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On the 27th and the 28th of October (2022), the 11th Edition of the International Colloquium of Social Sciences and Communication (ACUM 2022) and the Romanian Sociologists’ Society National Conference took place in Brașov. The event was jointly organized by the Faculty of Sociology and Communication at the Transylvania University of Braşov together with the Romanian Sociologists’ Society. Thus, two biennial scientific events were merged: the 11th edition of the international conference in social sciences and the national congress of the Romanian Sociologists Society. Over 110 papers were presented during the conference.

Research background: Research in this paper is based on analysis of data and documents published by official institutions and relevant studies and papers. Purpose of the article: This paper assesses how the pandemic situation influenced automotive sector in countries of Europe. It evaluates differences between European countries, in terms of automotive industry operation after COVID-19 crisis. It also outlines global trends and challenges for future industry development. Methods: In the research, we use mainly general methods, especially analysis, synthesis and comparison. Among the specific methods, we use statistical methods to obtain, process, explain and interpret data. Findings & Value added: In the findings we present the impact of COVID-19 on automotive industry in European countries. In the end, we present thoughts on globalization challenges for automotive industry, and possible ways for its future development.

Research background: The basis of the long-term sustainable movement of society forward is the paradigm of economic development as a set of scientific achievements, which for a certain period of time are recognized as the basis for the development of scientific and practical activities of the society. The paradigm of market relations is increasingly unable to solve vital problems for society and the economy, such as growing social stratification of the population, an increase in anthropogenic pollution of the biosphere, disruption of the normal functioning of ecosystems and a number of other equally important problems on a global scale. Purpose of the article: The goal set by the authors is to conduct a study of the sustainability of the modern paradigm of economic development in the face of global challenges. Methods: The authors carried out a systematic analysis of economic development in the face of global challenges. A new look at the relationship “global challenges - global actions” is proposed. Findings & Value added: The article presents the author’s view on the manifestations of the crisis of the modern development paradigm, based on the correlation of global challenges and global actions.

Research background: In this era of globalization, data growth in research and educational communities have shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. However, the analysis accuracy is low when the quality of educational data is incomplete. Moreover, the current approaches on dropout prediction cannot utilize available sources. Purpose of the article: This article aims to develop a prediction model for students’ dropout prediction using machine learning techniques. Methods: The study used machine learning methods to identify early dropouts of students during their study. The performance of different machine learning methods was evaluated using accuracy, precision, support, and f-score methods. The algorithm that best suits the datasets for these performance measurements was used to create the best prediction model. Findings & value added: This study contributes to tackling the current global challenges of student dropouts from their study. The developed prediction model allows higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education. It can also help the institutions to plan resources in advance for the coming academic semester and allocate it appropriately. Generally, the learning analytics prediction model would allow higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education.