Risky business or sharing the load? - Social flow in collaborative mobile learning

Mobile learning has been built upon the premise that we can transform traditional classroom or computer-based learning activities into a more ubiquitous and connected form of learning. Tentative outcomes from this assertion have been witnessed in many collaborative learning activities, but few analytic observations on what triggers this collaboration have so far been made. However Social Flow, a concept framework that extends Csikszentmihalyi’s flow theory, may help us to partially explain the triggering mechanism of collaborative mobile learning. Our case study in this article, where learners together explore a built environment as part of a simulated security guard training programme, describes how the concept of social flow in a collaborative learning space might sketch out what triggers an optimal learning experience in collaboration and what can be additionally achieved in a collaborative learning experience. In this learning context, collaborative mobile learning might be seen to prompt more knowledge generation and extra learning tasks by fostering greater motivation than other learning environments.     

Exploring collaborative learning effect in blended learning environments

The use of new technology encouraged exploration of the effectiveness and difference of collaborative learning in blended learning environments. This study investigated the social interactive network of students, level of knowledge building and perception level on usefulness in online and mobile collaborative learning environments in higher education. WeChat, which is a mobile synchronous communication tool, and modular object‐oriented dynamic learning environment (Moodle) were used as mobile and online collaborative learning settings. Seventy‐eight college students majoring in information engineering participated in the experiment. The following findings were revealed by combining methods of social network analysis, content analysis and questionnaire survey: (1) the collaborative social networks generated in this study showed that students had tighter interaction relationships in Moodle than in WeChat; (2) deeper level of knowledge building in collaboration and interaction through Moodle than WeChat was observed; and (3) Moodle got higher perception level than WeChat because of its usefulness for collaboration.     

eScience in the Social Cloud

Social networks offer great potential for fostering collaboration between individuals and amongst groups. This potential collaborative environment is not only applicable for recreation, but can also provide considerable value to diverse research communities. For this reason scientists are increasingly utilizing social networking concepts in projects to form groups, share information, publicize their work and communicate with their peers. This article describes two different approaches to supporting eScience, by providing scientific computing and collaboration within what we term the Social Cloud. In our first approach the social network is used as a collaborative overlay, in combination with the ad hoc creation of infrastructure composed of virtual machine clusters built from resources contributed, by the users, to the Social Cloud. Our second approach is based around the principle of volunteer computing, where the Social Cloud provides researchers with a platform to exploit social networks by reaching out to non technical users who would otherwise be unlikely to donate computational time for scientific and other research. In this article we specifically explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through the use of incentives in existing social networks.     

群组协作社交网络关系的分析及可视化

社交网络的研究应用领域广泛,大多数的研究主要关注于社交网络结构中节点和链接的变化,研究角度较为单一.而群组协作关系在社交网络中较为普遍,为更好地探索群组协作过程中群组级别网络结构的演变,首先将组级任务添加到用于图可视化的任务分类中,针对协作关系网络的特性,设置一个适用于群组协作关系分析的任务分类法,根据此任务分类法设置...     

Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design

As a newly-developed information exchange and management platform, Building Information Modeling (BIM) is altering the way of collaboration among multi-engineers for civil engineering projects. During the BIM implementation, a large number of event logs are automatically generated and accumulated to record details of the model evolution. For knowledge discovery from huge logs, a novel BIM event log mining approach based on the dynamic social network analysis is presented to examine designers’ performance objectively, which has been verified in BIM event logs about an ongoing year-long design project. Relying on meaningful information extracted from time-stamped logs, networks on the monthly interval are built to graphically represent information and knowledge sharing among designers. Special emphasis is put on measuring designers’ influence by a defined new metric called “impact score”, which combines the k-shell method and 1-step neighbors to achieve comparatively low computational cost and high accurate ranking. Besides, an emerging machine learning algorithm named CatBoost is utilized to predict designers’ influence intelligently by learning features from both network structure and human behavior. It has been found that twelve networks can be easily distinguished into two collaborative patterns, whose characteristics in both network structures and designers’ behaviors are significantly different. The most influential designers are similar within the same group but varied from different groups. Extensive analytical results confirm that the method can potentially serve as month-by-month feedback to monitor the complex modeling process, which further supports managers to realize data-driven decision making for better leadership and work plan towards an optimized collaborative design.     

Social network site use,mobile personal talk and social capital among teenagers

Social network sites (SNSs) and mobile phones are becoming increasingly important in teenagers’ lives. Using data collected from a nationally representative survey (N = 800), this study explores the variation of social capital by SNS adoption, different SNS activities, and mobile personal talk among teenagers. The results indicate that SNS adoption and mobile personal talk can not only enhance teenagers’ close ties with friends, but also jointly promote teenagers’ civic engagement. Among SNS users, mobile personal talk also increase teens’ network capital. Different SNS activities such as commenting on friend’s Facebook pictures and joining Facebook groups have different relationships with social capital, and such relationships are moderated by mobile personal talk.     

基于扩展的Granott相互作用模式的移动协同学习模型研究

传统的移动协同学习模型的研究和架构都是从技术的角度提出,而忽略了协同过程中学习者相互作用的社会性对其的影响程度。首先在经典Granott模型的基础上研究了一种扩展的Granott相互作用模式,然后提出了一种基于扩展的Granott相互作用模式的移动协同学习模型,并对其中的Granott驱动逻辑对象进行了形式化算法描述,最后通过原型系统对模型进行了验证和实现。模型支持移动学习者基于扩展的Granott相互作用模式获取协同过程中同伴的协同行为,从而有效地完成协同学习工作。     

Knowledge cloud system for network collaboration: A case study in medical service industry in China

This paper introduces a new service model in cloud computing – the knowledge as a service (KaaS) that facilitates the interoperations among members in a knowledge network. In this study, the KaaS is modeled as a framework of a knowledge cloud system which aims for the development of collaborative networks in medical service industry. The framework is applied in a case study of radiotherapy dynamic treatment service. The collaborative activities supported by knowledge cloud system are analysed. This study has investigated the essential components for the success of a private knowledge network for business network collaboration. This study shows the best practices of the formation of collaborative network facilitating by the knowledge cloud system in medical service industry which can be transferred to other industries.     

High level MANET protocol: Enhancing the communication support for mobile collaborative work

Mobile collaborative activities involve on-demand interactions among nomad users. Unavailability of communication support in the physical scenario where users are located cannot be a limitation to carry out such collaboration instances. Mobile workers can take advantage of the communication capability embedded in their mobile devices in order to create communication channels between them. The Mobile Ad-hoc Networks (MANET) are infrastructures that can be used to support the nomad users' activities. However, these networks have a short communication threshold; therefore, they need to include a routing protocol as part of its infrastructure to allow mobile workers to collaborate when they are not physically close. This paper presents an application level routing protocol that was designed to support nomad workers performing mobile collaborative activities. The protocol, named High Level MANET Protocol (HLMP), provides several automatic services that are required by mobile collaborative systems. Some of these services are the automatic MANET formation, peer detection and messages routing. HLMP has been implemented in a mobile communication infrastructure and used in several mobile groupware systems.     

Social networks,communication styles,and learning performance in a CSCL community

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both individual and structural factors (i.e., communication styles and a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high willingness to communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages. We also found that the resultant social network properties significantly influenced learners’ performance to the extent that central actors in the emergent collaborative social network tended to get higher final grades. The study suggests that communication and social networks should be central elements in a distributed learning environment. We also propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.     

A scalable framework for mobile real-time group communication services

A scalable framework for mobile real-time group communication services is developed in this paper. Examples for possible applications of this framework are mobile social networks, mobile conference calls, mobile instant messaging services, and mobile multi-player on-line games. A key requirement for enabling a real-time group communication service is the tight constraint imposed on the call delivery delay. Since establishing such communication service for a group of independent mobile users under a tight delay constraint is NP-hard, a two-tier architecture is proposed, that can meet the delay constraint imposed by the real-time service requirement for many independent mobile clients in a scalable manner. This goal is achieved by two dimensional partition of the space, first by organization and then geographically. Both the time and memory complexity associated with the location management of N mobile users are O(N) for the location management provided by the proposed framework, while a distributed scheme requires O(N²) for both time and memory complexity.     

How to orchestrate R&D networks? The role of orchestration subprocesses and collaborative practices over time

Although R&D networks have been increasingly used by companies to foster innovation through collaboration, there are still challenges regarding how to successfully orchestrate such projects. The goal of this study is to analyse how a hub firm orchestrates an R&D network through collaborative practices. We based our study on the three orchestration subprocesses proposed by Dhanaraj and Parkhe, namely, to foster knowledge mobility, guarantee innovation appropriability, and maintain network stability. To achieve our goal, we analysed the collaborative practices in an R&D network in the semiconductor industry in France. The case consists of an R&D network among 26 companies to create a new transistor. The data include 65 interviews and 192 documents. The study contributes to the literature on network orchestration by showing that specific collaborative practices performed by a hub firm enable a fourth orchestration subprocess—network reconfiguration—which is necessary in long‐lasting and complex R&D networks and also affects the other three subprocesses. Network reconfiguration consists in disassociating and attracting new members to the R&D network to cover knowledge demands, and is performed by the hub‐firm through specific collaborative practices. The results can benefit practitioners by showing which collaborative practices may be useful for a hub firm throughout the development of an R&D network.     

基于社会网络的DDoS防御协作关系优化算法

针对以往研究中协作关系建立过程不合理及其优化影响因素考虑不全面的问题, 基于社会网络思想, 提出了一种DDoS防御协作关系优化算法。从复杂适应系统和社会网络两个角度分析了DDoS防御协作关系特点, 给出了协作关系优化思路; 建立了一种五元组协作关系优化影响因素分析模型, 并阐述了模型内各元组之间的关系; 参考社会网络有效连带关系的建立过程, 采用强化学习思想, 设计了协作关系优化算法。DDoS攻防仿真实验结果验证了算法的有效性, 该算法能够获得较少交互关系数, 降低交互协作信息量, 提高整体防御能力。     

Collaborative learning in postgraduate level courses

Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of this, collaborative and mobile learning in educational environments have been encouraged thanks to the use of computing for human learning. By coupling the advantages of collaborative and mobile learning, the teaching-learning processes involved in postgraduate courses may be greatly enhanced. The pedagogical experiences in this regard lived by the authors in the Alpha–Beta Research Group when coupling collaborative and mobile learning in the context of postgraduate level courses, are presented in this paper.     

Structural effects of R&D collaboration network on knowledge diffusion performance

With the advent of knowledge-based economy, the process and structure of knowledge diffusion mechanism in R&D collaboration networks have become more complex and dynamic. However, regardless of the complexity and importance of these R&D collaboration networks, previous empirical analyses have focused mainly on statics, which cannot be a sufficient basis to thoroughly explore knowledge diffusion in the R&D collaboration network. Furthermore, there has been little research done on the relationship between knowledge diffusion and the structure of an R&D collaboration network. Hence, this paper modeled knowledge diffusion based on social network analysis. It also investigated the impact of network structure on the performance of knowledge diffusion, focusing on average knowledge stock and knowledge variance. The results confirm that the small-world network is the most efficient and equitable structure toward effective knowledge diffusion.     

Comparing Strategies of Collaborative Networks for R&D: An Agent-Based Study

In this work we analyze the evolving dynamics of different strategies of collaborative networks that emerge from the creation and diffusion of knowledge. An evolutionary economic approach is adopted by introducing decision rules that are applied routinely and an agent-based model is developed. Firms (the agents) can collaborate and create networks for research and development purposes. We have compared three collaboration strategies (A—peer-to-peer complementariness, B—concentration process and C—virtual cooperation networks) that were defined on the basis of literature and on empirical evidence. Strategies are introduced exogenously in the simulation. The aims of this paper are twofold: (i) to analyze the importance of the networking effects; and (ii) to test the differences among collaboration strategies. It was possible to conclude that profit is associated with higher stock of knowledge and with smaller network diameter. In addition, concentration strategies are more profitable and more efficient in transmitting knowledge through the network. These processes reinforce the stock of knowledge and the profit of the firms located in the centers of the networks.     

Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance

Social networks and gamification are having an important and growing role in education. Social networks provide unknown communication and connection possibilities while games have the potential to engage students. This paper analyzes the structure of the social network resulting from a gamified social undergraduate course as well as the influence that student's position has on learning achievement. In a semester long experiment, a social networking site was delivered to students providing gamified activities and enabling social interaction and collaboration. Social network analysis was used to build the network graph and to compute four measures of the overall network and nine measures for each participant. Individual measures were then assessed as predictors of students' achievement using three different methods: correlation, principal component analysis and multiple linear regressions. The resulting social network has 167 actors and 2505 links, and it can be characterized as a small-world. All analyses agreed on the potential of structural metrics as predictors of learning achievement but they differ in the measures considered as significant. A moderate correlation was found between most centrality measures and learning achievement.     

Boosting social collaborations based on contextual synchronization: An empirical study

Supporting context-based collaboration among online users is an important issue to computer-mediated collaboration to fulfill specified tasks. However, several problems make it difficult to be aware of the context. The context of the user task can be (i) dynamic (i.e., changing over time), and (ii) mixed with multiple sub-contexts together. We propose a novel ontology-based platform to overcome these problems. It finds the most relevant users from a given social network, taking into account two types of context (i.e., personal and group contexts) and matching them. By measuring similarities between the personal contexts, we can dynamically organize a number of communities, so that users can be contextually synchronized. Individual users can be involved in complex collaborations related to multiple semantics. This paper demonstrates and discusses how the proposed context synchronization process is able to boost social collaborations. We show the experimental results collected from a collaborative information searching system. The main empirical issues in this work are (i) setting thresholds, (ii) searching performance, and (iii) scalability testing.     

Toward collaboration sensing

We describe preliminary applications of network analysis techniques to eye-tracking data collected during a collaborative learning activity. This paper makes three contributions: first, we visualize collaborative eye-tracking data as networks, where the nodes of the graph represent fixations and edges represent saccades. We found that those representations can serve as starting points for formulating research questions and hypotheses about collaborative processes. Second, network metrics can be computed to interpret the properties of the graph and find proxies for the quality of students’ collaboration. We found that different characteristics of our graphs correlated with different aspects of students’ collaboration (for instance, the extent to which students reached consensus was associated with the average size of the strongly connected components of the graphs). Third, we used those characteristics to predict the quality of students’ collaboration by feeding those features into a machine-learning algorithm. We found that among the eight dimensions of collaboration that we considered, we were able to roughly predict (using a median-split) students’ quality of collaboration with an accuracy between ~85 and 100 %. We conclude by discussing implications for developing “collaboration-sensing” tools, and comment on implementing this approach for formal learning environments.     

基于角色-活动模型的移动制造协同链建模

针对移动制造协同过程的动态性、协同性和异构性,提出了移动制造协同链的概念和一种角色-活动模型。该模型针对移动制造协同过程的特点,综合考虑了角色、活动、流程之间的关系。在分别对角色和活动的属性进行编码的基础上,该模型通过遗传操作实现了活动的变异与过程的变异。建模过程则按照活动定义、角色定义、模板定义和项目的定义的顺序实现。所开发的原型系统证明,基于角色-活动模型的移动制造协同链模型满足了移动制造协同过程建模的需求。