移动学习平台的构建

移动学习平台的建立对教与学都起着至关重要的作用,有了这样的平台,能够增强学习者学习的兴趣,兴趣是学习之本,有了兴趣,自然也就提高了学习效率,而且它不受时间和空间的限制,可以随时随地、自由自在的进行任何目的、任何方式的学习,学习环境是移动的,教师和学习者也都是移动的。     

基于上下文感知的移动学习系统设计

移动学习足了信息化时代人们随时随地进行学习的需求,是泛在学习的重要组成。然而用户个体的差异性、学习环境的复杂性、终端设备的局限性等因素阻碍了移动学习的个性化发展。在对比了现有移动学习实现模式的基础上,构建了一个基于上下文感知的移动学习系统,利用泛在计算中的上下文感知技术应对复杂的学习环境,提升人与移动设备之间的交互,实现系统的个性化及智能化。     

基于泛在学习的《数字色彩》课程教学设计模式构建

无线通信技术、计算机网络技术、移动计算技术的高速发展，孕育出了一种新型的学习模式--泛在学习，即无所不在的学习。本文首先介绍泛在学习的基本涵义和特点，并分析泛在学习的理论基础、环境支持及具体实现形式，在此基础上，构建一个泛在学习的教学设计模式，并将此模式应用在《数字色彩》课程的泛在学习中，取得了良好的效果，具有一定的理论和和实践意义。     

基于个体自主学习的英语移动学习系统设计

本文通过对英语移动学习系统的特点及优势进行分析，结合英语教学工作，提出移动英语学习系统的构建模式，这对在网络信息环境下有效培养学习者个体自主学习能力及提高个体英语学习绩效具有一定的指导和借鉴作用。     

成人培训移动学习平台设计

在网络信息技术迅速发展的环境下,人们的学习环境发生了重大变化。目前,人们普遍追求“活到老,学到老”的生活精神,灵活、便捷、个性化的学习方式受到成人学习者的欢迎,这也成为了移动学习发展的动力。本文根据对成人教育定义的理解并结合其特点,对移动学习的概念及其主流模式等方面做出了探讨,设计出了成人培训移动学习平台的方案。     

移动学习基础环境发展现状研究

近年来,移动学习这一新型的远程教育模式受到了学术界和业界的普遍关注。移动网络、移动终端以及移动学习平台组成了移动学习的基础环境。基础环境的发展对移动学习业务的推动至关重要,为了了解其现状和趋势,文章对以上三因素的发展现状进行了详细的调查及分析,以期对移动学习系统的建设有所裨益。     

社交软件作为一种服务--移动网络学习实施的新框架

社交软件联通了在任何位置,环境和语境中的学习者进行微学习。借助社交软件的服务功能够使学习模式更接近真实的学习,全面的学习和无缝学习。本文重点阐述了传统社交网络系统的问题,旨在探讨改良当前无线移动社交网络通信中基础架构、内容分发和安全等问题来更好地支持用户的微学习的需求。     

基于智能手机的终身学习模式的探讨

从E-Learning(电子学习)到M-Learning(移动学习)再到U-Learning(泛在学习),技术的进步引领着学习的一次次革命,本文将就智能手机和移动学习的特点及对推动全民终身学习的作用发表见解。     

信息技术背景下移动学习提高学生时空应用能力路径探讨

移动学习引发了又一次学习革命,必将促进学习的泛在化和终身化。移动学习以其自身的特点天然成为外语辅助教学的最佳手段,它具有情境多样化,时空泛在化,操作便捷化,学习个性化和协作化的特性。通过教学实践提出了提高学生外语应用能力的多种路径,并构建了以自主学习、协作学习为特征的外语教学时空域。     

基于微信公众平台移动混合学习SECOND模型研究

结合移动混合学习理论和文献研究法探讨任何学习模型和平台不断迁移的原因,结果显示平台的社交性、开放性和发展性是关键元素。在此基础上分三个层次构建移动混合学习SECOND模型,包括:以移动网络社交性、开放性和经济性为特色的基础层;由7C模型构成的教学实施层;以移动混合学习平台为媒介的发展层。最后以微信公众平台实证移动混合学习SECOND模型。     

Meta-transfer-adjustment learning for few-shot learning

Deep neural network models with strong feature extraction capacity are prone to overfitting and fail to adapt quickly to new tasks with few samples. Gradient-based meta-learning approaches can minimize overfitting and adapt to new tasks fast, but they frequently use shallow neural networks with limited feature extraction capacity. We present a simple and effective approach called Meta-Transfer-Adjustment learning (MTA) in this paper, which enables deep neural networks with powerful feature extraction capabilities to be applied to few-shot scenarios while avoiding overfitting and gaining the capacity for quickly adapting to new tasks via training on numerous tasks. Our presented approach is classified into two major parts, the Feature Adjustment (FA) module, and the Task Adjustment (TA) module. The feature adjustment module (FA) helps the model to make better use of the deep network to improve feature extraction, while the task adjustment module (TA) is utilized for further improve the model’s fast response and generalization capabilities. The proposed model delivers good classification results on the benchmark small sample datasets MiniImageNet and Fewshot-CIFAR100, as proved experimentally.     

Pattern learning in humans and electronic learning nets

The letter reports on a previously unseen similarity between an associative memory model recently proposed for the brain and some electronic systems that have been developed for practical learning machines. This similarity is analysed, and its implications are discussed. Results of a comparison between the learning behaviour of an electronic network and a group of human subjects in a pattern-recognition task are given to complete the comparative study.     

基于机器学习的小样本学习综述

机器学习在数据密集型应用中十分广泛,但缺点是当数据集很小时往往效果欠佳.近年来,人们提出了小样本学习来解决这个问题.小样本学习指只利用少量样本来训练识别这些样本的机器学习模型.由于小样本学习的实用价值,业界提出很多针对的研究方法,但是目前国内缺少该问题的综述.本文中,对目前业界提出的小样本学习模型及算法进行了总结和探索.首先,给出了小样本学习的问题定义,并介绍了其他一些相关的机器学习问题;然后,根据先验知识,通过从3种数据增强方法和4种模型详细介绍了小样本学习方法;最后,对小样本的未来发展进行了展望.     

From driving to learning: The organizational learning process

Organizational learning (OL) has been considered to be an effective developmental initiative in organizations that have a mission to develop, to sustain and to advance. There is a wide range of beliefs of thinking about what organizational learning is, how it occurs, and how it is applied and how it influences organizational development. Different perspectives are used to study OL by researchers from different disciplines. There is no clear and widely accepted depiction of the linkage between OL and its enabling factors. Such a comprehensive concept covering so many aspects is difficult to achieve. The authors introduce a variant of a classic approach to analyze the OL process?the DELO (Driving-Enabling-Learning-Outcome) model, by describing OL as a continuous process. The model not only describes OL as a continuous goal-driven process, but also allows the picturing of the relationships with various influencing factors and facilitating effect of the of an OL framework.     

基于Groove的虚拟学习社区的设计及研究

虚拟学习社区是一个通过信息通讯技术(ICT)在互联网或局域网内创建的学习环境。它为学生创造了广阔的协作学习空间,冲击着传统的教学方式,改变着教育的模式。通过定量研究,以数据结构实验课为载体,对学生在基于Groove的虚拟学习社区中进行协作学习的态度、学习成绩、信息素养和网络互动能力的分析,得出了具有一定实用价值的策略。     

Curious learning, cultural bias, and the learning curve

Today's interactive installations are characterized by simplistic feedback mechanisms, limited underlying databases of content, and very little autonomous intelligence. Yet even these rudimentary systems provide great opportunities for curious learning, surprise, and serendipitous discovery. They also serve as a social nexus. The interpersonal, back-channel communications and ancillary activities of the audience, which currently remain largely unsensed and unprocessed, can be just as important as the primary authored experience. The key to accessing and communicating larger perceptions in the digital age may well lie in the collective social activities that occur within the context of shared narratives. The exchanges and experiences of group exploration and discovery, communal curious learning promises rewards far beyond the mere enjoyment of story and the instant gratifications of an individual's remote control     

基于深度学习和半监督学习的入侵检测算法

深度学习是一种具有自动学习能力的智能算法,通过自我学习到的经验而动态地提升分类的能力。深度学习的思想与入侵检测中通过学习而提高检测率和降低误报率的思想是相同的。对于入侵检测准确率不高的情况,提出了一种基于深度学习和半监督学习的入侵检测方法,该方法使用深度学习对特征提取和后验概率对未标记数据进行分类,提高了分类性能。使用KDD CUP99数据集对算法准确性进行了验证,实验结果验证该方法能够有效改善入侵检测系统性能。     

移动学习中学习共同体的设计与实现

移动学习和学习共同体是当前教育技术领域两个研究的热点.但是对于移动学习中学习共同体的研究却少之又少.简单介绍了移动学习及其特点,分析了移动学习共同体和网络学习共同体之间的差异,并最终设计和实现了移动学习中的学习共同体.     

虚拟学社中协作式学习模式探析

虚拟学社是计算机技术与多媒体网络技术的产物,是一种网上学习环境.从虚拟学社和协作式学习的定义出发,探讨了基于虚拟学社的协作式学习模式的内涵,提出该学习模式的设计原则,详细阐述了虚拟学社协作学习平台的建构,以及该学习模式的实现方式.     

Fast competitive learning with classified learning rates for vector quantization

In this paper, we present a new competitive learning algorithm with classified learning rates, and apply it to vector quantization of images. The basic idea is to assign a distinct learning rate to each reference vector. Each reference vector is updated independently of all the other reference vectors using its own learning rate. Each learning rate is changed only when its corresponding reference vector wins the competition, and the learning rates of the losing reference vectors are not changed. The experimental results obtained with image vector quantization show that the proposed method learns more rapidly and yields better quality of the coded images than conventional competitive learning method with a scalar learning rate.