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341.
Sanne W. van der Kleij Adrian P. Burgess Jessie Ricketts Laura R. Shapiro 《Child development》2023,94(1):e57-e66
We examined the relation between socioeconomic status (SES), vocabulary, and reading in middle childhood, during the transition from primary (elementary) to secondary (high) school. Children (N = 279, 163 girls) completed assessments of everyday and curriculum-related vocabulary, (non)word reading, and reading comprehension at five timepoints from age 10 to 13. Piecewise linear mixed-effects models showed significant growth in everyday vocabulary and word reading between every time point. Curriculum vocabulary and reading comprehension showed significant growth during the school year, but not during the summer holidays. There were significant effects of SES on all measures except word reading; yet, SES differences did not widen over time. Our findings motivate targeted reading and vocabulary support for secondary school students from lower SES backgrounds. 相似文献
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The present study examined inter-ethnic, rural–urban, and sex differences in self-assessed intelligence (SAI) in a Malaysian general population sample. In total, 633 individuals varying in rural or urban location, ethnicity (Malay, Kadazan, and Bajau), and sex (women versus men) provided their self-assessed overall intelligence and ten multiple intelligences. In general, results of a series of univariate analyses of variance showed that urban participants tended to have higher SAI than their rural counterparts and that men reported higher SAI than women. There was also a significant main effect of ethnicity, with Malays generally having lower estimates than Bajaus and Kadazans, respectively. There were few significant interactions between ethnicity, urban–rural location, and sex. These data present the first concurrent investigation of ethnic, rural–urban, and sex differences in SAI, and are discussed in relation to previous theoretical discussions of SAI. 相似文献
344.
Adrian R. Bell 《Journal of Further & Higher Education》2018,42(8):1118-1142
This paper analyses data from the National Students Survey, determining which groups of students expressed the greatest levels of satisfaction. We find students registered on clinical degrees and those studying humanities to be the most satisfied, with those in general engineering and media studies the least. We also find contentment to be higher among part-time students, and significantly higher among Russell group and post-1992 universities. We further investigate the sub-areas that drive overall student satisfaction, finding teaching and course organisation to be the most important aspects, with resources and assessment and feedback far less relevant. We then develop a multi-attribute measure of satisfaction which we argue produces a more accurate and more stable reflection of overall student satisfaction than that based on a single question. 相似文献
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David A. Sprenger Adrian Schwaninger 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(4):857-877
The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to use. Technology acceptance is typically measured after the technology has been used for at least three months. This study aims to identify whether a minimal amount of exposure to the technology using video demonstrations is sufficient to predict the intention to use it three months later. In two studies—one using TAM and one using GETAMEL—we showed students of different cohorts (94 and 111 participants, respectively) video demonstrations of four digital technologies (classroom response system, classroom chat, e-lectures, mobile virtual reality). We then measured technology acceptance immediately after the demonstration and after three months of technology use. Using partial least squares modelling, we found that perceived usefulness significantly predicted the intention to use three months later. In GETAMEL, perceived usefulness significantly predicted the intention to use for three of the four learning technologies, while subjective norm only predicted the intention to use for mobile virtual reality. We conclude that video demonstrations can provide valuable insight for decision-makers and educators on whether students will use a technology before investing in it.
Practitioner notes
What is already known about this topic- The technology acceptance model helps decision-makers to determine whether students and teachers will adopt a new technology.
- Technology acceptance is typically measured after users have used the technology for three to twelve months.
- Perceived usefulness is a strong predictor of intention to use the technology.
- The predictive power of perceived ease of use for the intention to use varies from insignificant to strong.
- For the four digital learning technologies (classroom chat, classroom response system, e-lectures and mobile virtual reality), we measure technology acceptance after a video demonstration and again after three months of usage.
- Using structural equation modelling, we are able to predict intention to use after three months, with perceived usefulness measured after the video demonstration.
- We replicate these findings with a second study using the general extended technology acceptance model.
- Short video demonstrations can provide information for educators to predict whether students will use a technology.
- Early impressions of perceived usefulness are very important and valuable to predict whether students will use a technology.