education has been moved into a remote mode. In order to determine the factors driving the acceptance of distance learning technologies and ensuring sustainable education, a model based on the Unified Theory of Acceptance and Use of Technology has been proposed and empirically validated with data collected from 550 in-service primary school teachers in Lithuania. Structural equation modelling technique with multi-group analysis was utilized to analyse the data. The results show that performance expectancy, social influence, technology anxiety, effort expectancy, work engagement, and trust are factors that significantly affect teachers' behavioural intention to use distance learning technologies. The relationships in the model are moderated by pandemic anxiety and age of teachers. The results of this study provide important implications for education institutions, policy makers and designers: the predictors of intention to use distance learning technologies observed during the emergency period may serve as factors that should be strengthened in teachers' professional development, and the applicability of the findings is expanded beyond the pandemic isolation period.

In response to the digital transformation in education, teachers are expected to develop new competencies. Although teachers gained valuable experience in digital technology use during the COVID-19 pandemic, research and practice show that primary school teachers need to be supported and trained for the new normal of innovative, advanced use and adoption of digital technologies in educational practice. This study aims to identify the key factors that influence teachers’ motivation to transfer technology-enabled educational innovation in primary education. The Learning Transfer System Inventory (LTSI) factors and the adoption factors of technology-enabled educational innovation have been conceptually mapped. The LTSI model has been empirically validated with data collected from 12.7% of Lithuanian primary school teachers. The structural equation modeling technique was utilized to analyze causal relationships of factors influencing teachers’ motivation to transfer technology-enabled educational innovation. The qualitative research method was used to provide a deeper understanding of key factors that influence motivation to transfer. The conducted analysis shows that motivation to transfer is significantly influenced by all five domains of factors: perceived value, personal characteristics, social practices, organizational and technology-enabled innovation factors. Motivation to transfer innovation varies according to teachers’ perceived digital technology integration skills, which underpin the importance of applying different roles and strategies based on the teachers’ skills. This study provides implications for designing effective professional development for in-service teachers and creating a suitable environment in schools for the adoption of innovation in post-COVID-19 education.

Many countries have focused on the improvement of education system performance. Small number of studies consider system of a country as unit of assessment where indicators represent all levels of education system. In the paper, we propose the methodology for the performance analysis of education systems as a whole hybridizing Data Envelopment Analysis and Principal Component Analysis. Its applicability is illustrated by the analysis of the data collected for 29 European countries. In the analysis we used publicly available data from EUROSTAT and OECD which European Commission uses for the performance monitoring of education in European Union. No prior assumptions were made or expert judgements included. We demonstrated good performance of the method on limited data set. The proposed methodology of hybrid Data Envelopment Analysis and Principal Component Analysis allows researchers analyse education systems quantitatively. The recommendations for improvements and assessment of real world education systems should be based on the analysis of a sufficiently large data set comprehensively representing the considered education systems.

This article presents a comparative analysis of the educational systems of EU countries, exploring them from a socioeconomic perspective with a special focus on new EU member states. The research question was whether post-socialist countries, in terms of social and academic segregation, are moving toward a separate educational “regime,” or whether they are currently approaching either the Scandinavian, Continental, Anglo-Saxon, or Mediterranean model. Segregation was analyzed according to performance scores in science and economics, social and cultural status, and hierarchical regression was employed in analyzing PISA 2015 data. Results indicate that post-socialist EU member states, in terms of academic and social segregation, do not form a separate “educational regime.”