Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Scope, Strategy and Structure: The Dynamics of Knowledge Networks in Medicine

The objective of this paper is to analyse the dynamics of networks in which new knowledge emerges and through which it is exchanged. Our conjecture is that the structure of a network cannot be divorced from the dynamics of the knowledge underpinning its activities. In so doing we look beyond studies based on the assumption of exogenous networks and delve into the mechanisms that stimulate their creation and transformation. In the first part the paper adopts a functional perspective and views networks as constructs aimed at the coordination of knowledge; accordingly, network structure is an emerging property that reflects the employment of an agreed strategy to achieve a collective scope. In the second part these themes are articulated in relation to the dynamics of medical innovation and enriched by an empirical study on the long-term evolution of medical research in Ophthalmology. This exercise highlights the connection between changes in scientific and practical knowledge and the reconfigurations of the epistemic network over a forty-year period. By mapping different network structures we capture variety in the gateways of knowledge creation – that is, the network participants – as well as in the pathways – that is, the inter-organisational collaborations. Our goal is to analyse how these patterns of interaction emerge and transform over time.Innovation, Network analysis, Inter-organizational Relationships

Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks. Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv admin note: text overlap with arXiv:1810.1176

Multiple centrality assessment in Parma : a network analysis of paths and open spaces

One of the largest of Europe, the recently realized university campus 'Area of the Sciences' in Parma, northern Italy, has been planned for a comprehensive programme of renovation and revitalization with a special focus on vehicular accessibility and the quality of open spaces. As part of the problem setting phase, the authors, with Rivi Engineering, applied Multiple Centrality Assessment (MCA) - a process of network analysis based on primal graphs, a set of different centrality indices and the metric computation of distances - in order to understand why the existent system of open spaces and pedestrian paths is so scarcely experienced by students as well as faculty and staff members and why it appears so poorly supportive of social life and human exchange. In the problem-solving phase MCA was also applied, turning out to offer a relevant contribution to the comparative evaluation of two alternative proposed scenarios, leading to the identification of one final solution of urban design. In the present paper, the first professional application of MCA, an innovative approach to the network analysis of geographic complex systems, is presented and its relevance in the context of a problem of urban design illustrated

Information Flow and Influence during Collective Search, Discussion, and Choice

If decision-relevant information is distributed among team members, the group is inclined to focus on shared information and to neglect unshared information, resulting often in suboptimal decisions. This classical finding is robust in experimental settings, in which the distribution of information is created artificially by an experimenter. The current paper looks at information sharing effects when access to information is not restricted, and decision makers are very familiar with the decision task. We analyzed archival search and discussion data obtained from business executives completing a personnel selection exercise. Information popularity in the population from which groups were composed predicted number of group members accessing items during information searches and whether the group discussed the items. The number of group members who accessed an item predicted whether information was repeated during discussion, and repetition predicted which items were included on an executive summary. Moreover, cognitively central group members were more influential than cognitively peripheral members. One implication is that collective decision making amplifies what is commonly known at the expense of disseminating what is not.Information Sharing, Cognitive Centrality, Group Decision Making, Collective Choice, Archival Data

Managing Knowledge in a Distributed Decision Making Context

This paper considers the role of electronic communication in the creation and distribution of knowledge, and in particular, the creation and sharing of personalised knowledge. Personalised knowledge or "intellectual capital" is perhaps a least understood but most important asset of modern organisations. This paper reveals the creation and sharing of personalised knowledge in a network organisation. The network organisation investigated in this paper relies on electronic communication in a distributed decision making context to leverage the skills and intellect of its key professionals. This paper investigates electronic group meetings that take place on this electronic social space to analyse key processes of knowledge creation. Implications for managing distributed personalised knowledge are discussed and conclusions drawn with respect to the key decision support systems functionalities required for managing knowledge in situations where decision making is distributed and takes place on an electronic social space.Personalised Knowledge;c entrality;communication infrastructure;distributed decision support;electronic social space;prestige