Social Learning Capabilities in Broward’s Black Male Success Task Force: Informing Collective Impact Initiatives

Broward Schools is addressing an achievement gap for Black males with a collective impact initiative. Collective impact initiatives address complex social problems. The social learning capability of the initiative can be enhanced by applying Wenger’s (2009) social learning spaces, learning as citizenship, and social artists concepts.

First-priority relation graph-based malicious users detection in mobile social networks

Mobile social networks (MSNs) consist of many mobile users (individuals) with social characteristics, that provide a variety of data delivery services involving the social relationship among mobile individuals. Because mobile users move around based on their common interests and contact with each other more frequently if they have more social features in common in MSNs. In this paper, we first propose the first-priority relation graph, say FPRG, of MSNs. However, some users in MSNs may be malicious. Malicious users can break the data delivery through terminating the data delivery or tampering with the data. Therefore, malicious users will be detected in the process of looking for the data delivery routing to obtain efficient and reliable data delivery routing along the first-priority relation graph. Secondly, we propose one hamiltonian cycle decomposition of FPRG-based adaptive detection algorithm based on in MSNs under the PMC detection model (the system-level detection model).

‘‘Model age-based” and ‘‘copy when uncertain” biases in children’s social learning of a novel task

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N = 140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children’s continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of mode

Trustworthiness-hypercube-based reliable communication in mobile social networks

In mobile social networks (MSNs), the routing packet is forwarded from any user of in a group to any user of the other group until it reaches the destination group - the group where the destination is located. However, it is inevitable that malicious groups could compromise the quality and reliability of data. To alleviate such effect, analyzing the trustworthiness of a group has a positive influence on the confidence with which a group conducts transactions with that group. In our previous work, the feature-based first-priority relation graph (FPRG) of MSNs is proposed, in which two vertices (groups) are connected iff they have a first-priority relationship. In this paper, the trustworthiness computation of a group is firstly presented in the algorithm TC (Trustworthiness Computing) based on the FPRG. The trustworthiness of a group is evaluated based on the trustworthiness of neighbors and the number of malicious users in the group. We then establish the Trustworthiness-Hypercube-based Reliable Communication (THRC) algorithm in MSNs. The algorithm THRC can provide an effective and reliable data delivery routing. Finally, we also give two scenario simulations to elaborate the processes of the trustworthiness computation and reliable communication.

The social network virtual design studio : integrated design learning using blended learning environments

Online communications, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted to. Social Networks (SN), as instruments for communication, have provided a potentially fruitful operative base for VDS. These technologies transfer communication, leadership, democratic interaction, teamwork, social engagement and responsibility away from the design tutors to the participants. The implementation of Social Network VDS (SNVDS) moved the VDS beyond its conventional realm and enabled students to develop architectural design that is embedded into a community of learners and expertise both online and offline. Problem-based learning (PBL) becomes an iterative and reflexive process facilitating deep learning. The paper discusses details of the SNVDS, its pedagogical implications to PBL, and presents how the SNVDS is successful in enabling architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, knowledge and construction with a rich learning experience.

Early adoption of mobile devices : a social network perspective

As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impact the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network, and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

Early adoption of mobile devices : a social network perspective

As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impacted the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network; and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.

Virtual design studio within a blended social network

Online interactions, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted. Social Networks (SN), as instruments for communication, have provided a potentially fruitful operative base for VDS. These technologies transfer communication, leadership, democratic interaction, teamwork, social engagement and responsibility away from the design tutors to the participants. The implementation of a Social Network VDS (SNVDS) moved the VDS beyond its conventional realm and enabled students to develop architectural design that is embedded into a community of learners and their expertise both online and offline. Problem-based learning (PBL) becomes an iterative and reflexive process facilitating deep learning. The paper discusses details of the SNVDS, its pedagogical implications to PBL, and presents how the SNVDS is successful in empowering architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, knowledge and construction with a rich learning experience.

The social network learning cloud: architectural education for the 21 century

In less than a decade, architectural education has, in some ways, significantly evolved. The advent of computation has not so much triggered the change, but Social Networks (SN) have ignited a novel way of learning, interaction and knowledge construction. SN enable learners to engage with friends, tutors, professionals and peers, form the base for learning resources, allow students to make their voices heard, to listen to other views and much more. They offer a more authentic, inter-professional and integrated problem based, Just-in-Time (JIT), Just-in-Place (JIP) learning. Online SN work in close association with offline SN to form a blended social learning realm-the Social Network Learning Cloud (SNLC)-that greatly enables and enhances students' learning in a far more influential way than any other learning means, resources or methods do. This paper presents a SNLC for architectural education that provides opportunities for linking the academic Learning Management Systems (LMS) with private or professional SN such that it enhances the learning experience and deepens the knowledge of the students. The paper proposes ways of utilising SNLC in other learning and teaching areas of the curriculum and concludes with directions of how SNLC then may be employed in professional settings.

Sustainable futures for learning in a climate of change: mobile apps, social media, and crisis informatics during emergencies and disasters

Emergencies and disasters are different types of crisis events which can affect students and staff in their on-campus roles and off-campus activities. In such events, mobile technologies, mobile software applications (apps), and mobile social networks are becoming increasingly relied upon to communicate, to swiftly send and received information and images, to deliver learning moments, and to check the safety of colleagues and friends. This paper investigates the intersection of mlearning, mobile social media, mobile apps, and crisis informatics in times of emergencies and disasters, using the recent Gipplsand earthquake in south-eastern Australia as an exemplar. It also discusses proactive preparation for educational resilience during emergencies and disasters.

Virtual design studio via social networks

With the advent of social networks, it became apparent that the social aspect of designing and learning plays a crucial role in students’ education. The ease of communication, leadership opportunity, democratic interaction, teamwork, and the sense of community are some of the aspects that are now in the centre of design interaction. Online interactions, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted. On the sample of a design studio at Deakin University the paper discusses details of the Social Network VDS, its pedagogical implications to PBL, and presents how it is successful in empowering architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, and construction of knowledge. It studies the effectiveness of the generated social intelligence and explores the facilitation of students’ self-directed learning. Hereby the paper studies the construction of knowledge via social interaction and how blended learning environments foster motivation and information exchange.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

Writing together, learning together : the value and effectiveness of a research writing group for doctoral students

The high level of scholarly writing required for a doctoral thesis is a challenge for many research students. However, formal academic writing training is not a core component of many doctoral programs. Informal writing groups for doctoral students may be one method of contributing to the improvement of scholarly writing. In this paper, we report on a writing group that was initiated by an experienced writer and higher degree research supervisor to support and improve her doctoral students’ writing capabilities. Over time, this group developed a workable model to suit their varying needs and circumstances. The model comprised group sessions, an email group, and individual writing. Here, we use a narrative approach to explore the effectiveness and value of our research writing group model in improving scholarly writing. The data consisted of doctoral students’ reflections to stimulus questions about their writing progress and experiences. The stimulus questions sought to probe individual concerns about their own writing, what they had learned in the research writing group, the benefits of the group, and the disadvantages and challenges to participation. These reflections were analysed using thematic analysis. Following this analysis, the supervisor provided her perspective on the key themes that emerged. Results revealed that, through the writing group, members learned technical elements (e.g., paragraph structure), non-technical elements (e.g., working within limited timeframes), conceptual elements (e.g., constructing a cohesive arguments), collaborative writing processes, and how to edit and respond to feedback. In addition to improved writing quality, other benefits were opportunities for shared writing experiences, peer support, and increased confidence and motivation. The writing group provides a unique social learning environment with opportunities for: professional dialogue about writing, peer learning and review, and developing a supportive peer network. Thus our research writing group has proved an effective avenue for building doctoral students’ capability in scholarly writing. The proposed model for a research writing group could be applicable to any context, regardless of the type and location of the university, university faculty, doctoral program structure, or number of postgraduate students. It could also be used within a group of students with diverse research abilities, needs, topics and methodologies. However, it requires a group facilitator with sufficient expertise in scholarly writing and experience in doctoral supervision who can both engage the group in planned writing activities and also capitalise on fruitful lines of discussion related to students’ concerns as they arise. The research writing group is not intended to replace traditional supervision processes nor existing training. However it has clear benefits for improving scholarly writing in doctoral research programs particularly in an era of rapidly increasing student load.

The importance of a new kind of learning in collaborative networks

There is wide agreement that in order to manage the increasingly complex and uncertain tasks of business, government and community, organizations can no longer operate in supreme isolation, but must develop a more networked approach. Networks are not ‘business as usual’. Of particular note is what has been referred to as collaborative networks. Collaborative networks now constitute a significant part of our institutional infrastructure. A key driver for the proliferation of these multiorganizational arrangements is their ability to facilitate the learning and knowledge necessary to survive or to respond to increasingly complex social issues In this regard the emphasis is on the importance of learning in networks. Learning applies to networks in two different ways. These refer to the kinds of learning that occur as part of the interactive processes of networks. This paper looks at the importance of these two kinds of learning in collaborative networks. The first kind of learning relates to networks as learning networks or communities of practice. In learning networks people exchange ideas with each other and bring back this new knowledge for use in their own organizations. The second type of learning is referred to as network learning. Network learning refers to how people in collaborative networks learn new ways of communicating and behaving with each other. Network learning has been described as transformational in terms of leading to major systems changes and innovation. In order to be effective, all networks need to be involved as learning networks; however, collaborative networks must also be involved in network learning to be effective. In addition to these two kinds of learning in collaborative networks this paper also focuses on the importance of how we learn about collaborative networks. Maximizing the benefits of working through collaborative networks is dependent on understanding their unique characteristics and how this impacts on their operation. This requires a new look at how we specifically teach about collaborative networks and how this is similar to and/or different from how we currently teach about interorgnizational relations.