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1.
This editorial paper presents 11 papers related to the special issue proposed by UNICEF on the Education Response to COVID-19. The COVID-19 pandemic provoked an education emergency of unprecedented scale. At its onset in February 2020, school closures were announced in the worst-hit countries. At the peak of the crisis, 90 per cent of learners worldwide had had their education disrupted. Some learners, especially those from the most marginalised population groups, were put at risk of permanent dropout, provoking long-term and significant negative effects on children’s life-long wellbeing and the socio-economic development of their communities and countries. This special issue, which received contributions from UNICEF staff and various researchers, focuses on the impact of school closures, the effectiveness of remote learning solutions, equity implications, the mitigation of learning loss and notions around re-opening better. Different research perspectives and evidence is gathered to help strengthen policy considerations and future planning. The conclusion emphasizes building on the innovative solutions generated by the response to the crisis to make education systems more resilient, whilst also reinforcing the focus on equity and inclusion so that pre-existing disparities are not exacerbated in the future. 相似文献
2.
[目的/意义] 提升公民健康水平是经济社会发展到一定程度后的必然要求,健康数据协同治理体系的构建有助于从信息科学角度达成这一要求,治理体系需要以国家战略为宏观指导,全方位服务于公民健康水平提升的总体目标。[方法/过程] 通过对《"健康中国2030"规划纲要》文本精神的领会,在分析了各治理主体对健康信息治理需求的基础上,提出一种面向"健康中国2030"战略的健康数据协同治理体系,并针对关键环节提出保障措施。[结果/结论] 协调各层级、各部门与各利益主体的健康数据治理体系构建与保障,有助于在推动国家健康战略目标的同时实现健康数据的社会价值,拓展健康信息学的研究范畴。 相似文献
3.
在抗击新型冠状病毒疫情的众多新闻报道之中,数据新闻作品及数据可视化产品表现抢眼,效果独特,影响广泛,充分体现出数据新闻在新闻事件统计、分析、研判上的独特优势。大数据工具的介入,更让数据新闻从之前的"可视化"阶段衍变进入"工具化"阶段,成为此次公共卫生报道中值得研究的新形态。 相似文献
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[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据,通过对灰色预测法在情报研究中的应用情况进行梳理,总结其在应用过程中存在的不足,为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献,从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述,总结当前情报研究中灰色预测法的应用所存在的问题,并提出改进建议。[结果/结论] 在方法应用上,已有研究主要采用数列灰预测,且模型集中在单变量灰色预测模型,根据预测对象不同,灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用,未来可根据预测对象特点及研究目标尝试不同的灰色预测方法,扩宽灰色预测法在其他方面的情报研究问题中的应用。 相似文献
6.
AbstractHow does a small community college library, without marketing experience or budget, advocate for its value to a campus community and its administrators? We did so by creating an engaging, bright, and easy-to-read “pocket-graphic.” In this column, we reflect on the process of shaping a multi-use product out of a mountain of data. Through research in design practices, field observations of popular information tools, and an uncomfortable step into braggadocio, we learned to articulate the successes of our library. Our “pocket-graphic” told our story, yes – but it also provoked surprise, questions (“you really have all that?”) and delight in the lesser known successes (“you really DO all that!”). In the process, we connected our students more deeply to helpful services and resources, faculty to supportive instruction, and positively changed the tone of conversation with all our stakeholders. By turning data points into selling points, we found insights and a focus that moved our own development forward, helping ourselves to define improved priorities for advancing our critical role in student success. 相似文献
7.
This study examines the impact of the caste-class association on enrolment in elementary education in Uttar Pradesh (UP), the largest state of India. Using data from the 71st round of NSSO, with the help of a probit model, it is found that class has a stronger impact on the enrolment ratio than caste in UP. There is a strong and direct association between caste and class at the extremes of the caste system. The association between upper-caste and upper-class significantly augments the enrolment ratio, while the association between lower caste and lower class adversely affects it. However, the cross-associations at the higher side of class, i.e., lower caste with the upper class significantly augments enrolment but the association between lower class with upper-caste is found to be adversely affecting the enrolment, though some of the interaction terms are insignificant statistically. This also ensures the dominance of class effect. This pattern is observed in the case of both rural and urban UP across genders. 相似文献
8.
Education Preparation Programs (EPPs) are increasingly pressured to demonstrate alignment between program supports and candidates’ outcomes. Using mixed methods, we studied the Early Field Immersion School (EFIS), an effort to improve candidates’ early field experiences. Participants included 171 candidates enrolled in a graduate certificate program and 11 university-based faculty. EFIS candidates spent increased time in early field experiences, yet EFIS was not associated with candidates’ performance at program exit. Although we found that while candidates and faculty alike valued EFIS supports, participation was negatively associated with perceptions of preparedness. We discuss these discrepancies and offer suggestions for ongoing research. 相似文献
9.
2022年冬奥会已进入北京时间。黑龙江省各高校应在习主席新时代中国特色社会主义思想引领下,深入贯彻党的教育方针,将立德树人根本任务深切落到实处,积极响应"带动3亿人参与冰雪运动"的伟大号召,加速发展高校冰雪运动。本文采用文献资料、逻辑分析、归纳等研究方法,对北京冬奥会背景下黑龙江省高校冰雪运动发展的现状及存在问题进行分析,并探寻一条符合新时代发展需要的黑龙江省高校冰雪运动发展路径,旨在全面振兴黑龙江省高校冰雪运动,为2022年北京冬奥会保驾护航。 相似文献
10.
《Information processing & management》2023,60(2):103235
Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models. 相似文献