Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education

Higher education institutions are trying to provide more flexibility and individualization, which is mainly realized through the use of new technologies and implemented in online or blended learning designs. This systematic review aims to investigate the impact of replacing classroom time with an online learning environment. The meta-analysis (k = 21 effect sizes) applied strict inclusion criteria concerning research design, measurement of learning outcomes and implementation of blended learning. The estimated effect size (Hedge's g) was positive, although not significantly different from zero and the confidence interval [-0.13, 0.25], suggesting that overall differences between blended and conventional classroom learning are small, and, at best, very small negative or moderate positive effects are plausible. This means that despite a reduction in classroom time between 30 and 79 per cent, equivalent learning outcomes were found. Consequently, blended learning with reduced classroom time is not systematically more or less effective than conventional classroom learning.     

The RIX Wiki: the use of multimedia advocacy to support person-centred planning in a special needs secondary school – implications for current policy and practice

The article documents findings from a pilot study undertaken in 2012–2013 in a special needs secondary school in the England, that trialled the use of a multimedia advocacy tool, “The RIX Wiki”. The trial was part of the pathfinder programme, which aimed to reform existing Special Education Needs provision, from the system of statementing to one of developing education health care plans. The reforms became enshrined in the Children and Families Act (2014). The findings were overall positive and helped with person-centred planning processes. The discussion is framed within current and future policy directives. We argue that multimedia advocacy approaches and software tools, like the “RIX Wiki” will have a continued role to play in ensuring the needs, aspirations and choices of children and young people with learning disabilities remain central.     

数字内容产品版权保护的演化博弈分析

[目的/意义]基于版权保护的演化博弈分析，有助于探究数字内容产品的原创主体对仿冒行为是否具有容忍度，以及不同仿冒水平下实施版权保护的策略选择，对规范数字内容产品市场主体行为具有一定指导意义。[方法/过程]首先借助演化博弈模型，分析原创主体与仿冒主体的利益演化过程，探究版权保护下原创主体对仿冒水平的容忍行为；然后通过细化博弈双方的利润函数模型，探究原创主体容忍度水平的决定因素。[结果/结论]原创主体对较小的仿冒水平存在一定的容忍度，对较高的仿冒水平零容忍。最大仿冒水平容忍度由仿冒交叉收益系数、收益模型常系数、产品研发成本系数和原创主体的产品研发努力水平倍数4个因素共同决定。     

新冠肺炎疫情下专业图书馆科研新媒体服务探究——以中国科学院文献情报中心“中国科讯”为例

[目的/意义] 以中国科学院文献情报中心"中国科讯"为例，分析专业图书馆应急科学传播服务的举措及成效，为应急事件下专业图书馆科学传播服务提供借鉴。[方法/过程] 梳理新冠肺炎疫情期间"中国科讯"微信公众号推送的服务内容，从日均用户增长和阅读量分析用户对不同类型内容的关注程度，提出应急科学传播服务的思考。[结果/结论] 疫情期间，"中国科讯"日均用户增长明显，电子资源保障类内容阅读量最高，其次为新冠病毒科技攻关进展相关内容、科普类直播和信息素养教育直播观看人数达到数万人。应急事件下，专业图书馆应加强特色服务的宣传、加强信息甄选和审核机制，重大公共卫生事件下应急科学传播工作预案缺失也需引起重视。     

高职院校学习预警平台的构建及其实证分析

开展信息化、智慧化预警工作是实现全面精准育人的重要途径,而预警应用平台无疑是预警工作的有力工具和实践载体,其设计方案应用效果的好坏直接决定着学习预警信息化愿景的达成度。文章在分析高职院校学习预警工作现状的基础上,从工作流程设计、平台框架设计和平台建设方法等3个方面提出了解决方案,并对平台的应用效果进行了实证分析,结果表明:平台的应用扩展了预警范围、提升了预警的精准度和时效性、提高了工作效率,且干预更为灵活、个性化,可为当前高职院校学习危机的实践改革提供新思路。     

图书馆借助新媒体传播科学文化的实践研究——以中国科学院文献情报中心“科学人讲坛”为例

[目的/意义] 以"科学人讲坛"在"新媒体+科学文化内容"方面的有序推进为主要案例，论述新媒体技术在图书馆科学文化传播活动中的具体应用，找到专业图书馆科学文化传播工作优势及发展方向。[方法/过程] 梳理"科学人讲坛"讲座内容、推送渠道及观看数据，通过数据分析影响科学文化传播效果的主要因素。[结果/结论] 新媒体及时、灵活、强互动等特性在科学文化传播工作中起到了良好的效果。图书馆的服务意识、丰富馆藏及科学人文精神与新媒体技术优势的结合，使科学文化传播工作中公众的参与兴趣得到了极大提升，拉近了科学与公众间的距离，提高了科学文化传播效果。     

Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws

In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.     

下一代图书馆系统平台的实践与思考

[目的/意义]大数据与人工智能等技术为图书馆智慧服务带来机遇与挑战，图书馆新系统平台建设刻不容缓。旨在探讨下一代图书馆系统平台发展现状及对策，为建设一流高校智慧图书馆提供实践案例与参考依据。[方法/过程]梳理图书馆系统平台发展脉络，在盘点现有的国内外下一代图书馆系统平台的研究与实践的基础上，以南京大学为例，探索图书馆新系统平台的建设方案。[结果/结论]智慧图书馆将通过建设一体化电子资源管理平台并打造NLSP，提升图书馆资源管控和知识服务水平，构建开放共享的智慧图书馆服务体系。     

Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research

The emerging creative practices of digital collections are increasing in prominence thanks to the access to, and appeal of, data visualizations. Interactive data visualizations, such as a map, timeline, chart, graph, or artist’s rendering of data, enable users to pursue their own research interests within a collection and participate in new areas of research. Use of these data visualizations enhances the scope of the collection and enables a modern and diverse thematic collection to emerge. This article considers the question: Does the addition of data visualizations affect how users participate with a research collection? Impacts on collection development, application of user-centered design, and analysis of users’ exploratory strategies are discussed. Examples of data visualizations used in digital humanities projects and digital collections are presented as case studies.     

An analysis of review content and reviewer variables that contribute to review helpfulness

Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.