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101.

Age and sex differences in willingness to communicate (WTC), communication apprehension, and self‐perceived communication competence were examined using three age cohorts of participants drawn from junior high, high school, and university student populations. Results indicate that junior high females are higher in WTC than their male counterparts and females at the university level are higher in communication apprehension and lower in self‐perceived competence than are male university students. Communication apprehension and self‐perceived competence show a consistent negative relationship that does not vary with age or sex in the present sample. The degree to which communication apprehension arid self‐perceived competence predict WTC varies with age and sex. In all three age cohorts, communication apprehension is a significant predictor of WTC among women. Among men, self‐perceived competence emerges as a significant predictor of WTC in all three age groups.  相似文献   
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A co-operative project between the Association of Learned and Professional Society Publishers and Queen's University, Belfast, supported by the Electronic Libraries Programme, was set up to create a website and host the publications of five learned societies on an experimental basis. In the light of the experience gained, societies who have not taken the first steps in providing electronic services for their members and subscribers are offered advice on the structure, setup, and service requirements for their first website.  相似文献   
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Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance tasks requiring argumentation must capture the many ways students can construct and evaluate arguments in science, yet such tasks are both expensive and resource-intensive to score. In this study we explore how machine learning text classification techniques can be applied to develop efficient, valid, and accurate constructed-response measures of students' competency with written scientific argumentation that are aligned with a validated argumentation learning progression. Data come from 933 middle school students in the San Francisco Bay Area and are based on three sets of argumentation items in three different science contexts. The findings demonstrate that we have been able to develop computer scoring models that can achieve substantial to almost perfect agreement between human-assigned and computer-predicted scores. Model performance was slightly weaker for harder items targeting higher levels of the learning progression, largely due to the linguistic complexity of these responses and the sparsity of higher-level responses in the training data set. Comparing the efficacy of different scoring approaches revealed that breaking down students' arguments into multiple components (e.g., the presence of an accurate claim or providing sufficient evidence), developing computer models for each component, and combining scores from these analytic components into a holistic score produced better results than holistic scoring approaches. However, this analytical approach was found to be differentially biased when scoring responses from English learners (EL) students as compared to responses from non-EL students on some items. Differences in the severity between human and computer scores for EL between these approaches are explored, and potential sources of bias in automated scoring are discussed.  相似文献   
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