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Scoop.it!
Free resource of educational web tools, 21st century skills, tips and tutorials on how teachers and students integrate technology into education Via Tom D'Amico (@TDOttawa) , michel verstrepen 
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Many elementary school teachers love to teach reading and writing, but are less comfortable with science and math. It’s not a hard and fast truth, of course, but learning to read is a big focus of the early school years, so it makes sense that teachers who gravitate toward elementary school like teaching literacy. But it’s also important to expose kids to science early and get them excited about the practices that define scientific inquiry. And literature may be the perfect starting point. Stories are full of tension, conflict and dilemmas that make wonderful departure points for engineering projects that weave subjects together. Via John Evans
Omar Elizondo's curator insight,
May 16, 2019 8:10 AM
Many elementary school teachers love to teach reading and writing, but are less comfortable with science and math. It’s not a hard and fast truth, of course, but learning to read is a big focus of the early school years, so it makes sense that teachers who gravitate toward elementary school like teaching literacy. But it’s also important to expose kids to science early and get them excited about the practices that define scientific inquiry.
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"This spring, at St. Anne’s-Belfield School in Charlottesville, Virginia, the fifth-grade Spanish class programmed computers to produce bilingual, animated photo albums. The seventh-grade science class rejiggered the code behind climate models ..." Via Leona Ungerer
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Interview with Doug Kim chairs the Intellectual Property Practice Group at McNair Law Firm Doug Kim is a frequent lecturer on patents, trademarks, copyrights, and licensing, and will be speaking at POSSCON on Tuesday, April 14th. The title of his presentation is, The Law and Open Source: What You Must Know. ... Via Gonzalo San Gil, PhD.
Gonzalo San Gil, PhD.'s curator insight,
March 31, 2015 4:12 AM
# ! Wonder if Queen Anne did expect all this mess
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From
tech
"Since they first appeared in science fiction decades ago, engineers have long played with the idea of holograms. The technology required has only been ..." Via Leona Ungerer
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"One of the Internet's most popular science stars explains why kids watch his lessons for entertainment ..." Via Leona Ungerer
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Interview with Ross Mounce University of Bath Via Gonzalo San Gil, PhD.
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Please take full advantage of this How-to Guide for software freedom, digital rights, and free culture activism! Via Gonzalo San Gil, PhD.
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"Throughout middle school and high school conducting lab experiments was my favorite part of every science class that I took. There was something about the hands-on aspect of science labs that always got me excited about learning." Via Beth Dichter, MonVall
Beth Dichter's curator insight,
April 29, 2014 9:50 PM
Richard Byrne provides a number of links to websites that have great hands-on activities for students to learn science. The sites include: * Science is Fun - 25 chemistry experiments geared to students in grades 4 - 9 * Museum of Science and Industry in Chicago has activities in twelve topics. You will find experiments for students preK through grade 12. * Discover the World is from NOAA. A total of 43 experiments which are probably best for grades 4 - 8. * Squishy Circuits. Learn how to create the "dough" to create these circuits and watch a TedEd to learn more. There are many ideas to be found in this post and lots of fun for your students to experience while they explore and learn science!
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Teachers are encouraged to use our science education resources to maximize the learning potential of the Smithsonian Science How? webcasts! This is a new and unique resource that taps the learning power of our curriculum while aligning with standards. Via Tom D'Amico (@TDOttawa)
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Some advances are genuinely exciting, but whether they will really produce human-level A.I. is unclear. Via Szabolcs Kósa, luiy
luiy's curator insight,
February 26, 2014 6:23 AM
..... but, examined carefully, the articles seem more enthusiastic than substantive. As I wrote before, the story about Watson was off the mark factually. The deep-learning piece had problems, too. Sunday’s story is confused at best; there is nothing new in teaching computers to learn from their mistakes. Instead, the article seems to be about building computer chips that use “brainlike” algorithms, but the algorithms themselves aren’t new, either. As the author notes in passing, “the new computing approach” is “already in use by some large technology companies.” Mostly, the article seems to be about neuromorphic processors—computer processors that are organized to be somewhat brainlike—though, as the piece points out, they have been around since the nineteen-eighties. In fact, the core idea of Sunday’s article—nets based “on large groups of neuron-like elements … that learn from experience”—goes back over fifty years, to the well-known Perceptron, built by Frank Rosenblatt in 1957. (If you check the archives, the Times billed it as a revolution, with the headline “NEW NAVY DEVICE LEARNS BY DOING.” The New Yorker similarly gushed about the advancement.) The only new thing mentioned is a computer chip, as yet unproven but scheduled to be released this year, along with the claim that it can “potentially [make] the term ‘computer crash’ obsolete.” Steven Pinker wrote me an e-mail after reading the Timesstory, saying “We’re back in 1985!”—the last time there was huge hype in the mainstream media about neural networks.
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Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence. Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain — a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats1. Google Brain's discovery that the Internet is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language.
Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.
Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins. Via Szabolcs Kósa, Dr. Stefan Gruenwald
R Schumacher & Associates LLC's curator insight,
January 15, 2014 1:43 PM
The monikers such as "deep learning" may be new, but Artificial Intelligence has always been the Holy Grail of computer science. The applications are many, and the path is becoming less of an uphill climb.
luiy's curator insight,
February 26, 2014 6:19 AM
Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.
Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins.
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Company cultural anthropologist Genevieve Bell tells us to get ready to take our fondness for smartphones, tablets and other devices to the next level Via Sharrock, Lynnette Van Dyke
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Here’s how Skype A Scientist describes itself: Skype a Scientist matches scientists with classrooms around the world! Scientists will skype into the classroom for 30-60 minute Q and A … Via Tom D'Amico (@TDOttawa) , Jim Lerman, John Evans
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| Sean Michael Kerner | The idea of being open has proven itself before in human history, claims Cory Doctorow in a LinuxCon keynote. Via Gonzalo San Gil, PhD.
Gonzalo San Gil, PhD.'s curator insight,
September 6, 2016 6:11 AM
# ! ... of #sharing, from #discoveries to #creation,
# ! for #advancing in #Mankind's #welfare.
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Quand Charles Darwin rencontre Larry Page Le premier arbre phylogénétique, tel qu'il apparaît en 1859 dans De l'origine des espèces au moyen de la sélection naturelle de Charles... "Dessine-moi un mouton", demandait le Petit Prince à l'aviateur. La représentation graphique a toujours été vecteur de connaissance, mais aujourd'hui, dans une culture qui abandonne peu à peu l'écrit pour l'image, elle devient une composante essentielle de la transmission du savoir. Un second défi se dresse : comment rendre compte de la masse d'informations disponibles pour décrire fidèlement la complexité du monde dans lequel nous évoluons, et notamment la diversité de la biosphère ? La conjugaison de ces deux ambitions a donné naissance au projet OneZoom (www.onezoom.org), un outil numérique qui permet de visualiser une version numérique de l'arbre de la vie. Une immersion dans la biodiversité, et une belle invitation à la curiosité. (...) - par Guillaume Frasca, La science infuse, 16/10/2012
Source : J. Rosindell et L.J. Harmon, OneZoom: A Fractal Explorer for the Tree of Life, PLoS Biology, 16 octobre 2012. Via Julien Hering, PhD, luiy, @backbook, Catherine Pascal
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Blog post at PragmaticMom : Via Dr. Susan Bainbridge
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Top free resources for teaching and learning Science (also check out open source curriculum) National Science Foundation (NSF) - is an independent federal agency created by Congress in 1950 "to pr... Via Dr. Susan Bainbridge
Willem Kuypers's curator insight,
October 26, 2014 3:34 AM
Des sites pour trouver des informations pour les cours de sciences. 
vicky carroll's comment,
May 12, 2017 2:09 AM
Fantastic resource for transforming learning by getting students to communicate/collaborate with external parties after researching topics. Would allow redefinition using the SAMR model.
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The main objective of #di2014 is to bring together researchers, practitioners and students from a large variety of fields and to provide them with the opportunity to share their visions and research achievements as well establish worldwide cooperative research and developpement.Areas of research include but are not limited to: Data, Social Web, Digital Humanities, Digital Identity, The commons, Digital Art, Smart Cities, Media and Digital Cultures, Human-Computer Interface, Digital Literature, Digital Literacy, Computational Thinking, Secutity, Safety and Privacy, e-Learning, Business intelligence.The mindset of #di2014 is unique in bringing these disciplines together in creative and critical dialogs. We broadly invite contributions that describe original research, analysis, practices, and works-in-progress in all areas of Digital Cultures.#di2014 will be organized jointly with Scopitone, the major French digital arts and music festival (Sept. 16-21, 2014). Via Jacques Urbanska
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From graphene to self-healing materials. Next-generation materials include super-light materials and active materials that react to changes in their environment and ultimately smart materials that explain how they are doing. Functional materials follow by borrowing ideas from biology to improve performance and add new behaviours. Self-assembling materials are about making large-scale products that are more precise, enabling better properties (strength, tear resistance, conductivity, etc.) Via Marc Kneepkens, Justin Jones
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"At its simplest, a flipped format can involve a teacher inviting students to view YouTube videos at home as a preview to the day's topic. At its most rigorous, a flipped curriculum involves teachers writing, producing, directing, editing, and posting their own original lessons -- complete with custom narrations and visualizations -- via third-party applications." Via Beth Dichter, Alfredo Calderón
Ness Crouch's curator insight,
March 29, 2014 5:13 PM
These motion infographics look interesting. I wonder if I can find content for my class? 
Jeongbae Kong Enanum's curator insight,
August 16, 2014 9:48 AM
Won Ho :<생각이 깊은 교수님의 글이라서 연구해봐야겠다.> Why should the video watching previous to in-class? The core is quality video access and intensive in-class interaction. The lecture can't complete with these superb ones. 꼭 뒤집어야만 하는가? 내게 플립러닝의 핵심은 수준 높은 비디오와 강력한 상호작용이다. 순서와 방식은 여러 가지가 가능하다. 선생님이 개념 설명 행위는 여기 비디오를 보면 조만간 사라질 게 당연해 보인다.
María Dolores Díaz Noguera's curator insight,
February 4, 2016 7:39 AM
Flipping The Flipped Classroom - Motion Infographics For STEM Learning | @scoopit via @BethDichter http://sco.lt/...
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Invisibility cloaks. The search for extraterrestrial intelligence. A Facebook for genes. These were just a few of the startling topics IFTF explored at our recent Technology Horizons Program conference on the "Future of Science." More than a dozen scientists from UC Berkeley, Stanford, UC Santa Cruz, Scripps Research Institute, SETI, and private industry shared their edgiest research driving transformations in science. MythBusters' Adam Savage weighed in on the future of science education. All of their presentations were signals supporting IFTF's new "Future of Science" forecast, laid out in a new map titled "A Multiverse of Exploration: The Future of Science 2021." The map focuses on six big stories of science that will play out over the next decade: Decrypting the Brain, Hacking Space, Massively Multiplayer Data, Sea the Future, Strange Matter, and Engineered Evolution. Those stories are emerging from a new ecology of science shifting toward openness, collaboration, reuse, and increased citizen engagement in scientific research. by Institute For The Future Via Szabolcs Kósa, luiy
luiy's curator insight,
February 26, 2014 6:28 AM
The map focuses on six big stories of science that we think will play out over the next decade:
1. Decrypting the Brain, 2. Hacking Space, 3. Massively Multiplayer Data, 4. Sea the Future, 5. Strange Matter, and 6. Engineered Evolution.
- See more at: http://www.iftf.org/our-work/people-technology/technology-horizons/the-future-of-science/#sthash.J6Ga3QHn.dpuf
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Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence. Via Szabolcs Kósa, luiy
R Schumacher & Associates LLC's curator insight,
January 15, 2014 1:43 PM
The monikers such as "deep learning" may be new, but Artificial Intelligence has always been the Holy Grail of computer science. The applications are many, and the path is becoming less of an uphill climb.
luiy's curator insight,
February 26, 2014 6:19 AM
Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.
Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins.
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A workshop on sustainable science and open access to scientific research.
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