In this volume, Robert J. Sternberg and David D. Preiss bring together different perspectives on understanding the impact of various technologies on human abilities, competencies, and expertise. The inclusive range of historical, comparative, sociocultural, cognitive, educational, industrial/organizational, and human factors approaches will stimulate international multi-disciplinary discussion. Major questions that are addressed include:
*What is the impact of different technologies on human abilities?
*How does technology enhance or limit human intellectual functioning?
*What is the cognitive impact of complex technologies?
*What is the cognitive impact of the transfer of technologies?
*How can we design technologies that foster intellectual growth?
*How does technology mediate the impact of cultural variables on human intellectual functioning?
Part I addresses the history of cognitive technologies and how they have evolved with culture, but at the same time helped culture evolve. Part II focuses on how educational technologies affect the ways in which students and others think. The topic of Part III is technology in the world of work. Part IV deals with the interface between intelligence and technology. The diversity and richness of technology relates to different forms of abilities, competencies, and expertise. In consequence, many psychologists, educators, and others are interested in exploring the ways in which technology and human abilities interact, but lack a handy source of information to satisfy their interest. This book provides researchers and students in these areas with relevant perspectives and information.
The present study investigates on the learning impact of utilizing Wikipedia’s community in education. Today, many instructors assign their students editing Wikipedia’s articles as part of their coursework. Participation in a cyber-community during an educational assignment exposes students to a brand new culture and netiquette, to a set of explicit and tacit rules and cultural norms. This requires students to internalize the embedded online culture in order to join the community — a form of acculturation which may cause stress, but which can lead to opportunities for growth, learning and development. By taking advantage of a virtual community, educators can literally bring a whole thriving community into their classrooms. The acculturation of the educational group into the culture of a hosting virtual community, through collaborative actions, conflicts and disturbances, results to the formation of a collective zone of proximal development: what the students’ group manages to perform today with the aid of the community’s members will be performed independently tomorrow. The formation of a virtual learning community through the procedural and structural coupling of two discrete activity systems opens a new space for participatory learning.
Posted in Acculturation, Culture, Education, Learning, Virtual community, Wikipedia
Tagged Acculturation, culture, education, learning, Virtual community, wikipedia
Now, with the advent of personal assistants like Siri and Google Now that aim to serve up information before you even know you need it, you don’t even need to type the questions. Just say the words and you’ll have your answer. But with so much information easily available, does it make us smarter? Compared to the generations before who had to adapt to the Internet, how are those who grew up using the Internet — the so-called “Google generation” — different? Heick had intended for his students to take a moment to think, figure out what type of information they needed, how to evaluate the data and how to reconcile conflicting viewpoints. He did not intend for them to immediately Google the question, word by word — eliminating the process of critical thinking.
Read also: How Google impacts the way Students Think
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