Objectives: Feedback is an essential factor that may affect students’ motor skill learning during physical education (PE) classes. This review aimed to (1) systematically examine the evidence for the effectiveness of feedback on students’ skill learning during PE classes and (2) summarize the evidence for the effects of feedback elements (i.e., format and content). Methods: A systematic search was conducted on seven electronic databases to identify studies that explored the effects of feedback on student learning during PE classes. Twenty-three studies were selected, and the study quality was evaluated using the Physiotherapy Evidence Database scale. The levels of evidence were determined with the best evidence synthesis. Results: Strong evidence indicates the effectiveness of feedback intervention on students’ skill learning compared with those who received no feedback. Limited evidence was found for the effect of visual feedback compared with verbal feedback. There were mixed results for the effectiveness of information feedback compared with praise or corrective feedback. Conclusion: The current evidence suggests that feedback is useful for skill learning during PE classes. Emergent questions still need to be addressed, such as those regarding the efficiency of using different formats and contents for feedback delivery to enhance motor skill learning during PE classes.
Schools provide opportunities for children with visual impairments (VI) to accumulate recommended daily moderate-to-vigorous-intensity physical activity (MVPA). This study aimed to determine physical activity (PA) across the school day among special school children with VI in China. The study objectively measured the MVPA levels of children with VI during the recess, lunchtime, physical education (PE) classes, before-school, and after-school periods segments on PE days and non-PE days. Moreover, this research compared the gender, age, and body mass index (BMI) differences in MVPA during each segment. A total of 70 children with VI aged 7–17 years (mean age = 13.7; SD = 3.4) from the special school participated in this study. Accelerometers were utilized to measure the MVPA of children with VI. The participants with VI accumulated significantly more MVPA time on PE days than on non-PE days. Before-school periods and structured PE classes showed higher percentages of MVPA time than recess, lunch break, and after-school periods during the school day. Children with VI aged 7–12 years old were significantly more physically active than those aged 13–17 years old during recess, lunch break, and after-school periods. In conclusion, PA interventions during structured PE classes are recommended. Special attention should be provided to children with VI as they grow up to increase their MVPA.
Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources (split by “above-average or not”), home possessions (split by “disadvantaged or not”), mother's education (split by “below high school or not”), and gender (split by “male or female”) were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by “above-average or not”) and sense of belonging at school (split by “above-average or not” and “disadvantaged or not”) were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.
Professional translation is almost synonymous with specialty translation (as well as interpretation). A technical or scientific discourse is not radically different the from any other speech: function, formulation (textual strategy) and effect are always factors which determine a translator’s decision. Considering the discourse to translate a dynamic existence is essential for the translator to preserve the cognitive interaction between author and his interlocutors of other cultures.
BackgroundTo help clinicians provide timely treatment and delay disease progress, it is crucial to identifydementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into earlyand late MCI stages before they progress to alzheimer's disease (AD). In the process of diagnosing MCI andAD in living patients, brain scans are regularly collected using neuroimaging technologies such as computedtomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brainscans measure the volume and molecular activity within the brain resulting in a very promising avenue todiagnose patients early in a non-invasive manner.MethodsWe have developed an optimal transport based transfer learning model to discriminate betweenearly and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcomethe over-tting problem and improve model stability and prediction accuracy.ResultsWith the transfer learning methods that we have developed, we outperform the current state of theart MCI stage classication frameworks and show that it is crucial to leverage alzheimer's disease and normalcontrol subjects to accurately predict early and late stage cognitive impairment.ConclusionsOur method is the current state of the art based on benchmark comparisons. This method is anecessary technological stepping stone to widespread clinical usage of MRI based early detection of AD.
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