e-Learning 2.0的基础理论探究

随着e-Learning 2.0应用的不断扩展和深入,其理论缺失的弊端已日益突出。在大量实践工作的基础上,本文总结与提炼了四个e-Learning 2.0的基础理论:学习控制理论、灵活学习内容理论、社会学习理论和泛在学习理论,阐述了每个理论的主要观点,剖析了其组成要素。

Web2.0下的E-Learning变革:从单向传递到协同共享

随着互联网技术从Web1.0发展到Web2.0,E-learning也相应地从E-learning1.0进化到E-learning2.0。E-learning1.0下的教学方式主要体现为单向传递,E-learning2.0下的教学方式则发展和超越了E-learning1.0,更多体现为协同共享。两种教学方式在师生角色、知识管理和传播方式、职责分配、学习目标定义和成绩考核等方面都有质的不同。文章提出E-learning2.0将是单向传递与协同共享教学方式的融合,这个融合的过程不仅需要硬件上的升级,更需要人们思维模式上的转变。

Learning 2.0:图书馆员2.0培训初探

文章首先说明了图书馆员学习2.0技术的必要性,然后通过举例的方法陈述了国外图书馆Web2.0培训项目的实践,最后总结分析了项目的特点以供国内图书馆借鉴。

从数字图书馆到e-learning2.0

21世纪e-learning(数字化学习)进入2.0时代,学习者成为学习活动的中心,通过先进的平台技术及点对点(P2P)工具,学习者突破了时间、空间、语言的局限,数字图书馆加上e-learning造就了终身学习的理想平台。通过数字图书馆在e-learning领域的应用,展示了以学习者为中心的e-learning2.0的无限潜力。参考文献8。

Effects of Media and Distributed Information on Collaborative Concept-Learning

The present study explores the effects of media and distributed information on the performance of remotely located pairs of people′s completing a concept-learning task. Sixty pairs performed a concept-learning task using either audio-only or audio-plus-video for communication. The distribution of information includes three levels: with totally same information, with partly same information, and with totally different information. The subjects′ primary psychological functions were also considered in this study. The results showed a significant main effect of the amount of information shared by the subjects on the number of the negative instances selected by the subjects, and a significant main effect of media on the time taken by the subjects to complete the task.

Learning2.0时代高校教师角色转换的策略研究

高校教师是影响大学教学质量和学生满意度的关键角色,Learning2.0理念开放、协作与共享的内涵和学习方式要求高校教师具有与以往不同的责任指向和角色意识。高校教师需要更新教育理念、树立新型的师生观念、创新教学设计思路、勇敢尝试新技术以促进信息化教学能力的提升,充当大学生学习的引导者、组织者和促进者。

基于Learning2.0理念的《现代教育技术》课程学习活动设计研究

Learning2.0作为信息时代一种新的学习理念,它强调学生在学习过程中的主动参与,积极互动,协作分享和集体智慧,该理念能够为信息化环境下的课程教学改革和创新提供新思路。学习活动设计是教学设计的核心内容,也是改变课堂教学结构、方式和管理模式的很好途径。在Learning2.0理念的指导下对《现代教育技术》课程的学习活动进行设计。从学习活动开展的主题、环境、流程及评价这几个方面展开研究设计与实践应用,以期能够改进《现代教育技术》课程中存在的部分问题。

On the Task-based Collaborative Learning

Task-based language teaching(TBLT) has been a prevalent teaching practice in the TEFL field in the recent years and its momentum for striving to be the legitimate one has never ceased. The present study tries to provide a theoretical foundation for its application in the communicative learning approach of English as the second language(ESL),namely the collaborative learning mode.

Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective

Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.

The MOOC/SPOC Based"1+M+N"Multi-University Collaborative Teaching and Learning Mode:Practice and Experience

Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level and quality becomes an important issue. In China, many universities have explored the new modes and approaches for MOOC/SPOC-based teaching and learning. Especially, the China MOOC Association on Computing Education(CMOOC association), established in 2014, has done a set of successful practice and achieved fruitful experiences on MOOC courses development and computer education reform. Based on the practical experiences, a MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode is presented, which is adapted to the real situation of Chinese university education. In the paper, the practices and experiences of CMOOC association are introduced, the MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode and its approaches are described. Finally, the suggestions for MOOCs development and applications are also presented.

Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

Hidden Two-Stream Collaborative Learning Network for Action Recognition

The two-stream convolutional neural network exhibits excellent performance in the video action recognition.The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames,to train a model each,and to finally integrate the outputs of the two models.Nevertheless,the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition,and the temporal and the spatial streams are just simply fused at the ends,with one stream failing and the other stream succeeding.We propose a novel hidden two-stream collaborative(HTSC)learning network that masks the steps of extracting the optical flow in the network and greatly speeds up the action recognition.Based on the two-stream method,the two-stream collaborative learning model captures the interaction of the temporal and spatial features to greatly enhance the accuracy of recognition.Our proposed method is highly capable of achieving the balance of efficiency and precision on large-scale video action recognition datasets.

Deep Reinforcement Learning-Based Collaborative Routing Algorithm for Clustered MANETs

Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of services.Mobile ad hoc networks(MANETs)are able to provide flexible communication services to users through self-configuration and rapid deployment.However,the dynamic wireless environment,the limited resources,and complex QoS requirements have presented great challenges for network routing problems.Motivated by the development of artificial intelligence,a deep reinforcement learning-based collaborative routing(DRLCR)algorithm is proposed.Both routing policy and subchannel allocation are considered jointly,aiming at minimizing the end-to-end(E2E)delay and improving the network capacity.After sufficient training by the cluster head node,the Q-network can be synchronized to each member node to select the next hop based on local observation.Moreover,we improve the performance of training by considering historical observations,which can improve the adaptability of routing policies to dynamic environments.Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion.In addition,the effectiveness of the routing policy in a dynamic environment is verified.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation

In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.

E-learning 2.0理念下网络学习平台的设计

Web2.0的到来使数字化学习发展成E-learning 2.0。文章从理论和学习实践出发,结合知识管理与Web2.0工具的使用,构建一个开放、分享的学习平台环境,以提供丰富的学习辅助手段给学习者,让学习者能够根据自己的需求,达成自己的学习目标。

Studying Design and Use of Healthcare Technologies in Interaction: The Social Learning Perspective in a Dutch Quality Improvement Collaborative Program

Designing technologies is a process that relies on multiple interactions between design and use contexts. These interactions are essential to the development and establishment of technologies. This article seeks to understand the attempts of healthcare organisations to integrate use contexts into the design of healthcare technologies following insights of the theoretical approaches of social learning and user representations. We present a multiple case study of three healthcare technologies involved in improving elderly care practice. These cases were part of a Dutch quality improvement collaborative program, which urged that development of these technologies was not “just” development, but should occur in close collaboration with other parts of the collaborative program, which were more focused on implementation. These cases illustrate different ways to develop technologies in interaction with use contexts and users. Despite the infrastructure of the collaborative program, interactions were not without problems. We conclude by arguing that interactions between design and use are not naturally occurring phenomena, but must be actively organised in order to create effects.

A Sentinel-Based Peer Assessment Mechanism for Collaborative Learning

This paper introduces a novel mechanism to improve the performance of peer assessment for collaborative learning.Firstly,a small set of assignments which have being pre-scored by the teacher impartially,are introduced as“sentinels”.The reliability of a reviewer can be estimated by the deviation between the sentinels’scores judged by the reviewers and the impartial scores.Through filtering the inferior reviewers by the reliability,each score can then be subjected into mean value correction and standard deviation correction processes sequentially.Then the optimized mutual score which mitigated the influence of the subjective differences of the reviewers are obtained.We perform our experiments on 200 learners.They are asked to submit their assignments and review each other.In the experiments,the sentinel-based mechanism is compared with several other baseline algorithms.It proves that the proposed mechanism can effectively improve the accuracy of peer assessment,and promote the development of collaborative learning.

An Empirical Study of the Optimum Team Size Requirement in a Collaborative Computer Programming/Learning Environment

Pair programming has been widely acclaimed the best way to go in computer programming. Recently, collaboration involving more subjects has been shown to produce better results in programming environments. However, the optimum group size needed for the collaboration has not been adequately addressed. This paper seeks to inculcate and acquaint the students involved in the study with the spirit of team work in software projects and to empirically determine the effective (optimum) team size that may be desirable in programming/learning real life environments. Two different experiments were organized and conducted. Parameters for determining the optimal team size were formulated. Volunteered participants of different genders were randomly grouped into five parallel teams of different sizes ranging from 1 to 5 in the first experiment. Each team size was replicated six times. The second experiment involved teams of same gender compositions (males or females) in different sizes. The times (efforts) for problem analysis and coding as well as compile-time errors (bugs) were recorded for each team size. The effectiveness was finally analyzed for the teams. The study shows that collaboration is highly beneficial to new learners of computer programming. They easily grasp the programming concepts when the learning is done in the company of others. The study also demonstrates that the optimum team size that may be adopted in a collaborative learning of computer programming is four.