基于手机的移动学习资源设计探究

近年来,随着移动通信技术与手机软硬件技术的飞速发展,基于手机的移动学习因学习不受时间和空间的限制、便于携带、师生互动方便等优点,逐步成为一种新的学习方式。但与此同时,由于手机软硬件的特性、移动学习环境的不稳定以及网络的限制,使得基于手机的移动学习仍然受限。移动学习资源是移动学习过程中最重要的环节,在极大程度上影响学习者的学习效果,是当前移动学习研究领域乃至教育技术研究领域的热点问题之一。本文对移动学习资源相关概念进行了界定,并在此基础上研究分析了基于手机的移动学习资源设计的原则及设计形式,以期为移动学习的发展起到一定的促进作用。     

基于云计算的远程移动学习系统设计

伴随着以无线网络、3G技术等为代表的移动网络技术的飞速发展,基于手机、PDA等移动终端的移动云计算（MobileCloudComputing）逐渐受到研究者的关注。如何将移动云计算运用到远程移动学习中已成为当前教育研究的热点问题。在分析移动云计算优势的基础上,根据移动云计算的特．羔,设计了一个整合高性能计算（HPC）集群架构和校园Moodle的云计算远程移动学习平台。该平台能够充分发挥云计算的优势,降低远程教育成本,为我国远程教育的发展提供强有力的支撑。     

移动学习互动性的设计分析

本文通过对移动学习、协同学习与联通主义等概念的介绍,结合目前国内研究发展现状,对互动性设计的必要性、可行性和主要方向进行了阐述。3G/4G网络的发展及智能手机的普及为移动学习提供了更加良好的社交平台,联通主义的学习观也证明建立互动管道比管道中的内容更加重要。     

国内外泛在学习研究主题及其历时演进 ──基于WoS和CSSCI来源期刊的知识图谱对比分析

[目的/意义]泛在学习作为一种新型的学习范式,在世界各国呈快速发展态势,已成为教育技术领域的研究热点。[方法/过程]运用CiteSpace的可视化技术,采用文献计量学知识图谱分析的方法,以检索发表在WoS和CSSCI平台的相关主题学术论文作为数据,从共时比较和历时发展两个维度,梳理国内外近15年(2005-2019年)泛在学习领域研究主题的发展脉络。[结果/结论]国际期刊上泛在学习研究以实证主义研究范式为主,从技术开发、项目实验等角度探究泛在学习教学策略、认知行为和教育应用等议题;而国内期刊论文则侧重理论思辨的方法,解构泛在学习的理论内涵,以及泛在学习与数字化学习、移动学习、终身学习和后现代远程教育对学习模式和认知行为的重构与影响。今后,关于个性化、社会性泛在学习的议题会继续向纵深发展,从认知和情感角度探究泛在学习环境中学习者的学习效能、学习评价和学情反馈等内容将是泛在学习领域未来研究关注焦点和发展趋势。     

知识异质、组织学习与企业创新绩效关系研究

选取山东、江苏两省225家创新类企业为样本,对不同内容的异质性知识(技术知识异质,市场知识异质)、不同的组织学习行为(利用式学习,探索式学习)与企业创新绩效之间的关系进行了考察。结果表明:技术知识异质对企业创新绩效有倒U型影响;市场知识异质对企业创新绩效有正向影响;利用式学习与探索式学习对企业创新绩效均有正向影响;技术知识异质对利用式学习有倒U型影响,对探索式学习有正向影响;市场知识异质对利用式学习与探索式学习均有正向影响;利用式学习与探索式学习在技术知识异质与企业创新绩效关系及市场知识异质与企业创新绩效关系中均起到了部分中介作用。     

手机移动学习在高校思政课教学中的应用研究

将手机的移动学习功能有效引入高校思政课堂辅助教学,把传统教学方式的优势和网络教学的优势结合起来,构建课前、课中及课后的移动学习模式,积极探索和推进思政课堂教学方式方法的改革,激发学生对思政课的兴趣、乐趣和情趣,真正发挥思想政治理论课的育人使命。     

移动学习在大学英语自主学习能力培养中的应用

大学英语的教学不应只把重点放在向学生传授英语知识上,而是要教会学生如何学习和应用这门语言,帮助学生学会学习,形成自主学习的能力。基于现代化教学手段的移动学习打破了固有的教学思维,以其丰富的教学资源、弹性的学习方式、个性化的学习模式,完美地融入到传统的课堂教学模式中,并对传统的教学模式进行了恰当的补充。本文试图找出二者的关联,在大学英语教学中引入移动学习法,将移动学习与自主学习有机结合起来,探讨其对大学生英语自主学习能力的培养和促进作用。     

普适技术、嵌入式系统的研发及在教育体系的应用

本文主要介绍了普适技术,并介绍了运用普适技术、嵌入式系统在我国的中、小学教学过程中的应用,从而体现了该技术的产、学、研的结合,进一步实现了我国教育信息技术化的目标。该技术与课程的结合主要是通过自主开发以计算机、网络、移动技术为核心的普适教育服务系统．将多媒体技术、移动设备技术和网上社区技术支持下的中小学全科教学结合。本研究构建了一个新的自适应学习系统,一个个性化的学习管理系统。该系统扩展性强,可以用于其他的教学方面。     

Technological learning and entrepreneurial behaviour: A taxonomy of the chemical industry in Venezuela

This paper presents a taxonomy of entrepreneurial behaviour vis-à-vis technology, derived from empirical research on a specific sector, that of the chemicals industry in Venezuela. The research focuses heavily on the accumulation of minor innovations through which enterprises acquire their technical knowledge and know how, their technological learning. We have tried to give a precise empirical meaning to that notion in order to understand which are the most important variables that affect the technical learning process. Both the process of technological learning and the technical external linkages — in particular with foreign firms — of Venezuelan companies seem to be the fundamental factors in understanding the development of a new competitive industry which adopts a new products-oriented strategy. But not all firms do respond to such a scheme and it is thus necessary to develop a taxonomy of the industrial sector both for scholarly reasons as well as a policy guide.     

An intelligent blockchain-based system for safe vaccine supply and supervision

Immunization is an indispensable mechanism for preventing infectious diseases in modern society, and vaccine safety is closely related to public health and national security. However, issues such as vaccine expiration and vaccine record fraud are still widespread in vaccine supply chains. Therefore, an effective management system for the supervision of vaccine supply chains is urgently required. As the next generation of core technology after the Internet, blockchain is designed to build trust mechanisms that can change current information management methods. Meanwhile, the development of machine learning technologies provides additional ways to analyze the data in information management systems. The main objective of this study is to develop a “vaccine blockchain” system based on blockchain and machine learning technologies. This vaccine blockchain system is designed to support vaccine traceability and smart contract functions, and can be used to address the problems of vaccine expiration and vaccine record fraud. Additionally, the use of machine learning models can provide valuable recommendations to immunization practitioners and recipients, allowing them to choose better immunization methods and vaccines.     

Student privacy in learning analytics: An information ethics perspective

ABSTRACT

Higher education institutions have started using big data analytics tools. By gathering information about students as they navigate information systems, learning analytics employs techniques to understand student behaviors and to improve instructional, curricular, and support resources and learning environments. However, learning analytics presents important moral and policy issues surrounding student privacy. We argue that there are five crucial questions about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students' privacy and associated rights, including (but not limited to) autonomy interests. We address information access concerns, the intrusive nature of information-gathering practices, whether or not learning analytics is justified given the potential distribution of consequences and benefits, and issues related to student autonomy. Finally, we question whether learning analytics advances the aims of higher education or runs counter to those goals.     

Learning assessments in search-as-learning: A survey of prior work and opportunities for future research

People often search for information in order to learn something new. In recent years, the “search-as-learning” movement has argued that search systems should be better designed to support learning. Current search systems (especially Web search engines) are largely designed and optimized to fulfill simple look-up tasks (e.g., navigational or fact-finding search tasks). However, they provide less support for searchers working on complex tasks that involve learning. Search-as-learning studies have investigated a wide range of research questions. For example, studies have aimed to better understand how characteristics of the individual searcher, the type of search task, and interactive features provided by the system can influence learning outcomes. Learning assessment is a key component in search-as-learning studies. Assessment materials are used to both gauge prior knowledge and measure learning during or after one or more search sessions. In this paper, we provide a systematic review of different types of assessments used in search-as-learning studies to date. The paper makes the following three contributions. First, we review different types of assessments used and discuss their potential benefits and drawbacks. Second, we review assessments used outside of search-as-learning, which may provide insights and opportunities for future research. Third, we provide recommendations for future research. Importantly, we argue that future studies should clearly define learning objectives and develop assessment materials that reliably capture the intended type of learning. For example, assessment materials should test a participant’s ability to engage with specific cognitive processes, which may range from simple (e.g., memorization) to more complex (e.g., critical and creative thinking). Additionally, we argue that future studies should consider two dimensions that are understudied in search-as-learning: long-term retention (i.e., being able to use what was learned in the long term) and transfer of learning (i.e., being able to use what was learned in a novel context).     

Compulsive policy-making—The evolution of the German feed-in tariff system for solar photovoltaic power

In recent years, policy approaches that build upon the notion of innovation systems have enjoyed increasing attention in science, technology and innovation policy. But while the usefulness of systemic thinking in policy-making has been demonstrated in a large number of empirical settings, we still lack a detailed understanding of the dynamics at play when policy makers address systemic problems. In this paper, we show how complex interdependencies and the uncertain nature of technological change shape the process of targeted policy interventions in socio-technical systems. Toward this end we analyzed the evolution of the German feed-in tariff (FIT) system for solar photovoltaic power, a highly effective and widely copied policy instrument targeted at fostering the diffusion and development of renewable energy technologies. We find that the policy has been subject to a considerable amount of changes, many of which are the result of policy makers addressing specific system issues and bottlenecks. Interestingly, however, often these issues themselves were driven by unforeseen technological developments induced by previous policy interventions. We argue that the pattern of policy serving as both a solution to and a driver of technological bottlenecks shows strong similarities with what Rosenberg (1969) called ‘compulsive sequences’ in the development of technical systems. By shedding more light on how the characteristics of socio-technical systems affect policy interventions, our framework represents a first step toward more closely integrating the literature on innovation systems with the work on policy learning.     

高新技术企业中的组织学习与技术创新

在当今知识经济的大背景下,以高新技术企业为对象,通过探讨组织学习与技术创新的关系,阐明了组织学习对技术创新的作用,并提出了高新技术企业如何通过组织学习加快实现技术创新的基本思路,以期为我国高新技术企业技术创新能力的提升和国际市场竞争力的增强提供指导。     

我国教育学国际论文研究主题的热点、演化及其趋势分析(2007-2013)

以Web of Science中SCI、SSCI和A&HCI数据库收录的2007-2013年我国教育学领域的4 362篇文献为研究对象,借助Bibexcel、SPSS和Ucinet工具采用共词分析、聚类分析和战略坐标图对这一时期我国教育学走向国际的研究热点、主题演化和研究趋势进行可视化展示。研究发现,2007-2013年我国教育学国际发文研究热点主要集中在交互式学习、智能辅导、移动学习、高等教育、特殊教育、教师教育和电子学习7个方面;从2008年开始,我国教育学国际研究领域有了很大的拓展,研究主题更为丰富;进入2010年后,在深化传统研究主题的同时,新的研究主题也不断出现;交互式学习是核心研究主题,移动学习、特殊教育、教师教育和电子学习是边缘研究主题,智能辅导和高等教育是潜在研究主题。     

Automatic extraction of titles from general documents using machine learning

In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint, respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word are 0.810 and 0.837, respectively, and precision and recall for title extraction from PowerPoint are 0.875 and 0.895, respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to other domains, and more surprisingly we can even train models in one language and apply them to other languages. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles.     

移动环境下基于情境感知的个性化信息推荐

随着移动互联网的发展,越来越多的用户信息获取过程通过移动终端完成.但当前个性化推荐系统对用户情境的感知能力不足,缺乏为用户提供符合当前情境的个性化信息推荐服务.为此,本文提出了基于贝叶斯方法的情境化用户资源类别偏好学习以及融合该类别偏好的协同过滤个性化信息推荐.运用贝叶斯方法学习用户在不同情境下对各资源类别的偏好,然后将该类别偏好与传统协同过滤推荐算法相结合,生成符合用户当前情境的个性化信息推荐.实验表明本文提出的改进算法可以提高推荐的准确率.     

技术学习对企业创新绩效的影响因素分析

 以上海、杭州、深圳、青岛、洛阳等地的118家企业作为研究对象，对技术学习如何通过影响企业技术能力进而影响企业创新绩效进行实证研究。结果表明，技术学习的五方面要素——学习源、学习内容、学习主体、学习层次和学习环境均会直接或通过影响技术能力间接影响企业创新绩效。其中，学习源对创新绩效不具备间接影响作用，学习环境对创新绩效不具备直接影响作用，而对于创新绩效影响最为显著的要素依次是学习内容、学习层次和学习源。     

基于深度学习的智慧图书馆移动视觉搜索服务模式研究

[目的/意义]大数据时代智慧图书馆用户需要精确智能的检索工具，而移动视觉搜索技术能够满足用户以视觉资源数据为中心的检索需求。[方法/过程]文章在分析国内外基于深度学习的视觉资源识别技术的研究基础之上，构建了基于深度学习的智慧图书馆移动视觉搜索服务模式模型，并设计了模型的工作流程，最后对基于深度学习的智慧图书馆移动视觉搜索系统的发展趋势进行了展望。[结果/结论]将深度学习融入智慧图书馆移动视觉搜索系统中，可以整合多源异构视觉数据、贴合用户个性化偏好以及提高移动视觉搜索系统性能。     

广度学习研究进展:基于情报学的视角

[目的/意义]总结了基于在线社交媒体数据的广度学习工作研究进展,从情报学的视角分析了广度学习的应用展望及未来发展趋势。[方法/过程]利用文献统计分析方法,重点分析了广度学习技术在网络嵌入、链路预测、社区检测等在线社交网络分析领域的应用现状。[结果/结论]广度学习可以将多个不同种类的大型异构数据源融合在一起,设计并使用一套统一的分析方法来跨越这些融合的数据源执行协同数据挖掘任务。广度学习在异构社交网络分析中的这些成功应用为其在情报学领域中的研究奠定了理论基础和技术支持,将会有更广泛更深远的研究成果出现。