Factors That Influence Healthcare Professionals’ Online Interaction in a Virtual Community of Practice

Online technologies have facilitated the development of Virtual Communities of Practice (virtual CoPs) to support health professionals collaborate online to share knowledge, improve performance and support the spread of innovation and best practices. Research, however, shows that many virtual CoPs do not achieve their expected potential because online interaction among healthcare professionals is generally low. Focusing on health visitors, who are UK qualified midwives or nurses who have undertaken additional qualifications as specialist public health workers in the community, the paper examines the factors that influence online interaction among health visitors collaborating to share knowledge and experience in a virtual CoP. The paper makes suggestions for how to improve online interaction among health professionals in virtual CoPs by increasing the size of membership in order to take advantage of both posting and viewing contributions, facilitating moderation to improve networking among geographically dispersed members groups and improving the topic relevance in order to stimulate contributions

Discourse-centric learning analytics

Drawing on sociocultural discourse analysis and argumentation theory, we motivate a focus on learners' discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. However, software platforms must gain access to qualitative information about the rhetorical dimensions to discourse contributions to enable such analytics. This is difficult to extract from naturally occurring text, but the emergence of more-structured annotation and deliberation platforms for learning makes such information available. Using the Cohere web application as a research vehicle, we present examples of analytics at the level of individual learners and groups, showing conceptual and social network patterns, which we propose as indicators of meaningful learning

Infrapatellar Fat Pad Stem Cells Responsiveness to Microenvironment in Osteoarthritis: From Morphology to Function

Recently, infrapatellar fat pad (IFP) has been considered as a source of stem cells for cartilage regeneration in osteoarthritis (OA) due to their ability for differentiation into chondrocytes. However, stressful conditions, like that related to OA, may induce a pathogenic reprograming. The aim of this study was to characterize the structural and functional properties of a new population of stem cells isolated from osteoarthritic infrapatellar fat pad (OA-IFP). Nine OA patients undergoing total knee arthroplasty (TKA) were enrolled in this study [median age = 74 years, interquartile range (IQR) = 78.25-67.7; median body mass index = 29.4 Kg/m2, IQR = 31.7-27.4]. OA-IFP stem cells were isolated and characterized for morphology, stemness, metabolic profile and multi-differentiative potential by transmission electron microscopy, flow cytometric analysis, gene expression study and cytochemistry. OA-IFP stem cells displayed a spindle-like morphology, self-renewal potential and responsiveness (CD44, CD105, VEGFR2, FGFR2, IL1R, and IL6R) to microenvironmental stimuli. Characterized by high grade of stemness (STAT3, NOTCH1, c-Myc, OCT-4, KLF4, and NANOG), the cells showed peculiar immunophenotypic properties (CD73+/CD39+/CD90+/CD105+/CD44\u2013/+/CD45\u2013). The expression of HLA-DR, CD34, Fas and FasL was indicative of a possible phenotypic reprograming induced by inflammation. Moreover, the response to mechanical stimuli together with high expression level of COL1A1 gene, suggested their possible protective response against in vivo mechanical overloading. Conversely, the low expression of CD38/NADase was indicative of their inability to counteract NAD+-mediated OA inflammation. Based on the ultrastructural, immunophenotypic and functional characterization, OA-IFP stem cells were hypothesized to be primed by the pathological environment and to exert incomplete protective activity from OA inflammation

Collective intelligence for promoting changes in behaviour: a case study on energy conservation

Climate change is one of the biggest challenges humanity faces today. Despite of high investments in technology, battling climate change is futile without the participation of the public, and changing their perception and habits. Collective intelligence tools can play an important role in translating this “distant” concept that is climate change into practical hints for everyday life. In this paper, we report a case study grounded on collective intelligence tools to collaboratively build knowledge around energy conservation. A preliminary study to raise energy awareness in an academic environment is summarised, setting the scene to a more ambitious initiative based on personal stories to transform energy awareness into behaviour change. The role of the collective intelligence tools and other technical artefacts involved are discussed, suggesting strategies and features that contributed (or not) to users’ engagement and collective awareness. Lessons learned from both studies are reported with a sociotechnical approach as implications for design pursuing behaviour change

Contested Collective Intelligence: rationale, technologies, and a human-machine annotation study

We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework

The FuturICT education accelerator

Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT

The FuturICT education accelerator

Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT

Towards a global participatory platform Democratising open data, complexity science and collective intelligence

The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of self-organization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed