Community college faculty's teaching social networks and their implications for librarians

Collaboration between faculty and librarians is an important topic of discussion and research among academic librarians. These partnerships between faculty and librarians are vital for enabling students to become lifelong learners through their information literacy education. This research developed an understanding of academic collaborators by analyzing a community college faculty's teaching social networks. A teaching social network, an original term generated in this study, is comprised of communications that influence faculty when they design and deliver their courses. The communication may be formal (e.g., through scholarly journals and professional development activities) and informal (e.g., through personal communication) through their network elements. Examples of the elements of a teaching social network may be department faculty, administration, librarians, professional development, and students. This research asked 'What is the nature of faculty's teaching social networks and what are the implications for librarians?' This study moves forward the existing research on collaboration, information literacy, and social network analysis. It provides both faculty and librarians with added insight into their existing and potential relationships. This research was undertaken using mixed methods. Social network analysis was the quantitative data collection methodology and the interview method was the qualitative technique. For the social network analysis data, a survey was sent to full-time faculty at Las Positas College, a community college, in California. The survey gathered the data and described the teaching social networks for faculty with respect to their teaching methods and content taught. Semi-structured interviews were conducted following the survey with a sub-set of survey respondents to understand why specific elements were included in their teaching social networks and to learn of ways for librarians to become an integral part of the teaching social networks. The majority of the faculty respondents were moderately influenced by the elements of their network except the majority of the potentials were weakly influenced by the elements in their network in their content taught. The elements with the most influence on both teaching methods and content taught were students, department faculty, professional development, and former graduate professors and coursework. The elements with the least influence on both aspects were public or academic librarians, and social media. The most popular roles for the elements were conversations about teaching, sharing ideas, tips for teaching, insights into teaching, suggestions for ways of teaching, and how to engage students. Librarians' weakly influenced faculty in their teaching methods and their content taught. The motivating factors for collaboration with librarians were that students learned how to research, students' research projects improved, faculty saved time by having librarians provide the instruction to students, and faculty built strong working relationships with librarians. The challenges of collaborating with librarians were inadequate teaching techniques used when librarians taught research orientations and lack of time. Ways librarians can be more integral in faculty's teaching social networks included: more workshops for faculty, more proactive interaction with faculty, and more one-on-one training sessions for faculty. Some of the recommendations for the librarians from this study were develop a strong rapport with faculty, librarians should build their services in information literacy from the point of view of the faculty instead of from the librarian perspective, use staff development funding to attend conferences and workshops to improve their teaching, develop more training sessions for faculty, increase marketing efforts of the librarian's instructional services, and seek grant opportunities to increase funding for the library. In addition, librarians and faculty should review the definitions of information literacy and move from a skills based interpretation to a learning process.

Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

[EN]This work analyzes the problem of community structure in real-world networks based on the synchronization of nonidentical coupled chaotic Rössler oscillators each one characterized by a defined natural frequency, and coupled according to a predefined network topology. The interaction scheme contemplates an uniformly increasing coupling force to simulate a society in which the association between the agents grows in time. To enhance the stability of the correlated states that could emerge from the synchronization process, we propose a parameterless mechanism that adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. We show that the characteristic frequency vector that results from the adaptation mechanism reveals the underlying community structure present in the network.

Evaluating the effectiveness of community-based dementia care networks:The Dementia Care Networks' Study

Purpose: The Dementia Care Networks' Study examined the effectiveness of four community-based, not-for-profit dementia networks. The study involved assessing the relationship between the types of administrative and service-delivery exchanges that occurred among the networked agencies and the network members' perception of the effectiveness of these exchanges. Design and Methods: With the use of a case-study method, the evolution, structure, and processes of each network were documented. Social network analysis using a standardized questionnaire completed by member agencies identified patterns of administrative and clinical exchanges among networked agencies. Results: Differences were found between the four networks in terms of their perceptions of service-delivery effectiveness; perceptions of administrative effectiveness did not factor significantly. Exchanges between groups of agencies (cliques) within each of the four networks were found to be more critical than those between individual agencies within each network. Implications: Integration-measured by the types of exchanges within as opposed to across networks-differentiated the four networks studied. This research contributes to our understanding of the use of multiple measures to evaluate the inner workings of service delivery and their impact on elder health and elder health care. Copyright 2005 by The Gerontological Society of America.

Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

‘Pre-plan mapping’, networks, capital resources and community strategies in England

In this working paper we discuss current attempts to engage communities in planning policy formulation in the UK. In particular we focus on the preparation of Community Strategies (CS) in England to inform local public policy and the wider proposals recently published by the UK government to move towards enhanced community engagement in planning (DTLR, 2001). We discuss how such strategies could be operationalised with a conceptual framework developed following ideas derived from ANT (cf. Murdoch, 1997, 1998; Selman, 2000; Parker & Wragg, 1999; Callon, 1986, 1998) and the ‘capitals’ literature (Lin, 2002; Fine, 2001; Selman, 2000; Putnam, 1993). We see this as an expression of neo-pragmatic planning theory, (Hoch, 1996; Stein & Harper, 2000) to develop a form of ‘pre-plan mapping’.

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.

Community structure and dynamics in climate networks

We consider climate networks constructed from observed and model simulated fields of three climate variables and investigate their community structure. We find that for all fields the number of effective communities is rather small (four to five). We are able to trace the origin of these communities to certain dynamical properties of climate. Our results suggest that the complete complexity of the climate system condenses beyond the `weather` time scales into a small number of low-dimensional interacting components and provide clues as to the nature of the climate subsystems underlying these components.

A cold blizzard was raging, but the light shone brightly' : social networks presented in stained glass within an industrial community

Historical stained glass in Calumet and Laurium revealed the complex structures of these industrial communities. Creating an Industrial Archaeology-focused approach, I examined stained glass as material culture. Sacred glass revealed ethnic and religious values of a congregation through the style, iconography, and quality of the glasswork. Residential glass showed how owners represented themselves within cultural settings by meeting social expectations. Commercial glass indicated community status of owners through discreet and artistic shows of wealth and taste. Corporate glass displayed prosperity and belonging through the superior quality and cost of the glasswork. Viewing stained glass as material culture opened new methods of looking at both stained glass and industrial communities. Findings from my research can teach the public about the importance of preserving and conserving stained glass, and that can lead to greater public appreciation for the material culture found within these industrial communities.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

Material matters for learning in virtual networks : A case study of a professional learning programme hosted in a Google+ online community

Date of Acceptance: 13/07/2015