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.     

个体在线知识分享行为的惯性效应分析

在线知识社群是大数据时代组织学习、协同创新、知识与信息交流与转移的重要方式。针对这种在线知识网络的分析、预测和管控,是大数据对经济社会实现数据服务和决策咨询功能的重要环节。以CMKT咨询俱乐部2群QQ群为例,搜集分析该群在2016年3月份所有的专业交流、讨论和分享记录,并基于这些实时数据建构CMKT动态知识网络,分析了在线动态知识网络中个体知识分享行为的策略性选择,最终验证了动态知识网络中个体分享行为的惯性效应。基于这一惯性效应,在大数据技术的支撑下,政府、高校或科研机构以及大型企业可以对其所构建的跨组织学习知识网络的动态演化趋势进行有效的分析和预测,并适时地选择干预或管控手段以引导知识网络更好地发挥在协同创新、知识转移、跨组织学习等方面的作用。     

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.     

How is the Semantic Web evolving? A dynamic social network perspective

Finding how the Semantic Web has evolved can help understand the status of Semantic Web community and predict the diffusion of the Semantic Web. One of the promising applications of the Semantic Web is the representation of personal profiles using Friend of a Friend (FOAF). A key characteristic of such social networks is their continual change. However, extant analyses of social networks on the Semantic Web are essentially static in that the information about the change of social networks is neglected. To address the limitations, we analyzed the dynamics of a large-scale real-world social network in this paper. Social network ties were extracted from both within and between FOAF documents. The former was based on knows relations between persons, and the latter was based on revision relations. We found that the social network evolves in a speckled fashion, which is highly distributed. The network went through rapid increase in size at an early stage and became stabilized later. By examining the changes of structural properties over time, we identified the evolution patterns of social networks on the Semantic Web and provided evidence for the growth and sustainability of the Semantic Web community.     

基于P2P的协作学习平台的研究与设计

分析目前Web协作学习平台的研究现状,提出利用P2P网络的技术优势,打造一个"平台+资源+交流"的新型协作学习应用模式,并利用JXTA P2P技术设计一个协作学习平台,该平台支持协作学习全过程,主要包括学习小组支持、组内协作学习、协作学习成果评价,以及方便灵活的资源管理和多形式多层次的在线交互支持。     

The co-learning process in healthcare professionals: Assessing user satisfaction in virtual communities of practice

Communities of practice are nowadays an important concept in the healthcare sector. Particularly, the intensive use of ICT has allowed their creation into a virtual environment – Virtual Communities of Practice (VCoPs) developing optimal conditions to make possible the collaborative learning process. The VCoPs antecedents can be situated on social network phenomenon, where individuals with different traits but a common interest/objective are linked, use ICT potency (especially social media) to interchange information, experiences and contents among them. And as a result, people create and share knowledge, and learn collaboratively. VCoP users have a higher satisfaction level in the collaborative learning process when they can: (1) Achieve benefits related to patient diagnosis and treatment (cost reductions, faster management, quality and accuracy of diagnosis, etc.); (2) Increase the share capital of participants and creating networks of trusted individuals. Given the interest in this topic, the objective of this work is to identify the factors that determine user satisfaction in relation to Community Practice (CoP) and the process of building shared knowledge. For this, a sample of 130 Spanish health professionals participating in an online community, and developed in a virtual community of practice, is discussed. The results obtained from an analysis of logistic regression show evidence of the perception of efficiency and effectiveness in collaboration with the members of the VCoP as positively influencing the perceived satisfaction with the CoP. Also, the degree of individual participation in the community affects the degree of perceived satisfaction. The conclusions provide interesting strategic recommendations in the management process of the CoP.     

社交网络项目推荐模型及应用研究

将社交网络中目标用户和朋友之间相同兴趣产生的原因解释为潜在因子空间中的潜在因子,对社交网络中目标用户和朋友用户共同兴趣进行潜在因子分析,构建基于用户朋友关系的社交网络项目推荐模型,预测社交网络目标用户喜欢的项目。将基于社交网络项目推荐模型应用于实际应用场景中,研究表明与基于协同过滤技术的推荐方法相比较,该模型能够显著提高推荐质量,并具有良好的可扩展性。     

Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing

Designers face many system optimization problems when building distributed systems. Traditionally, designers have relied on optimization techniques that require either prior knowledge or centrally managed runtime knowledge of the system's environment, but such techniques are not viable in dynamic networks where topology, resource, and node availability are subject to frequent and unpredictable change. To address this problem, we propose collaborative reinforcement learning (CRL) as a technique that enables groups of reinforcement learning agents to solve system optimization problems online in dynamic, decentralized networks. We evaluate an implementation of CRL in a routing protocol for mobile ad hoc networks, called SAMPLE. Simulation results show how feedback in the selection of links by routing agents enables SAMPLE to adapt and optimize its routing behavior to varying network conditions and properties, resulting in optimization of network throughput. In the experiments, SAMPLE displays emergent properties such as traffic flows that exploit stable routes and reroute around areas of wireless interference or congestion. SAMPLE is an example of a complex adaptive distributed system.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Investigating recognition-based performance in an open content community: A social capital perspective

As the open source movement grows, it becomes important to understand the dynamics that affect the motivation of participants who contribute their time freely to such projects. One important motivation that has been identified is the desire for formal recognition in the open source community. We investigated the impact of social capital in participants’ social networks on their recognition-based performance; i.e., the formal status they are accorded in the community. We used a sample of 465 active participants in the Wikipedia open content encyclopedia community to investigate the effects of two types of social capital and found that network closure, measured by direct and indirect ties, had a significant positive effect on increasing participants’ recognition-based performance. Structural holes had mixed effects on participants’ status, but were generally a source of social capital.     

Understanding online consumers’ purchase intentions: a contribution from social network theory

There is a dynamic and interconnected international setting shaped by the power of the Internet and social media. To gain more consumers, understand their behaviours and needs, and maintain closest relationships with them, businesses should understand how consumers behave in social media and how they vary in their purchase intentions. In the scope of the study, we integrate the social network theory and the theory of planned behaviour to analyse online consumers’ purchase intentions and to investigate their structural positions by analysing their friendships in social networks. We target Twitter users to conduct analysis due to Twitter's popularity in use, market penetration, and opportunity to work with open-source data. This study contributes to a better theoretical understanding of online consumers’ purchase intentions by integrating multiple theoretical perspectives. It expands the literature by considering both online consumers’ friendship network in Twitter and their individual online purchasing intentions. The study also guides e-marketers to design proper strategies for potential and current consumers and target the right sets of people in the social networks.     

Personalization on a peer-to-peer television system

We introduce personalization on Tribler, a peer-to-peer (P2P) television system. Personalization allows users to browse programs much more efficiently according to their taste. It also enables to build social networks that can improve the performance of current P2P systems considerably, by increasing content availability, trust and the realization of proper incentives to exchange content. This paper presents a novel scheme, called BuddyCast, that builds such a social network for a user by exchanging user interest profiles using exploitation and exploration principles. Additionally, we show how the interest of a user in TV programs can be predicted from the zapping behavior by the introduced user-item relevance models, thereby avoiding the explicit rating of TV programs. Further, we present how the social network of a user can be used to realize a truly distributed recommendation of TV programs. Finally, we demonstrate a novel user interface for the personalized peer-to-peer television system that encompasses a personalized tag-based navigation to browse the available distributed content. The user interface also visualizes the social network of a user, thereby increasing community feeling which increases trust amongst users and within available content and creates incentives of to exchange content within the community.     

Effective bias reduction methods for passive source localization using TDOA and GROA

For passive source localization based on both TDOA and GROA, this paper proposes two bias reduction methods for the well-known Weighted-Least-Squares (WLS) estimator. We first derive the passive source localization bias from the two-step algebraic closed-form solution. This bias is found to be considerably larger than the Maximum Likelihood Estimator (MLE) and limits the WLS estimator’s practical applications. In this paper, We develop two methods to reduce the bias. The first one called Bias-Subtraction-Method (BSM) directly subtracts the expected bias from the solution of the WLS estimator, and the second one called Bias-Reduction-Method (BRM) imposes a constraint to the equation error formulation to improve the source location estimate. The noise covariance matrix must be known exactly in calculating the expected bias in BSM, and we only need to know the structure of it in BRM. For far-field sources localization when the noise is Gaussian and not too large, both of the two proposed methods can reduce the localization bias effectively and achieve the Cramér-Rao Lower Bound (CRLB) performance very well, and the BRM almost has the same performance as the MLE estimator. Simulations corroborate the performance of the two proposed methods.     

The ties that bind: Social network principles in online communities

In a Web 2.0 environment, the online community is fundamental to the business model, and participants in the online community are often motivated and rewarded by abstract concepts of social capital. How networks of relationships in online communities are structured has important implications for how social capital may be generated, which is critical to both attract and govern the necessary user base to sustain the site. We examine a popular website, Slashdot, which uses a system by which users can declare relationships with other users, and also has an embedded reputation system to rank users called ‘Karma’. We test the relationship between user's Karma level and the social network structure, measured by structural holes, to evaluate the brokerage and closure theories of social capital development. We find that Slashdot users develop deep networks at lower levels of participation indicating value from closure and that participation intensity helps increase the returns. We conclude with some comments on mechanism design which would exploit these findings to optimize the social networks and potentially increase the opportunities for monetization.     

学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

A collaborative learning approach for geographic information retrieval based on social networks

Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases.     

学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

Computing in social networks with relationship algebra

Communities are the latest phenomena on the Internet. At the heart of each community lies a social network. In this paper, we show a generalized framework to understand and reason in social networks. Previously, researchers have attempted to use inference-specific type of relationships. We propose a framework to represent and reason with general case of social relationship network in a formal way. We call it relationship algebra. In the paper, we first present this algebra then show how this algebra can be used for various interesting computing on a social network weaved in the virtual communities. We show applications such as determining reviewers in a semi-professional network maintained by conference management systems, finding conflict of interest in a publication system, or to infer various trust relationships in a community of close associates, etc. We also show how future community networks can be used to determine who should be immunized in the case of a contagious disease outbreak and how these networks could be used in crime prevention, etc.     

Actively learning to infer social ties

We study the extent to which social ties between people can be inferred in large social network, in particular via active user interactions. In most online social networks, relationships are lack of meaning labels (e.g., ??colleague?? and ??intimate friends??) due to various reasons. Understanding the formation of different types of social relationships can provide us insights into the micro-level dynamics of the social network. In this work, we precisely define the problem of inferring social ties and propose a Partially-Labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social relationships. The model formalizes the problem of inferring social ties into a flexible semi-supervised framework. We test the model on three different genres of data sets and demonstrate its effectiveness. We further study how to leverage user interactions to help improve the inferring accuracy. Two active learning algorithms are proposed to actively select relationships to query users for their labels. Experimental results show that with only a few user corrections, the accuracy of inferring social ties can be significantly improved. Finally, to scale the model to handle real large networks, a distributed learning algorithm has been developed.     

Homophily of Network Ties and Bonding and Bridging Social Capital in Computer-Mediated Distributed Teams

This research studied homophily of network ties in distributed teams in both task-related instrumental networks and non-task related expressive networks. Homophily of network ties was examined in terms of demographic and social characteristics, including gender, race, geographic location, and group assignment. Social network data were collected from 32 students enrolled in a distance learning class from two universities. MQAP regression analysis showed that homophily in gender and in race had no significant impact on the development of either instrumental or expressive ties. In instrumental networks, both homophily in group assignment and in location had significant impact on the development of network ties. In expressive networks, homophily in location had significant impact on the development of network ties, but the impact of homophily in group membership was only marginally significant. Further analysis of bonding ties with people of the same group and bridging ties with people from different groups showed that bonding social capital can exert significant influence on performance.