The objective of the study was to study the perceptions of student teachers and educators of an educational cluster as a tool for improving the professional competence of future educators, and to identify the cluster-related tools to be used to improve the professional competence of future educators. The project evaluation checklist for experts, a questionnaire for participants in the experiment, a semi-structured interview questionnaire to conduct with focus group respondents were used for collecting data. Data were processed using content analysis technique and SPSS Statistics v24 package. The study is quantitative, based on subjective evaluations of study participants and representatives of educational clusters, and does not aim at evaluating students’ achievements. Two simulation models of the educational cluster (“Cluster Educational Initiative” and “Cluster of Educational Opportunities for Educators 2.0”) and four educational activities for educators (discussion "Agglomeration of Subjects, Educational Institutions: Realities and Prospects”; online conference “Ukraine-Brussels - Sharing Experience in Education”; an educational tour to Finland for teachers, parents and managers in the field of education; the exchange of four teachers from two schools) was a key result of this study. It has been found that the creation of clusters for improving the professional competence of future educators and their professional advancement is now part of national education policy in Ukraine and in the world. The tools of this form of organization of educational activity are competence-oriented and aimed at deepening professional experience, development of competences of realization of life-long education, creation of own public image.

The academic buoyancy scale (ABS) is one of the most widely used instruments for measuring academic buoyancy. To obtain meaningful and valid comparisons across groups using ABS, however, measurement invariance should be ascertained a priori. To that end, we examined its measurement invariance, validity evidence based on relations to other variables, and score reliability using categorical omega across culture and gender among Egyptian and Omani undergraduates. Participants were 345 college students: Egyptian sample (N=191) and Omani sample (N=154). To assess measurement invariance across culture and gender, multiple–group confirmatory factor analysis was performed with four successive invariance models: (a) configural, (b) metric, (c) scalar, and (d) residual. Results revealed that the unidimensional baseline model had adequate fit to the data in the full sample. Moreover, measurement invariance was found to hold across culture but not across gender and consequently the ABS could be used to yield valid cross-cultural comparisons between the Egyptian and Omani students. Conversely, it cannot be used to yield valid inferences related to comparing gender groups within each culture. Validity evidence based on relations to other variables was supported by the significantly moderate correlation between ABS and academic achievement (GPA; r =.435 and r = .457, P < .01) for the Egyptian and Omani samples, respectively. With regard to score reliability, categorical omega coefficients were moderate across both samples. Educational and psychological implications, limitations and suggestions for improving the scale are discussed.

Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education management, to study the relationship of continuing education at the upper secondary level, and to construct an appropriate education program prediction model for high school students in a rural school. The data for research is the academic achievement data of 1,859 students from Manchasuksa School at Mancha Khiri District, Khon Kaen Province, Thailand, during the academic year 2015-2020. Research tools are separated into 2 sections. The first section is a basic statistical analysis step, it composes of frequency analysis, percentage analysis, mean analysis, and standard deviation analysis. Another section is the data mining analysis phase, which consists of discretization technique, XGBoost classification technique (Decision Tree, Gradient Boosted Trees, and Random Forest), confusion matrix performance analysis, and cross-validation performance analysis. At the end, the research results found that the reasonable distribution level of student achievement consisted of four clusters classified by academic achievement. All four clusters were modeled on predicting academic achievement for the next generation of students. In addition, there are four success models in this research. For future research, the researcher aims to develop an application to facilitate instruction for learners by integrating prediction models into the mobile application to promote the utilization of modern technology.