Teachers who work at the poorest schools are more likely to think that computer science is vital to their students’ futures, but are less likely to think their school boards agree, a new survey released Tuesday reveals.
The survey was conducted by Gallup on behalf of Google, and looks at perceptions of computer science for different groups, including students, parents, educators and school district administrators. It follows an earlier survey released in August, which looked at access to computer science courses and found that lower-income students have fewer opportunities to study the subject. However, this latest survey shows that low-income students' lack of access is not due to apathy on the part of their educators.
Twenty-one percent of teachers who work at schools where more than half of the student body qualifies for free or reduced-price lunch said they thought access to computer science is more important to a student’s future success than other elective courses, like music or art. Only 10 percent of teachers who work at schools where 25 percent or fewer students qualified for free or reduced-price lunch said the same thing.
Much like a good song, good code is all about how the individual pieces fit together.
“Some of the best musicians I know are also engineers,” (coder and musician Richard) Plom says, pointing to various coders among the vast ranks at Apple. The two pastimes, you see, aren’t as different as they might seem. “Good code—when it’s written the right way—sings,” Plom explains. “It’s like constructing a song.”
You get a glimpse of that watching a Vine video with a perfect loop. It’s music, driven by code. And in a way, itresembles code, which often include loops. But at the same time, to use Plom’s term, these Vine videos “sing”—in multiple ways. And reaching that point requires a quality found in coders and musicians and coder-musicians. As Plom describes it: “It’s a way of thinking.”
Since the mid-90s, the hermetic and anonymous artist behind the name Dextro has been quietly creating some of the most pioneering algorithmic and generative art. Though his identity is a mystery, he is known for converging various styles, with work resembling luminous waves in one piece, and glitchy patterns in the next.
“[The work] was interactive within a pre-defined frame of possible behaviors,” he said. “Now I like 'algorithmic' more than 'generative' or 'code-generated' [because it] hints at functions, relationships, dynamics, and not just information and rules.”
For his video work, once Dextro “captures” the initial idea, he begins experimenting with code, changing numbers to see what happens. Changes can be surprising if the script is unstable.
Dextro explained that, although numerical values change linearly, the visual results are far from linear. What's of interest for Dextro is the ability to define a mathematical situation that “holds in it many possibilities, which come to surface through numerical variations in some part of its initial setting."
It requires only a fraction of a second to take a picture on an iPhone and share it with someone halfway around the world. An alternative photography process from the 19th century involves dozens of steps and can take an entire day to get right. So why do it? Located in the historic so-called "Photo District" of NYC, The Penumbra Foundation offers educational opportunities where inquisitive artists and students can learn a variety of alternative processes, test them out, and decide for themselves.
New legislation makes computer science an official part of STEM education, as more Americans say STEM is critical to the nation's future.
Three out of four Americans in a recent survey said they think “science is cool in a way that it wasn’t 10 years ago.”
Seventy-three percent of participants in the Finger on the Pulse opinion survey, from Horizon Media’s WHY Group, agreed with the statement that “in the future, all the best jobs will require knowledge of computer coding languages.”
Eighty-six percent of those surveyed said they believe knowing how to use a computer is equally important as knowing how to read and write.
How do students and teachers learn math and computer science, and how can we ease the coming shortage of computer science teachers? Worcester Polytechnic Institute will partner with Brown University and Bootstrap to examine those questions.
A team of computing education experts will study how students—and teachers—learn mathematics and computer science, and how those ways of learning can influence each other.
Describing algebra as “the gatekeeper for most STEM jobs,” Fisler and colleagues are excited to further explore the connection between math and computer science. Implementing computer science courses in the schools poses a challenge, as few middle schools and high schools have computer science teachers on staff, and few states offer teacher certification in the area.
After a decade of work, the Blue Brain Project of the École Polytechnique Fédérale de Lausanne claims, in a paper published in Cell, that it has created 31,000 virtual neurons comprised of 207 individual neuron subtypes. While the entire rat brain is estimated to have some 21 million neurons, even this tiny portion of the organ has scientists agog with the new realm of possibilities this latest discovery unlocks.
The ultimate goal of the $1 billion endeavor is not only to construct a whole rat brain in a computer, but a human brain as well. his project is separate from efforts to upload a consciousness to the cloud — rather, the hope here, as the New York Times explains, is for researchers to be able to “digitally encode some characteristics of neurons and their connections that are common to all brains.”
Towards the goal of creating a more robust system of unsupervised learning, a team at Loughborough University in the UK has been perfecting an artificial intelligence model based on “memory Foam.” The name hints at the nature of the model itself. Memory foam, which has become a popular component of mattresses, can take on an infinite variety of curvatures depending on the impression left on it by the person. In a similar vein, a computer employing the memory-foam approach learns to recognize stimuli by gaining an overall impression of sensory stimuli left upon it. Many believe this method more closely resembles the actual working of the human brain rather than algorithms used in supervised machine learning.
Computational Thinking is considered a universal competence, which should be added to every child’s analytical ability as a vital ingredient of their school learning. In this article we further elaborate on what Computational Thinking is and present examples of what needs to be taught and how. First we position Computational Thinking in Papert’s work with LOGO. We then discuss challenges in defining Computational Thinking and discuss the core and peripheral aspects of a definition. After that we offer examples of how Computational Thinking can be addressed in both formal and informal educational settings. In the conclusion and discussion section an agenda for research and practice is presented.
Secret Coders is a project that’s been on my mind for a long, long time. I’m a cartoonist. I write and draw comic books and graphic novels. I’m also a coder. I majored in Computer Science at U.C. Berkeley and worked as a software developer for a couple of years. Then I taught high school computer science for over a decade and a half in Oakland, California. For the most part, these were two separate worlds for me. I taught programming by day and made comics by night. But I’ve always wanted to bring them together. I wanted to make an explicitly educational comic that taught readers the concepts I covered in my introductory programming class. That’s what Secret Coders is. It’s both a fun story about a group of tweens who discover a secret coding school, and an explanation of some foundational ideas in computer science.
My characters are inspired by real people. Hopper and Eni’s mentor is a grumpy old janitor named Mr. Bee who has a secret past. Bee is an embodiment of the ideals espoused by a computer scientist named Seymour Papert. Papert is something of a genius. He helped invent a computer programming language for kids called Logo, which was how I learned to code. Papert also worked on the Lego Mindstorms toy line. By including a wide array of inspirations, I’m hoping to reflect at least some of the diversity of the tech community.
Computers and the algorithms they run are precise, perfect, meticulously programmed, and austere. That’s the idea, anyway But there’s a burgeoning, alternative model of programming and computation that sidesteps the limitations of the classic model, embracing uncertainty, variability, self-correction, and overall messiness. It’s called machine learning, and it’s impacted fields as diverse as facial recognition, movie recommendations, real-time trading, and cancer research—as well as all manner of zany experiments, like Google’s image-warping Deep Dream.
The question, then, is why one would want to generate opaque and unpredictable networks rather than writing strict, effective programs oneself. The answer, as Domingos told me, is that “complete control over the details of the algorithm doesn’t scale.”
There is merit in school students learning coding. We live in a digital world where computer programs underlie everything from business, marketing, aviation, science and medicine, to name several disciplines. During a recent presentation at a radio station, one of our hosts said that IT would have been better background for his career in radio than journalism.
There is also a strong case to be made that Australia’s future prosperity will depend on delivering advanced services and digital technology, and that programming will be essential to this end. Computer programs and software are known to be a strong driver of productivity improvements in many fields.
Although we've yet to settle on a term for it, the convergence of HPC and a new generation of big data technologies is set to transform science. ConFlux will enhance traditional physics-based computer models with big data techniques. The design strategy calls for specialized supercomputing nodes matched to the needs of data-intensive operations. Enabling technologies include next-generation processors, GPUs, large memories, ultra-fast interconnects, and a three-petabyte hard drive.
Software is in almost everything we touch, so the demand for software engineers is increasing exponentially. If our kids can't fill these jobs, then someone else's will and we'll have to continue to import talent.
But focusing on computer science as a gateway to good jobs would be akin to thinking about English Language Arts only in the context of script writing. It's shortsighted and completely misses the fact that coding is a new literacy that can help kids develop and achieve across every core competency.
According to a recent study by Tufts University, kids who study computer science improve transferrable skills like sequencing, which has a direct positive correlation with improved reading comprehension.
A team of researchers led by a Virginia Tech faculty member have received $1.25 million from the National Science Foundation to introduce students to computational approaches and the ways they can help deepen their classroom science experiences.
Programming is now common in schools but just because students have spent their lives using technology does not mean they are all good at creating the coding that underpins it.
Programming makes things work. Phones, fridges, robot vacuum cleaners – these digital devices work because a programmer has coded a list of instructions a computer can understand.
"Programming gets students to think," Gesthuizen says.
"Coding is a high-level skill that gets them thinking about solving a problem, and there is more than one way to solve a problem. When students are learning maths, they are learning individually, but programming is very social and collaborative."
If we look at some of the ways that researchers are developing artificial intelligence now—through deep learning, iterative neural networks, and so on—the systems they’re creating don’t seem likely to respond emotionally in ways that would be recognizable to us. Is it a fantasy to imagine that our artificial intelligences will feel in the same way that we feel?
I don’t think it’s a fantasy; I think it’s a necessity. Or, at the very least, emotion is a side effect of intelligence. If we do accidentally manage to create something intelligent, I would guess that it would exhibit some kind of emotion as a side effect of intelligent action in the world. And I’m not alone in thinking this, by the way.
You might presume, or at least hoype, that humans are better at understanding fellow humans than machines are. But a new MIT study suggests an algorithm can predict someone's behavior faster and more reliably than humans can.
It’s fairly common for machines to analyze data, but humans are typically required to choose which data points are relevant for analysis. In three competitions with human teams, a machine made more accurate predictions than 615 of 906 human teams. And while humans worked on their predictive algorithms for months, the machine took two to 12 hours to produce each of its competition entries.
An Iowa State University study found instructors may be able to better assess how a student performs in class through the use of digital textbook analytics.
Of those 236 students who opted for the digital textbook, the average spent nearly 7.5 hours reading over 11 days through the 16-week semester. Junco noted students who spent more time highlighting their readings also earned overall higher grades in the course.
Though the technology industry is booming, especially in Washington, only about one in four high schools nationwide teach computer science.
Many local and national efforts are hoping to close that gap. In its last term, the Legislature passed a bill that will use $2 million in state and private funds to train high school teachers to teach computer science and to set standards and teacher training programs for 2016-17.
And in the past few weeks, Microsoft also expanded its own program, which now is operating in nearly 60 Washington schools.
You probably know the original story: One of the blind men touched the ear and said an elephant is a curtain; the second touched the leg and announced that an elephant is a huge pillar; and the third touched the tail and said, “No, no, it’s a small snake.” The story allows us to cast the question about the integrity of Logo as: Where’s the elephant? As teachers we might well worry: in our desire to show children many ways to use the computer are we missing the wood for the trees?
The critical logician in me felt a little silly when the associationist bricoleur in me reached for Robert Dehort’s The Life and Lore of the Elephant and Why Animals Have Tails. But the bricoleur won, for the trail led me to see the connection between ears, legs, and tails in a deeper light than I had before. And it turned the story into a much better metaphor for parts and wholes of understanding.
By this time the meaning of the word “blind” in the original story had changed for me. In its complacent reading the story allows those of us who are blessed with sight to congratulate ourselves on seeing the connections between the ears, legs, and tails of the elephant. But to “see” real connections we also need a spirit of playful, exploratory inquiry and skill in the unnamed art of making connections. And most of all, we need to have a taste for making connections, to retain the joy in connecting that is innate in all of us – though so often attenuated in school by habits of dissociated learning.
Kids may intuitively master Twitter, Snapchat and Angry Birds, but unless we teach them programming in school, they’ll never have the skills necessary to develop the next generation of software.
Coding as a career is not for everyone, nor should it be. Unlike 2012’s viral campaign called CodeYear, created by startup Codecademy, I am not suggesting that people drop what they’re doing and try to become programmers overnight. The point is, just as we do with broadly applicable subjects like math and science, we should introduce it and teach it early on so that kids have yet another valuable arrow in their knowledge quiver, whether they later pursue a career in programming or not.
As a visual experience, museums are finding new rivals in services like Netflix and YouTube. But many are incorporating new tech to enhance the museum-going experience.
Despite having one of the greatest collections of art and being one of the most visited museums in the world, the Met finds itself in the same boat as other museums: How does it compete in an age where our eyeballs are glued to our screens. Why spend the energy to visit a museum when you can do it virtually online?
“Our competition is Netflix and Candy Crush,” Sreenivasan says, not other museums.
Which is why the Met and other museums are investing in technologies to make the museum experience more interactive, even working with the smartphones that guests carry with them. The Met has a staff of 70 in the digital-media department, and 70 more handling tech hardware in general. Rather than fighting Facebook and YouTube, it’s acknowledging that services like Snapchat are the new culture. The Met’s mission is finding a way to fit in alongside them.
Thanks to the advances in deep machine learning, technology companies across the globe are teaching computers to think for themselves
Machine learning and deep learning have grown from the same roots within computer science, using many of the same concepts and techniques. Simply put, machine learning is an offshoot of artificial intelligence that enables a system to acquire knowledge through a supervised learning experience.
It’s a straightforward enough process, in theory: a human being provides data for analysis, and then gives error-correcting feedback that enables the system to improve itself. Depending upon the patterns in the data it’s exposed to, and which of those it recognises, the system will adjust its actions accordingly. It's this ability to self-develop without the need for explicit programming, but rather to change and adapt when exposed to new data, that makes machine learning such a powerful tool.
But how much control can we knowingly cede to a computer that has no morals, no ethics, only programming. If an autonomous car kills, who do we blame?
“Fundamental to Computer Science is 'computational thinking', but this term is slippery. It implies learning to think like a computer, but thinking is a human concept that computers are not actually capable of.”
‘Computational thinking’ is the understanding of how to construct problems so they can eventually be expressed in binary mathematics. This is a complex and multi stage process involving an understanding of the key concepts of algorithms, logical reasoning, decomposition and abstraction.
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