Factors influencing students' learning satisfaction may differ between face-to-face and non-face-to-face flipped learning. For non-face-to-face flipped learning, which was widely employed during the COVID-19 pandemic, it is necessary to examine the impacts on learning satisfaction, which may vary depending on professor-student interaction rather than individual competencies, such as SDL readiness. This descriptive study, conducted 2 March 2019 to 24 June 2020, included 89 s-year, flipped-learning nursing students (28 face-to-face, 61 non-face-to-face). Students completed questionnaires about learning satisfaction, SDL readiness, and professor-student interaction. The data, collected using e-surveys, were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation, and multiple stepwise regression with IBM's SPSS Statistics 25.0 program. The total average score of learning satisfaction (38.19 ± 6.04) was positively correlated with SDL readiness (r = 0.56, p < 0.001) and professor-student interaction (r = 0.36, p = 0.001), although total learning satisfaction was significantly different between the face-to-face and the non-face-to-face groups (t = 5.28, p = 0.024). They were also significant influencing factors, along with face-to-face flipped learning, for total learning satisfaction (F = 18.00, p < 0.001, explanatory power = 36.7%), suggesting flipped learners in non-face-to-face contexts must increase engagement beyond professor-student interaction.
Pubmed ID: 34444396 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 1st, 2022. Software application for genetic analysis of classical biometric traits like blood pressure or height that are caused by a combination of polygenic inheritance and complex environmental forces. (entry from Genetic Analysis Software)
View all literature mentionsData analytics software to compute statistical power analyses for many commonly used statistical tests in social and behavioral research. It can also be used to compute effect sizes and to graphically display the results of power analyses.
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