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

A Collaborative Approach for Reducing Wildland Fire Risks to Communities and the Environment

The Secretaries of Agriculture and the Interior joined the Western Governors' Association, National Association of State Foresters, National Association of Counties, and the Intertribal Timber Council to endorse this 10-Year Comprehensive Strategy to deal with the wildland fire and hazardous fuels situation, as well as the needs for habitat restoration and rehabilitation in the Nation. Its goals were to improve fire prevention and suppression, reduce hazardous fuels, restore fire-adapted ecosystems, and promote community assistance. Educational levels: Graduate or professional

Technology Target Studies: Technology Solutions to Make Patient Care Safer and More Efficient

Presents findings on technologies that could enhance care delivery, including patient records and medication processes; features and functionality nurses require, including tracking, interoperability, and hand-held capability; and best practices

Towards a dynamic capabilities view on ecosystem formation: A case study on the emergence of an innovation ecosystem

Digitalization is a catalyser that drives rapid changes in industries. While bringing huge opportunities for business, digitalization outdated existing capabilities and working methods, thus, it brings threats to companies who cannot timely innovate themselves. In today’s business landscape, no company has sufficient resources to develop digital innovation alone. Companies have to be able to attract, secure and combine a variety of new resources and competencies from other organizations to co-create new services on top of its technology platform. Currently, we see that innovation ecosystems are emerging to answer to this need. Innovation ecosystems are inherently complex as they consist of multiple actors coming from different cultural, political, economical and knowledge backgrounds. Thus, developing innovation ecosystems can be very challenging. However, we have not been equipped with sufficient theoretical and practical knowledge to understand how a company can form an innovation ecosystem. Therefore, this thesis was set to establish a deeper understanding of the factors and capabilities that support the formation of an innovation ecosystem. Through an extensive literature review of both fields - ecosystem and dynamic capabilities, this thesis established the first theoretical model that explains the development of an innovation ecosystem. This theoretical model was applied and developed iteratively in an in-depth case study of a European-based Intelligent Mine innovation ecosystem. This thesis was conducted using an exploratoratory, qualitative approach and followed an abductive research design. Data was collected through several open-ended interviews with ecosystem members and analyzed following Gioia methodology. The results of this thesis shed light on: (1) the key factors that trigger the formation of an innovation ecosystem, (2) the motivations of a hub company for forming an innovation ecosystem, and (3) the sensing and seizing mechanisms that a hub company employed while forming its innovation ecosystem. Moreover, a conceptual model was developed after refining the initial theoretical with new empirical insights. This thesis contributes directly to the development of new theory on ecosystem formation and the new application of dynamic capabilities framework in ecosystem literature. It also provides useful suggestions for companies whose aspiration is to develop innovation ecosystems around their core technologies

Lower Mekong Portfolio: Interim Evaluation

This report summarizes a portfolio evaluation of the MacArthur Foundation's conservation investments in the Lower Mekong region since 2011. It is explicitly a portfolio-level evaluation, focusing on common themes rather than individual grants. The evaluation involved understanding the portfolio context through reviewing relevant documents and speaking with donor partners; gathering data from MacArthur grantees; calibrating initial evaluation findings through consultations with independent regional experts and donor partner grantees; improving future evaluation ability by cooperating with NatureServe to improve the Lower Mekong Dashboard; and presenting results in this evaluation report and to MacArthur directly

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Disease Surveillance Networks Initiative Global: Final Evaluation

In August 2009, the Rockefeller Foundation commissioned an independent external evaluation of the Disease Surveillance Networks (DSN) Initiative in Asia, Africa, and globally. This report covers the results of the global component of the summative and prospective1 evaluation, which had the following objectives:[1] Assessment of performance of the DSN Initiative, focused on its relevance, effectiveness/impact, and efficiency within the context of the Foundation's initiative support.[2] Assessment of the DSN Initiative's underlying hypothesis: robust trans-boundary, multi-sectoral/cross-disciplinary collaborative networks lead to improved disease surveillance and response.[3] Assessment of the quality of Foundation management (value for money) for the DSN Initiative.[4] Contribute to the field of philanthropy by:a. Demonstrating the use of evaluations in grantmaking, learning and knowledge management; andb. Informing the field of development evaluation about methods and models to measure complex networks