School engagement reflects the degree to which students are invested, motivated and willing to participate in learning at their school and this relates to future academic and professional success. Although school engagement is a primary factor predicting educational dropout or successful school completion in Europe and North America, little is known about school engagement factors in non-English speaking countries. We adapted a 15-item school engagement scale and assessed validity and reliability of the Russian translation on a sample of Russian school-aged children (N = 537, 6–12 years, 46% females) who attended at public schools in Moscow. Results of the final factorial structure that included emotional, cognitive and behavioral components were selected based on its excellent fit indices and principles of parsimony. Component results show that the emotional component has the highest internal consistency and the behavioral component has the lowest. Although, all components are significantly interrelated, we observed no gender differences and no significant correlation with age. Theoretically, our data agree with the notion that children’s emotional engagement in schools sets the foundation for learning, participating and succeeding in school activities. Practically, the proposed scale in Russian can be used in educational and clinical settings with Russian speaking children.
A small percentage of children shows outstanding cognitive abilities and perform at m uch higher levels th an their same age peers. Psychological science has absorbed knowledge from different spheres such as psychometrics, mathematics, statistics, and psychology to develop m ethods for identifying cognitively gifted children. The study of intelligence has a long history and has been influenced by social environm ent, wars, education systems and revolutions. In this paper we focus on tw o main techniques of identifying cognitively gifted children (a) intelligence testing and (b) domain specific exams called Olympiads (e.g., m ath and physics). We provide a short his torical perspective of th e evolution of intelligence testing in Europe and th e U SA and domain specific Olympiads in Russia. We discuss advantages and lim itations of b oth techniques. Moreover, we highlight th a t cognitive neuroscientists have been trying to understand th e brain mechanisms th a t may drive cognitive abilities in highly performing children using neuroimaging techniques such as functional magnetic resonance imaging (fM R I). We summarize th e know l edge we gained to date from fM RI studies and show th a t th e m ajority of studies examine m ath ematically gifted male adolescents w ith m ental rotation tasks. Despite critical advances there is still a lot to be done in understanding th e semantic brain-behavior relations in cognitively gifted children.
A constant blood supply to the brain is required for mental function. Research with Doppler ultrasonography has important clinical value and burgeoning potential with machine learning applications in studies predicting gestational age and vascular aging. Critically, studies on ultrasound metrics in school-age children are sparse and no machine learning study to date has used color duplex ultrasonography to predict age and classify age-group. The purpose of our study is two-fold: first to document cerebrovascular hemodynamics considering age, gender, and hemisphere in three arteries; and second to construct machine learning models that can predict and classify the age and age-group of a participant using ultrasonography metrics. We record peak systolic, end-diastolic, and time-averaged maximum velocities bilaterally in internal carotid, vertebral, and middle cerebral arteries from 821 participants. Results confirm that ultrasonography values decrease with age and reveal that gender and hemispheres show more similarities than differences, which depend on age, artery, and metric. Machine learning algorithms predict age and classifier models distinguish cerebrovascular hemodynamics between children and adults. Blood velocities, rather than blood vessel diameters, are more important for classifier models, and common and distinct variables contribute to age classification models for males and females.
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