A participatory culture is a culture with relatively low barriers to artistic expression and civic engagement, strong support for creating and sharing creations, and some type of informal mentorship whereby experienced participants pass along knowledge to novices. In a participatory culture, members also believe their contributions matter and feel some degree of social connection with one another (at the least, members care about others’ opinions of what they have created). A growing body of scholarship suggests potential benefits from these forms of participatory culture, including opportunities for peer-to-peer learning, a changed attitude toward intellectual property, the diversification of cultural expression, the development of skills valued in the modern workplace, and a more empowered conception of citizenship. Access to this participatory culture functions as a new form of the hidden curriculum, shaping which youths will succeed and which will be left behind as they enter school and the workplace.
Schools as institutions have been slow to react to the emergence of this new participatory culture; the greatest opportunity for change is currently found in after-school programs and informal learning communities. Schools and after-school programs must devote more attention to fostering what we call the new media literacies: a set of cultural competencies and social skills that young people need in the new media landscape. Participatory culture shifts the focus of literacy from individual expression to community involvement. The new literacies almost all involve social skills developed through collaboration and networking. These skills build on the foundation of traditional literacy and research, technical, and critical-analysis skills learned in the classroom.
Designing courses is passé! In a world where the shelf-life of knowledge and skills are rapidly shrinking, where best practices of yore yield increasingly little or no return on investment, where exceptions are the norm, and constant change and flux the new normal, designing set courses using SME-defined content is like trying to build a dam to rein in the surging waves of a tumultuous ocean. We have to think agile, instant, accessible, contextual, micro-sized, real time… We need to uberize organizational learning.
“Uberization” has taken off as the new term that according to me has come to stand for – disruption, innovation, lean operating model, harnessing of the affordances of the sharing economy, and a hyper-connected world driven by imagination and creativity where everything is a mobile-click away – including learning.
In summary, the world of L&D has dramatically changed. Just as the rules of business and leadership have changed in the networked era, so has the rules for how to enable employees to deliver with efficacy. The L&D department can no longer sit in an isolated bubble designing courses for skills that are fast becoming redundant. It is time to build an entirely new set of skills in oneself as well as in the workforce.
There are several separate factors at work here. The first is the continuing development of new knowledge, making it difficult to compress all that learners need to know within the limited time span of a post-secondary course or program. This means helping learners to manage knowledge – how to find, analyze, evaluate, and apply knowledge as it constantly shifts and grows.
The second factor is the increased emphasis on skills or applying knowledge to meet the demands of 21st century society, skills such as critical thinking, independent learning, knowing how to use relevant information technology, software, and data within a field of discipline, and entrepreneurialism. The development of such skills requires active learning in rich and complex environments, with plenty of opportunities to develop, apply and practice such skills.
Lastly, it means developing students with the skills to manage their own learning throughout life, so they can continue to learn after graduation.
Computers are uniquely qualified to handle massive data sets since they can simultaneously keep track of all the important conditions necessary for the analysis. Though they could reflect human errors they’re programmed with, computers can deal with large amounts of data efficiently and they aren’t biased toward the familiar, as human investigators might be. Computers can also be taught to look for specific patterns in experimental data sets – a concept termed machine learning, first proposed in the 1950s, most notably by mathematician Alan Turing. An algorithm that has learned the patterns from data sets can then be asked to make predictions based on new data it’s never encountered before. Machine learning has revolutionized biological research since we can now utilize big data sets and ask computers to help understand the underlying biology.
The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the W3 also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size.
Location-based social networks are allowing scientists to study the way human patterns of behavior change in time and space, a technique that should eventually lead to deeper insights into the nature of society.
The increasing availability of big data from mobile phones and location-based apps has triggered a revolution in the understanding of human mobility patterns. This data shows the ebb and flow of the daily commute in and out of cities, the pattern of travel around the world and even how disease can spread through cities via their transport systems. So there is considerable interest in looking more closely at human mobility patterns to see just how well it can be predicted and how these predictions might be used in everything from disease control and city planning to traffic forecasting and location-based advertising.
Read also: Indigenization of Urban Mobility
A review of the history and current state of distance, blended, and online learning. The articles presented in this report provide an overview of research literature in:
• Distance education
• Blended learning
• Online learning
• MOOC research
• Future learning technology infrastructures
Higher education is changing. central to this change is the transition from a physically based learning model to one that makes greater use of digital technologies. A brave, new landscape of toolsets is now emerging, each with various elements of control, integration, ownership, and structure. As leaders, educators, and students begin selecting tools for enterprise deployment, questions of control and ownership become as important as questions of integration and structure. More importantly, the technologies selected will determine the quality of learning, the scope of teaching practices, and ultimately, how well learners are equipped for both employment and engagement in democratic and equitable models of modern global society.
The Massive Open Online Course (MOOC) movement is the latest ‘big thing’ in Open and Distance Learning (ODL) which threatens to transform Higher Education. Both opportunities and threats are extensively discussed in literature, comprising issues on opening up education for the whole world, pedagogy and online versus campus education. Most of the literature focus on the origin of the MOOC movement in the US. The specific context of Europe with on the one hand autonomous countries and educational systems and on the other hand cross-border cooperation and regulations through the European Union differs from the US context. This specific context can influence the way in which the MOOC movement affect education in Europe, both reusing MOOCs from other continents (US) as publishing MOOCs, on a European platform or outside of Europe. In the context of the EU funded HOME project, a research was conducted to identify opportunities and threats of the MOOC movement on the European institutions of higher education. Three sources of data were gathered and analysed. Opportunities and threats were categorized in two levels. The macro level comprises issues related to the higher education system, European context, historical period and institutional level. The micro level covers aspects related to faculty, professors and courses, thus to the operational level. The main opportunities mentioned were the ECTS system as being a sound base for formal recognition of accomplishments in MOOCs, the tendency to cooperate between institutions, stimulated by EU funded programs and the many innovative pedagogical models used in MOOCs published in Europe. The main threats mentioned were a lacking implementation of the ECTS system, hindering bridging non/formal and formal education and too much regulation, hindering experimenting and innovation.
Book edited by Javiera Atenas and Leo Havemann: “… is the outcome of a collective effort that has its origins in the 5th Open Knowledge Open Education Working Group call, in which the idea of using Open Data in schools was mentioned. It occurred to us that Open Data and open educational resources seemed to us almost to exist in separate open worlds. We decided to seek out evidence in the use of open data as OER, initially by conducting a bibliographical search. As we could not find published evidence, we decided to ask educators if they were in fact, using open data in this way, and wrote a post for this blog (with Ernesto Priego) explaining our perspective, called The 21st Century’s Raw Material: Using Open Data as Open Educational Resources. We ended the post with a link to an exploratory survey, the results of which indicated a need for more awareness of the existence and potential value of Open Data amongst educators…..