深度学习的理论只有应用于课堂实践,才能真正促进教育教学的变革.为了探究课堂深度学习的现状以及促进课堂深度学习的策略,文章基于布鲁姆认知目标分类理论,对174节中小学课堂师生对话采用统计分析方法、滞后序列分析法、社会网络分析方法、数据挖掘和知识发现方法,研究发现并得出如下结论:第一,当前课堂教学呈现浅层教学的样态,课堂教学存在理解层级的固化等问题;第二,增加批判性问题,提升反思指导,是促进深度学习有效发生的教学策略;第三,弥补评判和执行等关键教学行为,将其作为知识建构过程中的中介和桥梁,构建层进式学习;第四,转变教育信念,增强与创造认知层级中生成、计划和产生有关的创生性教学活动,开展生成性教学.

随着教师培训的备受关注,网络研修成为教师学习的重要途经,而如何借助活动理论的相关思想开展有效的网络活动设计,以促进研修教师的实践反思成为研究的重点.文章以活动理论为基础提出了讲故事网络活动设计过程模型,并以教师在线实践社区中的一次讲故事网络活动为例进行实证分析,运用CoI认知临场分析和反思层级编码,针对网络活动中的发帖数据进行了综合分析,实践表明:讲故事网络活动有利于提高教师参与积极性,促进教师实践反思以及隐形知识分享.研究结果为促进教师网络研修实践等研究提供了新的思路和具体经验.

教师的教学行为对教学效果会产生直接影响,教学行为改进是一个动态的过程并且具有群体性特征.本研究基于经验学习圈理论,对71名教师在2年持续教学行为改进过程中的350节视频课例,采用内容分析法,探究新手教师、胜任教师和成熟教师三类群体的教学行为改进特征.研究发现:基于经验学习圈2年的持续教学行为改进过程取得了显著的改进效果,三类教师均能够较好地改进问题的开放性;讨论后汇报和鼓励学生提出问题是三类教师在教学行为改进中共同的难点;经验学习中的反思性观察和抽象概括两个阶段对教师教学行为的正向改进作用比较小;三类教师群体的教学行为改进着力点明显不同,成熟教师最难改进的教学行为和最难改进的研修阶段与新手教师和胜任教师不同,新手教师与胜任教师存在不同的教学行为改进难点和相同的最难改进阶段.根据上述不同教师群体的教学行为改进特征,可以进一步优化与完善教师教学行为的改进方法与策略,形成更加精准地教学行为改进支持服务与改进依据,丰富教学行为改进的有关研究.

文章对来自福建省厦门市思明区95位教师的课堂教学行为大数据、实践性知识大数据和在线学习行为大数据,运用路径分析法、聚类分析方法、COI认知临场分析法、文本可视化方法、主成分分析法、多维尺度分析法和相关分析法等基于大数据的知识发现方法与技术进行了深入分析,发现了七条典型的教师成长行为路径,以及实践性知识是教师成长行为路径中的重要中介变量,其中,教师的教育信念、策略知识、情境知识和反思知识是最主要的中介变量.发现的七条典型的教师成长行为路径为网络研修设计者和一线教师均带来了一些重要启示.

数据结构是计算机类专业的理论基础课,也是在各类计算机相关的研究中使用的理论基础.但是数据结构课程的概念多、专业术语多、算法多,导致学生在学习过程中存在着理解概念不到位、理论联系实际不够及学习兴趣越来越低,动手能力差的问题.在教学过程中,应让学生充分采用计算机思维理解各种数据结构的存储方式及算法.本文对计算机思维在数据结构教学中的应用进行了研究和探索.

Web has been a massive information repository on w hich information is scattered in different data sources .It is common that different data sources provide conflicting information for the same entity . It is called the truth finding problem that how to find the truths from conflicting information .According to the number of attribute values ,object attributes can be divided into two categories :single‐valued attributes and multiple‐valued attributes . Most of existing truth finding work is designed for truth finding on single‐valued attributes .In this paper ,a method called MTruths is proposed to resolve truth finding problem for multiple‐valued attributes .We model the problem using an optimization problem .The objective is to maximize the total weight similarity between the truths and observations provided by data sources .In truth finding process ,two methods are proposed to find the optimal solution :an enumeration algorithm and a greedy algorithm .Experiments on two real data sets show that the correctness of our approache and the efficiency of the greedy algorithm outperform the existing state‐of‐the‐art techniques .

The development of positioning technologies and pervasiveness of mobile devices make an upsurge of interest in location based services (LBS). The k nearest neighbor(kNN) query in road networks is an important query type in LBS and has many real life applications, such as map service. However, such query requires the client to disclose sensitive location information to the LBS. The only existing method for privacy-preserving kNN query adopts the cloaking-region paradigm, which blurs the location into a spatial region. However, the LBS can still deduce some information (albeit not exact) about the location. In this paper, we aim at strong privacy wherein the LBS learns nothing about the query location. To this end, we employ private information retrivial (PIR) technique, which accesses data pages anonymously from a database. Based on PIR, we propose a secure query processing framework together with flexible query plan for arbitrary kNN query. To the best of our knowledge, this is the first research that preserves strong location privacy for network kNN query. Extensive experiments under real world and synthetic datasets demonstrate the practicality of our approach.

The popularization of the network and the development of e‐commerce have changed the way people access information and consume .For most of people ,Web has been the important source of information .Meanwhile ,information quality issue is becoming increasingly prominent .There is a lot of information w hich is outdated ,incorrect ,false and bias .Particularly ,the problem of conflicting information provided by different w ebsites is obvious .It has to be solved that how to find the truth from conflicting information .As we know ,there is not a method which considers the credibility of data categories on data sources during discovering truth .So ,we propose a problem which is truth discovery based credibility of data categories on data sources .In this paper ,tw o methods are proposed to detect the credibility differences of data categories on sources ,and a Bayesian method is used to iteratively compute the data sources quality and data accuracy . Additional , data coverage and the difficulty of each object is considered to improve the accuracy of truth finding .The experiments on a real data set show that our algorithms can significantly improve the accuracy of truth discovery .

本研究针对中小学教师普遍存在教学反思方式单一、反思水平不高等问题,聚焦教师在线实践杜区的多元反思方法一体化反思活动模式的研究,以使教师通过将多种反思方法有机整合,提高反恩水平.基于该活动模式,本研究设计了同侪反思活动.通过对同侪反思活动设计和实证研究得出:在线实践社区多元反思方法一体化反思活动模式能够使个人反思和集体反思活动相互促进,并能够有效提升各教师群体的反思能力.

The Web is the largest repository of information. Personal information is usually scattered on various pages of different websites. Search engines have made it easier to find personal information. An attacker may collect a user's scattered information together via search engines, and infer some privacy information. We call this kind of privacy attack Privacy Inference Attack via Search Engines . In this paper, we propose a user-side automatic detection service for detecting the privacy leakage before publishing personal information. In the user-side service, we construct a User Information Correlation (UICA) graph to model the association between user information returned by search engines. We map the privacy inference attack into a decision problem of searching a privacy inferring path with the maximal probability in the UICA graph. We propose a Privacy Leakage Detection Probability (PLD-Probability) algorithm to find the privacy inferring path. Extensive experiments indicate that the algorithm is reasonable and effective.

Mitra Akasereth Mitra Akaseth Abdulmohsen Algarni Xiangdong An Cristina Baroglio Georgeta Bordea Réda Bouadjenek Tianyu Cao Sylvie Cazalens samaneh chagheri Wing Kwan Chan Jun Ming Chen Smitashree Choudhury Ying Cui Wenkui Ding Fabien Duchateau Elöd Egyed-Zsigmond Yulei Fan Andre Freitas Zhenmei Gu Rim Helaoui Lei Hou Sharon Hsiao Fu-MIng Huang Xin Huang Yi-Ting Huang Kai Hui Zheng Huo Hocheol Jeon Yexi Jiang Hamamache Kheddouci Selma Khouri Taehwan Kim Sheila Kinsella James Lanagan Christine Largeron Danielle Lee Els Lefever Jingxuan Li Lei Li Peipei Li Zhao Li Huizhi (Elly) Liang Ming Shun Lin Ruxia Ma Youzhong Ma Lucrezia Macchia Elisa Marengo Giatsoglou Maria Sergio Mascetti Ronaldo Mello Pascal Molli Razieh Naizi Nada Naji Jung Sun Oh Masayuki Okabe Hsing-Kuo Pao Symeon Papadopoulos Juyoung Park Juyuong Park Denis Parra Viviana Patti Patricia Della Méa Plentz Miao Qiao Chantal Reynaud Daniele Riboni Carlos Rojas Benjamin Roth Carmen Ruiz Vicente Shaghayegh (Sherry) Sahebi Khalid Saleem Karen Sauvagnat-Pinel Steven Schockaert Marijn Schraagen Chao Shen Yan Shen Dongwook Shin Anna Stavrianou Shaoxu Song Frank Takes Jürgen Umbrich Zhichun Wang dayong wang Hao Xia Fei Xie Chenyan Xiong Chi Shan Yeh Chunxia Zhang Xiao Zhang Ying Zhang Leyla Zhuhadar Antoine Zimmermann Caecilia Zirn Olena Zub

本文针对基于网络资源学习存在的问题,从P2P(PeopletoPeople)社会化交互学习设计的角度提出解决问题的对策和方法。本文分析了基于网络资源的学习环境的构成要素及其相互关系,描述了网络资源类型与其所能支撑的学习形式的联结关系,并提出了基于网络资源的P2P学习设计策略模型,为促进和提升基于网络资源的学习提供了新的设计思路和方法。

为了更好地反思网络课程的教与学,改进网络教学的教学方法,本文作者从自我反思的角度,以自己担任主讲教师的网络课程数据为个案研究对象,采用社会网络分析法,通过提出假设、数据收集、数据分析和假设检验等研究步骤,得到以下研究结论:(1)任课教师是虚拟学习社区社会网络中的意见领袖,且为最核心的意见领袖;(2)在虚拟学习社区社会网络中,由教师和部分学生形成了社会网络中的意见领袖群体,这些学习者实际上起到了助学者的作用;(3)意见领袖群体是虚拟学习社区中的一个具有直接的、联系紧密的和互惠的基础团体;(4)意见领袖群体是构成虚拟学习社区社会网络信息通路的重要人物,他们对网络属性特征值有很大的影响。

职业教育与普通教育相比具有教育目标以就业为导向,培养应用型、实践型和技能型人才,教育内容反映最新最前沿的生产技能知识,教育对象面向社会更广泛人群等特点。远程职业教育以其跨越时空限制、投入产出比高和开放性等优势满足了职业教育范围逐渐扩大化的趋势。认知学徒制强调在真实情境下的专家实践引领的教学方式克服了职业教育理论与实践脱节现象,满足了职业教育的实践性和职业性特点。本文重点介绍了远程职业教育中认知学徒制教学模式的构成要素、实施策略和基于网络的认知学徒制学习支持环境等方面的研究。

教育游戏的基本宗旨是为学生提供“寓教于乐”的学习环境,通过游戏激发学生知识探究的热情。本文提出游戏化学习社区是基于网络技术、虚拟现实技术和游戏技术等开发的一所虚拟学校,其主要特点是:学习社区环境虚拟化;学生的学习任务是通过具有一定情境的游戏任务来体现;为教师提供游戏编辑工具,使教师能够将教学策略映射成为学生的游戏策略,实现真正意义上的游戏与教育的融合。

The basic purpose of edutainment is to make learning interesting using games to inspire students' inquiry. The 'game-based learning community' in this paper is a virtual school associated with games in education through hosting network, virtual reality and computer games. Its main characteristics are virtual learning community, the learning tasks are embedded within gaming tasks in scenarios. The games editing tools are provided for teachers to add, remove or adjust various factors within the games. So their teaching strategies are mapped onto students' gaming strategies. In these ways, gaming is incorporated in the real sense with education.