212,353 research outputs found
Reinforced communication and social navigation generate groups in model networks
To investigate the role of information flow in group formation, we introduce
a model of communication and social navigation. We let agents gather
information in an idealized network society, and demonstrate that heterogeneous
groups can evolve without presuming that individuals have different interests.
In our scenario, individuals' access to global information is constrained by
local communication with the nearest neighbors on a dynamic network. The result
is reinforced interests among like-minded agents in modular networks; the flow
of information works as a glue that keeps individuals together. The model
explains group formation in terms of limited information access and highlights
global broadcasting of information as a way to counterbalance this
fragmentation. To illustrate how the information constraints imposed by the
communication structure affects future development of real-world systems, we
extrapolate dynamics from the topology of four social networks.Comment: 7 pages, 3 figure
Designing for interaction
At present, the design of computer-supported group-based learning (CS)GBL) is often based on subjective decisions regarding tasks, pedagogy and technology, or concepts such as ‘cooperative learning’ and ‘collaborative learning’. Critical review reveals these concepts as insufficiently substantial to serve as a basis for (CS)GBL design. Furthermore, the relationship between outcome and group interaction is rarely specified a priori. Thus, there is a need for a more systematic approach to designing (CS)GBL that focuses on the elicitation of expected interaction processes. A framework for such a process-oriented methodology is proposed. Critical elements that affect interaction are identified: learning objectives, task-type, level of pre-structuring, group size and computer support. The proposed process-oriented method aims to stimulate designers to adopt a more systematic approach to (CS)GBL design according to the interaction expected, while paying attention to critical elements that affect interaction. This approach may bridge the gap between observed quality of interaction and learning outcomes and foster (CS)GBL design that focuses on the heart of the matter: interaction
The role of social networks in students’ learning experiences
The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experience in a university environment are examined
Temporal Networks
A great variety of systems in nature, society and technology -- from the web
of sexual contacts to the Internet, from the nervous system to power grids --
can be modeled as graphs of vertices coupled by edges. The network structure,
describing how the graph is wired, helps us understand, predict and optimize
the behavior of dynamical systems. In many cases, however, the edges are not
continuously active. As an example, in networks of communication via email,
text messages, or phone calls, edges represent sequences of instantaneous or
practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at
hospitals can be represented by a graph where an edge between two individuals
is on throughout the time they are at the same ward. Like network topology, the
temporal structure of edge activations can affect dynamics of systems
interacting through the network, from disease contagion on the network of
patients to information diffusion over an e-mail network. In this review, we
present the emergent field of temporal networks, and discuss methods for
analyzing topological and temporal structure and models for elucidating their
relation to the behavior of dynamical systems. In the light of traditional
network theory, one can see this framework as moving the information of when
things happen from the dynamical system on the network, to the network itself.
Since fundamental properties, such as the transitivity of edges, do not
necessarily hold in temporal networks, many of these methods need to be quite
different from those for static networks
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