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Amber Johnson 《Communication Teacher》2019,33(3):228-233
This self-reflexive activity acts as an introduction to how we talk about and express gender identity, as well as the assumptions we may have about gender identity norms and expression. The activity illuminates student’s subconscious behaviors and understandings of gender, pushing them to sit self-reflexively with their own understandings of gender as an identity, expression, binary, and potential locus of shame/freedom.Courses: Introduction to Women and Gender Studies, Intercultural Communication, Media Studies, Gender and Communication, Performance StudiesObjectives: Designed to introduce students to their own understandings and embodiment of gender, this activity asks students to be honest about their preconceived notions regarding gender that they bring with them into the classroom. The activity utilizes predesigned components that test students’ subconscious knowledge of the gender binary. This is a one-time activity that can be conducted in one 50- or 75-minute class period. 相似文献
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Anthony J. Sadar 《Science & Education》2018,27(1-2):233-235
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In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary. 相似文献
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Measuring Search Engine Quality 总被引:12,自引:3,他引:9
The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures. 相似文献