The technology acceptance model (TAM) is a well-known postmodern idea that explains how humans adopt and use new technologies. The model focuses on variables that impact behavioral intention to use new technology from the perspective of the end user. The purpose of this study was to construct a viable questionnaire for assessing preschool teachers' technology acceptability in online instruction in ECEC, based on data collected from 182 Romanian preschool instructors, using the theory of planned behavior framework. Our application of theory of planned behavior in technology adoption in ECEC is extraordinarily good, with 66 percent explained variance of actual usage of technology in class. The research literature supports the findings that the intention to use technology and a good attitude toward technology are the most significant determinants of actual technology usage. Although more research is needed in larger and more complex samples to confirm these findings, there is compelling evidence that the prediction methodology can be used to predict preschool teachers' level of technology acceptance and assist educational decision-makers in designing timely interventions that improve the chances of success. The study's major findings point to crucial variables that might help national educational decision-makers improve technology adoption in ECEC.

According to Sustainable Development Goal 4.2 (SDG 4.2), Equal Access to Quality Pre-primary Education, governments throughout the world are working to ensure that all children have access to high-quality early childhood development, care, and pre-primary education by 2030. In order to organize available evidence into a coherent framework, the current scoping review represents an exploratory synthesis addressing the broad question of what qualitative and inclusive Early Childhood Education and Care strategies are currently being established globally to achieve SDG4 targets. The goal of this scoping review in this respect, was to map the available research and offer an overview of micro-, meso-, and macro-level perspectives on evidence-based interventions and strategies, for the promotion of SDG4 globally. A layered model of early childhood education that is both inclusive and egalitarian education emerged, starting with the micro level: child, family and community, mezo level: nursery, and kindergarten and macro level: national policies and SDG 4.2 Agenda for 2030. The mezzo level connects the micro and macro levels, being the most solicited level of implementing inclusive and qualitative ECEC strategies. Thus, starting with putting a real emphasis on children rights, creating a qualitative and inclusive culture with a holistic understanding of child development, then investing in teacher preparation and instilling a strong belief and positive attitudes toward equity in early childhood services, developing inclusive educational policies with an authentic community support offered by all stakeholders, then adapting curriculum and assessment methods to all early childhood educational contexts and lastly piloting and up-scaling good practices, and investing in infrastructure, facilities and innovative educational services, SDG4.2 targets could transparently and efficiently be attained by 2030, with all the setbacks arisen from the pandemic context. The data provide light on a vast topic range, including human rights and values, policy actions, and ideologies. The micro-level themes emphasized the importance of fostering equitable and inclusive environments for children., as well as instructional approaches that encourage positive attitudes toward diversity and instructors' levels of experience in dealing with diversity. We also discovered the significance of creating chances that promote socialization, connection development, and a sense of belonging. Meso-level principles emphasized the relevance of schooling in a child's holistic development and skill acquisition. Mainstream availability for all children, national curriculum regulations, teacher preparation for inclusive early childhood education, excellent funding and governance, evaluation and monitoring, and research on inclusive early childhood education comprise the macro level. As a concept and an approach, inclusive and qualitative education necessitates the preparedness of all relevant educational components to participate. Providing inclusive education in the early years requires setting the foundation for subsequent levels of schooling. The active engagement of a young kid should be directed by developmentally and individually suitable curricula. Access to and participation in age-appropriate general curricula becomes critical in identifying and providing specialized support services. Inclusive programming does not imply that the educational programs will necessarily be of good quality. Efficiency and wellbeing are synonymous with equity. Equitable education investment benefits everyone in society, not just the most marginalized. Investing in education will help communities achieve all of the Sustainable Development Goals related to education.

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors’ technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research.

Weather the silence of bystanders is consent or not in cyberbullying type incidents has risen a scientifically debate in the field of victim-aggressor-witness dynamics research. Silence implies consent represents a concept of social interaction, which generally states that people tend to assume lack of a response or opinion to an action as a tacit approval of that action. Our research team has developed the project Keeping youth safe from Cyberbullying, aiming to deeper understand the dynamics of different cyberbullying aspects in online environments among youth. Our focus was in analyzing the relationship between consent silence, seen as a core concept in bystanders' mindset and other psychological or behavioral traits like awareness, anonymity, fear, shame, school popularity and self-worth that fuel the tank of silence, in 140 Romanian high school students. Results show that consent silence represents the most significant predictor of future bystander, aggressor or victim type of response behavior when youth are directly or indirectly involved in cyberbullying incidents. Conclusions and implications are discussed.