Do teachers really know what students go through? To find out, one teacher followed two students for two days and was amazed at what she found. Her report is in following post, which appeared on the blog of Grant Wiggins, the co-author of “Understanding by Design” and the author of “Educative Assessment” and numerous articles on education. A high school teacher for 14 years, he is now the president of Authentic Education, in Hopewell, New Jersey, which provides professional development and other services to schools aimed at improving student learning. You can read more about him and his work at the AE site.
Jules Darmanin 19/03/15 : "Dans un billet de son blog officiel, la société a annoncé à l'ensemble des développeurs une nouvelle fonctionnalité de son API, l'interface qui permet à Uber de dialoguer avec les autres applications. Comme vous pouvez demander à vos amis Facebook des vies depuis Candy Crush, vous pourrez commander un trajet Uber depuis une autre application...
For years scientists searched fruitlessly for the causes of autism by looking for genes shared by families prone to the disorder. Now researchers taking a new approach have begun to unlock its secrets.
The society has a multi-layered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of inter-layer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multi-layer WSN model, where the indirect inter-layer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved. Furthermore, the network of social interactions can be considered as a multiplex from another point of view too: each layer corresponds to one communication channel and the aggregate of all them constitutes the entire social network. However, usually one has information only about one of the channels, which should be considered as a sample of the whole. Here we show by simulations and analytical methods that this sampling may lead to bias. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get with reasonable assumptions about the sampling process a monotonously decreasing distribution as observed in empirical studies of single channel data. We analyse the far-reaching consequences of our findings.
Multiplex Modeling of the Society
Janos Kertesz, Janos Torok, Yohsuke Murase, Hang-Hyun Jo, Kimmo Kaski
"In his introduction to this useful site, psychology professor Jon Mueller presents two arguments. First, he claims that our current educational system fails to teach the need for critical skills for success in the 21st century. Second, he posits that the reason for this is, at least partially, due to confusion about how to assess those skills. The website is an answer to that confusion. Here educators will find Mueller's program of Authentic Assessment. Readers may like to begin with the first three sections of the site, "What is it?," "Why do it?," and "How do you do it," before moving onto sections that explain such topics as Standards, Rubrics, and Tasks. Throughout the site, Mueller makes a genuine attempt to provide both a philosophical backdrop for his assessment techniques and the sorts of down-to-earth tools that educators can easily use in their classrooms."
Olivier Ezrattty 08/02/15 : "En à peine deux décennies, le numérique a transformé tout un tas d’industries, parfois violemment. Il a entrainé de nombreuses migrations de valeur, souvent au profit de grandes sociétés américaines avec en tête les fameux GAFA (Google, Apple, Facebook, Amazon). La plupart de ces migrations de valeur sont liées à des mécanismes d’intermédiation et à Internet, comme avec Amazon dans le commerce, Google dans la publicité, Netflix dans la vidéo et plus récemment Uber face aux taxis. On utilise maintenant le barbarisme de l’Uberisation pour décrire une migration de valeur liée à l’intermédiation de services...
Over the last two decades, computers have become omnipresent in daily life. Their increased power and accessibility have enabled the accumulation, organization, and analysis of massive amounts of data. These data, in turn, have been transformed into practical knowledge that can be applied to simple and complex decision making alike. In many of today's activities, decision making is no longer an exclusively human endeavor. In both virtual and real ways, technology has vastly extended people's range of movement, speed and access to massive amounts of data. Consequently, the scope of complex decisions that human beings are capable of making has greatly expanded. At the same time, some of these technologies have also complicated the decision making process. The potential for changes to complex decision making is particularly significant now, as advances in software, memory storage and access to large amounts of multimodal data have dramatically increased. Increasingly, our decision making process integrates input from human judgment, computing results and assistance, and networks. Human beings do not have the ability to analyze the vast quantities of computer-generated or -mediated data that are now available. How might humans and computers team up to turn data into reliable (and when necessary, speedy) decisions? Complex Operational Decision Making in Networked Systems of Humans and Machines explores the possibilities for better decision making through collaboration between humans and computers. This study is situated around the essence of decision making; the vast amounts of data that have become available as the basis for complex decision making; and the nature of collaboration that is possible between humans and machines in the process of making complex decisions. This report discusses the research goals and relevant milestones in several enabling subfields as they relate to enhanced human-machine collaboration for complex decision making; the relevant impediments and systems-integration challenges that are preventing technological breakthroughs in these subfields; and a sense of the research that is occurring in university, government and industrial labs outside of the United States, and the implications of this research for U.S. policy. The development of human-machine collaboration for complex decision making is still in its infancy relative to where cross-disciplinary research could take it over the next generation. Complex Operational Decision Making explores challenges to progress, impediments to achieving technological breakthroughs, opportunities, and key research goals.
Luciano Lampi's insight:
what decision making has in common with divinatory arts?
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