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Research in Higher Education - Given the significant need to increase and diversify graduate enrollments within computing fields, it is vital to understand what shapes students’ pathways to... 相似文献
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David Sánchez-Oliva Rebecca M. Leech Alberto Grao-Cruces Irene Esteban-Cornejo Carmen Padilla-Moledo Oscar L. Veiga 《Journal of sports sciences》2020,38(9):1062-1069
ABSTRACTThe aims of this study were to identify profiles of sedentary behaviour (SB) patterns, based on leisure-time self-reported SB modalities (screen, educative, social, and relaxing) and to evaluate changes in these profiles over 2 years among Spanish youth aged 8–18 years. Latent profile analysis (LPA), a data-driven analytic approach, was used to identify groups of boys and girls (n = 1553; 48% girls; mean±SD age: 12.56 ± 2.49 y) with distinct SB profiles using the SB modalities (time/d) as input variables. Latent transition analysis, an extension of LPA that uses longitudinal data, was used to analyse 2-year changes in these profiles. At baseline, four and three SB profiles were found among boys (labelled: screen, educative, social, and relaxing) and girls (labelled: screen/social, educative, and relaxing), respectively. Overall, more girls (range: 48%-67%) had the same profile over time, than boys (40%-52%). Participants with a screen or relaxing SB profile at baseline were more likely to have an educative profile after 2 years. Youth with a social and an educative SB profile at baseline were more likely to transition to profiles characterized by higher screen and social SB, respectively. Using a novel and person-centered approach, this study identified gender-specific SB profiles that were moderately stable over time. 相似文献
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Cultural Studies of Science Education - The retention of underrepresented students remains a significant challenge in the STEM (Science, Technology, Engineering and Math) disciplines. A broad range... 相似文献
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Harry F. Dorrell Joseph M. Moore Mark F. Smith Thomas I. Gee 《Journal of sports sciences》2019,37(1):67-73
This study investigated the validity and reliability of the GymAware PowerTool (GPT). Thirteen resistance trained participants completed three visits, consisting of three repetitions of free-weight back squat, bench press, deadlift (80% one repetition maximum), and countermovement jump. Bar displacement, peak and mean velocity, peak and mean force, and jump height were calculated using the GPT, a three-dimensional motion capture system (Motion Analysis Corporation; 150 Hz), and a force plate (Kistler; 1500 Hz). Least products regression were used to compare agreeability between devices. A within-trial one-way ANOVA, typical error (TE; %), and smallest worthwhile change (SWC) were used to assess reliability. Regression analysis resulted in R2 values of >0.85 for all variables excluding deadlift mean velocity (R2 = 0.54–0.69). Significant differences were observed between visits 3-2 for bench press bar displacement (0.395 ± 0.055 m; 0.383 ± 0.053 m), and deadlift bar displacement (0.557 ± 0.034 m; 0.568 ± 0.034 m). No other significant differences were found. Low to moderate TE (0.6–8.8%) were found for all variables, with SWC ranging 1.7–7.4%. The data provides evidence that the GPT can be used to measure kinetic and kinematic outputs, however, care should be taken when monitoring deadlift performance. 相似文献