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Saturday, January 20, 2018

Students as young as pre-K learning computer coding through Dallas ISD STEM | KENS 5 TV

"Students are learning computer coding as young as pre-kindergarten" reports Demond Fernandez, WFAA, TV News Reporter. 

Photo: KENS 5 TV

It is a trend happening at one elementary school in Pleasant Grove, where children are getting a very early introduction to STEM programs.

In Mrs. Rogers kindergarten class at Frederick Douglas Elementary School, five-year-old students are busy learning to code. In fact, computer coding and technology has become part of regular class instruction on the campus, since a pilot program was introduced last school year.

Watch this video

Students from pre-K to fifth grade are programming and developing apps.

"The kids love it,” said Allana Felder, the schools Science Instructional Coach. “They love getting on the computers.”

Teachers said these elementary school students are learning everything from the basics to beyond. Staff calls this early exposure to computer science a game changer for Dallas ISD.

“Statistics show that out of the stem discipline, technology and computer science is one of those that doesn’t have a high enrollment in the high schools and colleges," Felder explained. 

Staff believes the skills are setting students up for scholarships and careers early on.

Source: KENS 5 TV

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Science tells us when life begins. Why isn't it taught in the classroom? | Washington Examiner - Opinion

"Recent debates about what American students should be learning in their science classrooms have focused on evolution and climate change" says Brooke Stanton, founder and CEO of Contend Projects, a science education organization dedicated to spreading accurate information about the start of a human life and the biological science of human embryology. 

For 75 years the field of human embryology has documented when a human life begins in the Carnegie Stages of Early Human Embryonic Development and the Carnegie Chart.
Photo: iStock

Advocates for America’s latest K-12 science guidelines, the Next Generation Science Standards, claim that anyone who does not adopt or support the new standards, including the controversial content about evolution and climate change, is a “science denier.” Interestingly, this new science education policy excludes a fundamental, far-reaching, and powerful science reality: when the life of a human organism/being begins — thus making the NGSS a “science denier” as well.

The NGSS identifies key concepts or “core ideas” that represent the “most important aspects of science content knowledge” (per the designers). The first core idea in life sciences is that cells are the basic unit of life and that an organism may consist of one cell or many cells. This significant concept is extremely deficient if students are not learning about the structure, function, growth, and development of early human organisms/lives too.

When a human being begins to exist as a single-cell organism is an essential and relevant scientific fact of life that everyone can and should know, because there is a simple, well-established answer. For 75 years the field of human embryology (the branch of biology that specializes in the beginning of human life and early development) has documented when a human life begins in the Carnegie Stages of Early Human Embryonic Development and the Carnegie Chart.

Carnegie Stage 1a marks the beginning of a sexually reproduced human life.

The Carnegie Stages are the global authority of human embryological research. Human embryologists view the Carnegie Stages and Chart as chemists view the Periodic Table — it’s their gold standard. The Carnegie Chart contains the 23 Stages of development of the early human being during the eight-week embryonic period and was formally instituted in 1942 by the National Museum of Health and Medicine’s Human Developmental Anatomy Center (a secular government organization that is a part of the National Institutes of Health). The Carnegie Stages are required to be included in every genuine human embryology textbook worldwide.

In human sexual reproduction, both in vivo (inside the body) and in vitro (outside the body), the biological beginning of a new human being/organism occurs at Carnegie Stage 1a, at first contact of the sperm and the oocyte, the beginning of the biological process of fertilization. Fertilization mainly occurs in vivo in a woman’s fallopian tube, not in her uterus/womb, and the beginning of the fertilization process is when pregnancy normally begins as well.
Read more... 

Source: Washington Examiner

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Science of Learning symposium brings together experts from diverse fields | The Hub at Johns Hopkins - Science+Technology

The Science of Learning symposium will take place on Monday, Jan. 22, from 8 a.m.–5:30 p.m. at Hodson Hall on JHU's Homewood campus. The event will also be broadcast live on the Johns Hopkins UStream Channel.

"For its symposium next week, the Science of Learning Institute has borrowed the famous tagline chanted at passengers of the London Underground—"Mind the Gap."" inform Katie Pearce, Writer & Editor at Johns Hopkins University.

In this case, "the gap" refers to the gulfs that exist between different disciplines, research methods, and even individual viewpoints when it comes to understanding the fundamental science of learning.

Of course, the Johns Hopkins institute was launched five years ago this month to tackle this very challenge.

"Our key goal has been to bridge this gap by creating opportunities for innovative collaboration in science and practice," said Barbara Landau, who directs the cross-disciplinary effort. "We consider this essential for achieving a truly comprehensive, integrative understanding of learning."

The diverse range of experts who will take part in the institute's third biennial symposium Monday reflects this ongoing mission. The event brings together experts in cognitive science, neuroscience, education, and other fields to explore different perspectives on the cognitive and neural bases for learning and motivation.

One visitor from outside Hopkins is psychologist Daniel Simons of the University of Illinois, whose famous "Invisible Gorilla" study helped reveal just how much the human brain can miss when focused elsewhere...

selective attention test 

The institute, one of the most comprehensive of its kind, was forged with the goal of understanding learning from all levels of scientific inquiry, including brain and cognitive development, neurological and psychiatric diseases, and the effects of aging. Among its efforts since, the institute has funded a total of 33 collaborative grants on target research areas including memory and attention, language, and spatial cognition.​

Source: The Hub at Johns Hopkins and Daniel Simons Channel (YouTube)

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Sunday, January 14, 2018

Maryam Mirzakhani Scholarship for Women | Financial Tribune - Art And Culture

In honor of the late award winning Iranian mathematician at Stanford University, Maryam Mirzakhani (1977-2017), Persia Educational Foundation, based in London, has established the ‘Persia Mirzakhani Scholarship for Women.’

Photo: Financial Tribune

The scholarship is designed to support the education of Persian-speaking women of any age or citizenship enrolled in a master of science or final year of a doctorate program studying STEM at the University College London. STEM is a recent department at UCL, which focuses on the interface between science, technology, engineering and math.

The inaugural scholarship will be £1500 ($2,000). Eligible applicants will be encouraged to submit their application for the 2018-2019 academic year to the foundation by February 1, according to the website of the foundation

Scholarship winners will be announced on May 3, marking the first birth anniversary of the accomplished professor.

In 2014, Mirzakhani became a household name after becoming the first woman ever to win the Fields Medal, which is widely referred to as the Nobel Prize of mathematics awarded to honor excellence in the field to mathematicians under the age of 40.
Read more... 

Source: Financial Tribune

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The scientific debates of the Vienna circle | The Economist - Books and arts

"Philosophy and science between the wars" appeared in the Books and arts section of the print edition under the headline "Talking heads"  

 Entrance to the Mathematical Seminar at the University of Vienna, Boltzmanngasse 5. Meeting place of the Vienna Circle.
Photo: Wikipedia, the free encyclopedia

ON OCTOBER 21st 1916 Friedrich Adler, a theoretical physicist turned socialist politician, went to a famous restaurant in Vienna and ate a three-course lunch. Having lingered over coffee, he went up to Karl von Stürgkh, the imperial prime minister, who was sitting at a nearby table, and shot him several times with a pistol, killing him. Adler, the son of the legendary founder of Austro-Hungarian social democracy, calmly waited to be arrested. Something had to be done to change the general way of thinking, he claimed, and he had done it. At first condemned to death, he was pardoned two years later.

When the Nazis came to power in Austria, Adler, by then the secretary of the Socialist Workers’ International, held urgent meetings with other socialist politicians to work out a common strategy. During one of these meetings, an emotional Adler rambled on, seemingly unable to come to the point. “He shoots better than he talks,” one French delegate remarked drily. “Exact Thinking in Demented Times”, Karl Sigmund’s fond and knowledgeable exploration of the ideas and members of the legendary Vienna circle between the two world wars, contains stark warnings not only about demented times, but also about the possible costs of exact thinking.

Exact Thinking in Demented Times:
The Vienna Circle and the Epic Quest for the Foundations of Science

The Vienna circle was made up mainly of physicists, mathematicians and philosophers, whose fortnightly meetings were dedicated to investigating problems of logic, science, language and mathematics. Led by Moritz Schlick, a philosopher, the discussions attracted some brilliant intellectuals, including Kurt Gödel, a mathematician; Otto Neurath, an economist; three philosophers—Rudolf Carnap, Sir Karl Popper and Ludwig Wittgenstein (pictured, whose work became the main focus of the discussions for a while)—as well as Albert Einstein and Bertrand Russell.

Debates about the possibility of a unified science, the dangerous vagaries of everyday language or the structures of mathematics and logic raged on for more than two decades. These arguments, which seemed so abstract, produced insights of vital importance for computing, astrophysics and cosmology, not to mention theory of science and philosophy. Mr Sigmund devotes a considerable part of the book to explaining some of these concepts. Readers unable to grasp them immediately are in good company. “Most scholars agree”, he writes, “that neither Wittgenstein nor Russell ever really understood Gödel’s ideas.”
Read more... 

Source: The Economist 

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Alpha Zero Teaches Itself Chess 4 Hours, Then Beats Dad | Science 2.0 - Life sciences

"Peter Heine Nielsen, a Danish chess Grandmaster, summarized it quite well. "I always wondered, if some superior alien race came to Earth, how they would play chess. Now I know"" according to Tommaso Dorigo, experimental particle physicist. 


The architecture that beat humans at the notoriously CPU-impervious game Go, AlphaGo by Google Deep Mind, was converted to allow the machine to tackle other "closed-rules" games. Successively, the program was given the rules of chess, and a huge battery of Google's GPUs to train itself on the game. Within four hours, the alien emerged. And it is indeed a new class of player.
The AlphaZero neural network uses reinforcement learning to teach itself things from scratch. It does not rely on previous knowledge - which in the case of chess is surprising, as the mass of knowledge on the game accumulated in centuries of experimentation is hard to shrug off. Combined with a powerful search algorithm, the neural network is at present unbeatable. This was demonstrated in a 100-game match against the strongest chess program around, Stockfish 8.

What impressed me when I saw a few games from that match, which was concluded with 25 wins and 75 draws, no losses from Alpha zero, is that the machine can display an evolved treatment of openings, is keen to sacrifice material for positional gains, and has no prejudices. Indeed, while most chess machines around have pre-defined weights that discourage certain kinds of positions -say, putting your king in the center of the board when there's lots of pieces around potentially capable of threatening it is a no-no strategy, punished with negative weights that prevent chess engines from entertaining the thought- alpha zero knows no borders. Look at this position, e.g.:

It transpires that something has gone wrong for black - while its position is solid, it is left with a white-squared bishop that has no future, blocked as it is from its own central pawns. White, instead, has gotten rid of its own potentially similarly fated darksquared bishop, and enjoys more space. So what is the next move that white does here? 

Source: Science 2.0

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Learn To Build Your Own Neural Networks With This Training Bundle | Interesting Engineering - Innovation - AI

This is a promotional article about one of the company partners with Interesting Engineering. By shopping with us, you not only get the materials you need, but you’re also supporting our website.

IE Shop, The Interesting Engineering Shop features exclusive offers on the latest gadgets, software, and online courses hand-picked for Interesting Engineering readers inform, "Use TensorFlow and Theano to get a firm understanding of deep learning and artificial intelligence."

Photo: Pixabay

Science fiction movies seem to have done Artificial Intelligence (AI) a bit of a disservice. Due to decades of popular yet farfetched sci-fi releases, when most people think of AI, they think only of evil robots taking over the planet, or perhaps friendlier (but still evil, maybe?) robots along the lines of the robot-woman in Ex Machina.

In many ways, however, real-life artificial intelligence has become more interesting than in the movies, with self-driving cars redefining transportation, quantum computing reshaping how we work with large sets of data, and medical robots performing some of the most advanced surgeries known to man with astounding precision.

Indeed, the future of technology in many ways belongs to AI. This means that the most exciting and important careers of the future will belong to those who possess a solid understanding of both deep learning and artificial intelligence principles. The Deep Learning and Artificial Intelligence Introductory Bundle breaks down some of the more fascinating topics in these fields into easy-to-understand and entertaining lessons, and it’s on sale for just $39.

The first course in this bundle kicks off your education in this exciting field by giving you a deep understanding of probability theory—one of the most fundamental elements of deep learning and AI, since it allows for complex and accurate predictions to be made. You’ll learn about everything from Moore’s Law and linear regression to large-scale data analysis and beyond.

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Saturday, January 13, 2018

How machine learning engineers can detect and debug algorithmic bias | Boing Boing

Follow on Twitter as @doctorow
"Ben Lorica, O'Reilly's chief data scientist, has posted slides and notes from his talk at last December's Strata Data Conference in Singapore, "We need to build machine learning tools to augment machine learning engineers."" notes Cory Doctorow, Writer, blogger, activist.

Photo: Boing Boing

Lorica describes a new job emerging in IT departments: "machine learning engineers," whose job is to adapt machine learning models for production environments. These new engineers run the risk of embedding algorithmic bias into their systems, which unfairly discriminate, create liability, and reduces the quality of the recommendations the systems produce. 

He presents a set of technical and procedural steps to take to minimize these risks, with links to the relevant papers and code. It's really required reading for anyone implementing a machine learning system in a production environment. 

We need to build machine learning tools to augment machine learning engineers [Ben Lorica/O'Reilly]
(via 4 Short Links)

Source: Boing Boing

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Seven Ways Cybercriminals Can Use Machine Learning | Forbes - Technology

"AI has given cybercriminals new ways to steal information, but there are things you can do to prevent it" reports Alexander Polyakov, CTO and Co-Founder at ERPScan. President of EAS-SEC. SAP cybersecurity evangelist. 

Photo: Shutterstock

Ben Gurion, the main international airport in Israel, is one of the most protected airports in the world. It is known for its multilayered security. On the way from the office to the airport, you get caught in the lens of airport cameras. The road curves several kilometers to the terminal, and when you are driving, the security system has enough time to analyze your identity. In case of any signs of danger, you will be intercepted. The system of behavior anomalies analysis in computer systems works the same way. The implementation of these systems is effective in defense. While a perpetrator is running certain commands, an AI-based system can stave off any damage, having identified an intrusion.

AI deployment is not so rosy in the world of cybersecurity. Hackers move forward and adopt it as well. The U.S. intelligence community reports that artificial intelligence actually works in cybercriminals' favor.

Let's go over a few areas for hackers deploying machine learning and find out which cybersecurity measures should be taken.

Data Gathering

Every single breach starts with data gathering. Hackers maximize the chances of success by gaining more information. They classify users and select a potential victim thoroughly using several classification and clustering methods. This task can be automated.
How can you protect yourself from being their victim? It goes without saying that your personal information must not be available in open sources, so you should not publish an awful lot of information about yourself on social networks.

Neural networks can be trained to create spams that resemble a real email. However, in order for this to work, it is better to know the sender’s behavior. This can be achieved through network phishing that provides hackers with easy access to personal information. Research from BlackHat about automated spearphishing on Twitter proves this idea. This tool can increase the success of phishing campaigns up to 30% -- which is twice as much as traditional automation and similar to manual phishing.

How can you protect yourself from phishing? You could just mail a question to a sender. Hackers have become savvier, however, and can analyze your message and respond appropriately so that you are sure that the account is not compromised. Nowadays systems are not complicated but it will not be long before smart chat bots communicate with you like your friends do.

The most actionable recommendation is to ask the user through other channels and messengers if he or she sent the message. There is little chance that several of his or her accounts are compromised at once...

The ideas above are only some examples of the ways hackers can use machine learning.

Aside from using more secure passwords and being more careful while following third-party websites, I can only advise paying attention to security systems based on AI in order to be ahead of perpetrators. A year or two ago, everyone had a skeptical attitude toward the use of artificial intelligence. Today’s research findings and its implementation in products prove that AI actually works, and it's here to stay.

Source: Forbes 

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Artificial intelligence can ‘evolve’ to solve problems | Science Magazine

Photo: Matthew Hutson
"Neural networks are using one more trick from nature" says Matthew Hutson, freelance science journalist in New York City.  

Snippet: AI controlled walking success 

Many great ideas in artificial intelligence languish in textbooks for decades because we don’t have the computational power to apply them. That’s what happened with neural networks, a technique inspired by our brains’ wiring that has recently succeeded in translating languages and driving cars. Now, another old idea—improving neural networks not through teaching, but through evolution—is revealing its potential. Five new papers from Uber in San Francisco, California, demonstrate the power of so-called neuroevolution to play video games, solve mazes, and even make a simulated robot walk.

Neuroevolution, a process of mutating and selecting the best neural networks, has previously led to networks that can compose music, control robots, and play the video game Super Mario World. But these were mostly simple neural nets that performed relatively easy tasks or relied on programming tricks to simplify the problems they were trying to solve. “The new results show that—surprisingly—you may actually not need any tricks at all,” says Kenneth Stanley, a computer scientist at Uber and a co-author on all five studies. “That means that complex problems requiring a large network are now accessible to neuroevolution, vastly expanding its potential scope of application.”

At Uber, such applications might include driving autonomous cars, setting customer prices, or routing vehicles to passengers. But the team, part of a broad research effort, had no specific uses in mind when doing the work. In part, they merely wanted to challenge what Jeff Clune, another Uber co-author, calls “the modern darlings” of machine learning: algorithms that use something called “gradient descent,” a system that gradually improves a solution by reducing its error. Nearly all methods of training neural networks to perform tasks rely on gradient descent.

The most novel Uber paper uses a completely different approach that tries many solutions at once. A large collection of randomly programmed neural networks is tested (on, say, an Atari game), and the best are copied, with slight random mutations, replacing the previous generation. The new networks play the game, the best are copied and mutated, and so on for several generations. The advantage of this method over gradient descent is that it tries a variety of strategies instead of putting all its effort into perfecting a single solution. When compared with two of the most widely used methods for training neural networks, this exploratory approach outscored them on five of 13 Atari games. It also managed to teach a virtual humanoid robot to walk, developing a neural network a hundred times larger than any previously developed through neuroevolution to control a robot.
Read more... 

Related link 

Source: Science Magazine and Science Magazine Channel (YouTube)

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The AI glossary: 5 artificial intelligence terms you need to know | TechRadar - AI Week

TechRadar's AI Week is brought to you in association with Honor.

"Get your terms straight" continues TechRadar.

Photo: TechRadar - AI Week

Artificial intelligence is fast encroaching into every area of our digital lives, picking the social media stories we see, identifying our friends and pets in photos, and even making sure we avoid accidents on the road. If you want to understand AI though, you need to start with the terms underpinning it.

And so we present the TechRadar glossary of AI: five of the key words and phrases you'll want to know to get a hold on this ever-improving tech – and to keep up your end of the conversation the next time the topic crops up around the dinner table.

First, though, a disclaimer – not everyone agrees on the exact definition of some of these words, so you might see them used differently elsewhere on the web. Wherever possible we've tried to stick to the most commonly used definitions, but with such a fast-growing and new technology, there are always going to be discrepancies.
1. Algorithms 
Ah, the famous (or infamous) algorithm. Algorithms are sets of rules that computer programs can follow, so if one of your best friends posts a photo of you on Facebook, then the rules say that should go up at the top of your News Feed. Or if you need to get from A to B on Google Maps, an algorithm can help you work out the fastest route...

2. Artificial intelligence 
Just what is artificial intelligence anyway? Definitions differ depending on who you ask, but in the broadest sense it's any kind of intelligence that has been artificially created. Obviously.

So when Siri replies to you like a real human being, that's artificial intelligence. And when Google Photos seems to know what a cat looks like, that's artificial intelligence too. And Anthony Daniels hiding inside his C-3PO suit is artificial intelligence as well, in a way – the illusion of a talking, thinking robot which is actually controlled by a human.
Read more... 

Source: TechRadar

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Friday, January 12, 2018

Students and Robots, in Harmony | Inside Higher Ed - Digital Learning

"At Michigan State, some online students embody robots to populate face-to-face classrooms, helping bridge the distance gap with their on-campus counterparts" notes Mark Lieberman, Digital Learning Reporter at Inside Higher Ed.

A face-to-face student sits in a class session with her peers at a distance.
Photo: Christine Greenhow/Michigan State University 

Three years ago, Christine Greenhow, associate professor of educational psychology and educational technology at Michigan State University, attended a faculty meeting that would set her on an unexpected path. Presenters from the institution’s design studio showcased two different models of robots: a Kubi, which “looks sort of like an iPad on a neck that sits on a desk,” according to Greenhow, and a Double, which can roll around hallways.

The designers said they were employing the robots at alumni meet-up events, allowing out-of-town participants to mingle with their on-campus former peers. But Greenhow envisioned another place for the robots: in her own classroom.

Greenhow teaches doctoral courses with between 10 and 15 students -- some on campus, others participating synchronously online. For years, she struggled to bridge the “transactional distance” that remote students faced when trying to integrate into classroom discussions and activities. The robots, she thought, could solve that problem.

“If everybody felt embodied in a robot, I as the instructor would know exactly where to look. I’d look them right in the eye,” Greenhow said. “Also the ability to move -- in a videoconferencing environment, you take out a lot of these mobile cues, body language that shows attention. If both of these robots had the ability to move, maybe that would help us break down the distance that we feel.”

Greenhow has been using robots in her classes ever since. She recently published in Online Learning a study detailing her first attempt -- in spring 2015 -- integrating the devices into her classroom, revealing that online students felt more engaged when participating through the robots than when they appeared in the classroom via Zoom or another videoconference platform. The institution has since scaled up its robot inventory, purchasing 14 robots and using 15 more on loan.

“Any time you introduce a new technology, it’s fraught with anxiety. Inevitably problems happen,” Greenhow said. “Everybody has to be willing to take that risk.”

Design Studio - Video Spotlight #1, Robotic Telepresence

How It Works
To populate the robots, online students simply download free software on their personal computers and log in. They can remotely control their movements and zoom level using the arrow keys.

Greenhow says she was able to give more individual attention to students when she felt she could draw all of them into a conversation with equal success. On-campus students felt a greater sense of connection to their remote peers, she said. And online students reported feeling more engaged and less prone to distraction when using robots than when using the less advanced forms of synchronous online learning...

How They Got There
Greenhow said she was nervous to get acquainted with the robots on her own -- but she had help from one of the institution’s “tech navigators,” who sat in on classroom sessions and performed on-the-spot troubleshooting.

John Bell, professor of educational technology and director of the institution’s Counseling, Educational Psychology and Special Education/College of Education Design Studio, oversees those tech navigators. His team purchased two robots for Michigan State on a whim after a previous project came in under budget. Early responses from students used words like “transformative,” convincing Bell that they merited further exploration.
Read more... 

Source: Inside Higher Ed and DesignStudio MSU Channel (YouTube)

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NUC approves LASU open and distance learning and research institute | Vanguard - Education

"THE National Universities Commission, NUC, has approved the establishment of the Lagos State University, LASU, Open and Distance Learning and Research Institute, ODLRI" inform Vanguard.

Photo: Lagos State University, LASU

Read more at:
The approval came after a recent visit by the universities’ regulatory body to assess facilities of the institute at the Main Campus of the University in Ojo.

The ODLRI was established by the Prof. Olanrewaju Adigun Fagbohun-led administration to replace the now rested LASU External System, LASUES, and provide opportunity for millions of knowledge seekers who are desirous of pursuing their first degrees in the university, but are unable to gain admission into the regular programmes. The ODLRI Board is headed by renowned academic and former Executive Secretary, NUC, Professor Peter Okebukola. 

Vice-Chancellor, Professor Fagbohun, while receiving the NUC team on their assessment visit, had assured the Executive Secretary, NUC, Professor Abubakar Rasheed, that LASU-ODLRI will be the gold standard in the delivery of quality university education in Africa through the Open and Distance Learning delivery platform. The approved take-off programme is the Bachelor of Science in Business Administration. 

LASU-ODLRI has several unique features. Course contents are pitched above NUC minimum standards with slant on 21-Century skills especially critical thinking, teamwork, entrepreneurship and digital literacy. It will be delivered on a learner-friendly, easy-to-navigate e-learning platform with 24-hour learner support. It will apply global best practices in Open and Distance learning delivery.
Read more... 

Source: Vanguard

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Is Personalized Learning the Next Big Thing in K-12 Philanthropy? | Inside Philanthropy - Education

Photo: Caitlin Reilly
"Tech funders like CZI have led the way in backing personalized learning lately, but other foundations are also on board. We take a deep dive into what's happening in this fast evolving grantmaking space" says

Photo: Monkey Business Images/shutterstock

A former senior program officer at the Gates Foundation has noticed something changing when people talk about personalized learning.

“A lot of the conversation would be about why. Why do you need personalized learning? Why is it a good innovation and direction we should be going in?” Helayne Jones said. “And now, you’re not hearing the questions about why. You’re hearing the questions about how.”

“I think most national funders working on K-12 are looking to make personalized learning investments in a variety of ways,” Jones said.

Personalized learning grabbed headlines last year with several big gifts from Mark Zuckerberg and Priscilla Chan’s philanthropic outfit. Jones now works as a consultant to New Profit, a nonprofit accelerator that received one of those big gifts, for $13 million, from the Chan Zuckerberg Initiative and the Gates Foundation. In turn, these funds are being dispersed to groups working at the forefront of personalized learning. 

CZI escalated its personalized learning work after bringing Jim Shelton on board. Shelton is a former deputy secretary of education and previously worked for the Gates Foundation.

CZI funded several personalized learning projects in 2017. It helped fund Rhode Island’s move to bring personalized learning to classrooms statewide. CZI made donations of undisclosed amounts to Chiefs for Change, which works with a network of districts across the country, and the College Board, as we reported. CZI is also promoting a free personalized learning tool, the Summit Learning Platform, a project that began at Facebook in partnership with Summit Learning, and is now a centerpiece of CZI's work in this space. 

While personalized learning’s rise is undeniable, defining it is trickier. Generally speaking, the term refers to tailoring instruction to students’ needs, but in practice, it can take a wide range of forms. 

“Personalized learning is not particularly well-defined. There’s no definition that’s coalesced yet within the space,” said Elisabeth Stock, CEO of PowerMyLearning. “And so everyone is doing different things that they are calling personalized learning. There's no definitive answer,” Stock said.

The national nonprofit partners with schools and districts in under-resourced communities to help them implement personalized learning. PowerMyLearning is among the groups that landed recent funding from New Profit. It's also received grants in recent years from a wide range of other funders, including the Carnegie Corporation, the Broad Foundation, the W.K. Kellogg Foundation, and a number of corporations. 

A classroom following PowerMyLearning’s model has multiple stations where students engage in different modalities of learning. For example, Stock said, for a lesson on probability, a teacher may lead a mini-lesson on the topic in part of the classroom, while at another station, students work together on an activity rolling dice. At a third station, students do independent work while logged onto PowerMyLearning’s collaborative platform.

In a PowerMyLearning classroom, there would also be periodic homework assignments designed to engage families in their children’s learning, but that is not necessarily a characteristic of personalized learning.

The third station, the collaborative platform, is the biggest reason we’ve been hearing so much about personalized learning lately.  

Personalized learning doesn’t necessarily have to include technology. Tailoring instruction to student needs is an idea that has been around for a while. The research to back it up dates back to 1980, with work led by Benjamin Bloom, a professor at the University of Chicago. The practice, arguably, goes back further than that to Maria Montessori’s work in the early 1900s.

However, practitioners and funders say new technology has made it easier to put personalized learning into practice.  

Beth Rabbitt, the CEO of Learning Accelerator, an organization that supports implementation of blended learning in schools, has observed this in her work.

“Personalized learning for every student, every day is a really tough load for teachers,” Rabbitt said. 

“I think the places where technology has the most potential as it relates to personalized learning is helping teachers and students do what they’ve been trying to do, but haven’t been able to actually do without new resources and tools.”

Jones reported a similar sentiment: “Teachers would just say to me, ‘Personalized learning allows me to be the teacher I’ve always wanted to be.”

“When you have 30 students in a classroom, you desire to be able to know each student personally, and understand their learning style, and really meet their needs. The reality is that that has been very difficult to do at the individual level,” Jones said...

The field has especially caught the eye of Silicon Valley donors, perhaps unsurprisingly, given the discipline’s new emphasis on technology. As we've reported, the Khan Academy—which offers "personalized learning resources for all ages"—has attracted funding from a number of tech winners, including John Doerr, Reed Hastings, Scott Cook, and the Gates Foundation. We've also written about the CK-12 Foundation, co-founded by Neeru Kholsa, wife of billionaire Vinod Khosla, and bankrolled by the couple's Amar Foundation. Its educational tech tools are currently used by thousands of schools in the U.S. and a growing number of international schools. In addition, personalized learning has received attention from the Emerson Collective, the philanthropic organization of Laurene Powell Jobs.

Source: Inside Philanthropy

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Four Tools That Take Blended Learning to the Next Level by Alec Sears | eSchool News

Here are the characteristics of effective blended-learning tools. 

Photo: eSchool News

In any rush of new technology there comes fresh opportunities for learning and growth that were never possible before. We’ve seen this in the past two decades with blended learning, which combines digital media with traditional classroom methods to engage students like never before.

In all of the excitement to use technology, however, the real purpose of blended learning is often lost. Collaborating on a Google Doc is fun and convenient, but blended learning should be more than that.

Effective blended-learning tools should share a few key characteristics that distinguish them from tools that are simply digital in nature. Before implementing a tool, ask yourself these three questions:

1. How does this tool help students learn in personalized ways that are not possible in a traditional classroom alone?
2. How does this tool empower students to take their education into their own hands?
3. How does this tool tap into the collective knowledge of the global community?

For those who are seeking new ways to use technology in the classroom, here are four tools that take blended learning to the next level.
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Source: eSchool News

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Thursday, January 11, 2018

'It's hard to be what you can't see' | - University News

"UW-Madison has built a supportive community for women studying in STEM fields" summarizes Pat Schneider, Writer for the Capital Times. 

Participants in UW-Madison’s Women in Science and Engineering learning community board a bus at Elizabeth Waters Residence Hall for an October field trip to the headquarters of Epic Systems in Verona.
Photo: Michelle Stocker

The dining hall in the Carson Gulley Center on the University of Wisconsin-Madison campus was dressed for dinner, complete with white tablecloths. Along with an Asian-inspired buffet, networking was on the menu.
Scores of students, all women, gathered at the tables and chatted about their classes as female professors found their places among them.

The weekly seminar of the Women In Science and Engineering learning community gives female undergraduate students in science, technology, engineering and math a chance to meet women succeeding in STEM in a low-key setting.

There’s dinner table conversation, introduction of faculty guests — women who describe their research and announce opportunities for undergrads to work for them — and a presentation by a woman working in STEM.

Along with reassurances that the feeling of not belonging is widely shared among this crowd, sometimes there’s a glimpse of the fire that fuels breakthroughs.

As UW-Madison, like colleges across the country, strives to attract women to study, and pursue careers in STEM fields, research is sending conflicting signals on what works. Nevertheless, women say, they can flourish with the support of one another.

Trina McMahon, an engineering professor who studies aquatic ecology, was the evening’s presenter. She recalled that as a student her passion carried her through setbacks.

“Certainly there were times I came home sobbing, because my experiment was such a failure. But it was the one thing I wanted to do — I didn’t care if it made me miserable,” McMahon said.

Students in WISE say the assistance and moral support provided by the community help them stay the course.

“I don’t know if I would have stuck with it without it,” said Julia Loosen, a senior and WISE program assistant.

“You meet a woman doing research that’s highly interesting and you think: ‘What can I do to get there?’ Two years later you might be working for her,” said Marie Aguirre, a sophomore majoring in applied mathematics.

The proportion of women in STEM, at UW-Madison and nationally, ranges widely from majors that are strongly male-dominated to those where women are in the majority.

Computer science and engineering, for example, are fields where women are pretty scarce. Among UW-Madison bachelor’s degree recipients in computer science in 2015-2016, 13 percent were women. That compares to 19 percent nationally. In engineering, 21 percent of bachelor’s degree recipients that year were women, at UW-Madison and nationally.

Other disciplines are less disproportionate, but still have significantly fewer women than men. For example, 33 percent of bachelor’s degree recipients in physical sciences like chemistry and physics were female at UW-Madison in 2015-2016, compared to 39 percent nationally. In mathematics, it was 37 percent female at UW and 42 percent nationally.

But in biological and agricultural sciences, women are the majority. Sixty percent of bachelor’s recipients in the country in 2015-16 were female, 57 percent at UW-Madison. In social sciences and psychology, 63 percent of graduates, nationally, were women compared to 49 percent at UW-Madison...

Diversity is a top priority at UW-Madison’s Department of Computer Sciences, said professor Michael Swift, who has long worked on such efforts.

“It’s a very pressing issue. We don’t know quite what to do,” Swift said.
Introducing more female students to computer science is important all around, he said.

“We feel there is a large population of people who would benefit from learning computer science that we are not reaching,” Swift said. “We like to teach and work with the best students — and we’re not getting all the best and brightest now by not getting many women.”...

UW-Madison’s College of Engineering began to see an increase in women students several years ago, when it followed the lead of competing institutions and changed its policy to allow freshmen to enroll in the school as majors, said Manuela Romero, associate dean for undergraduate affairs.

“It takes away the uncertainty,” Romero said. “It’s been a big recruitment tool for us.”

To support women and other students who are underrepresented in engineering, the Leaders in Engineering Excellence and Diversity program provides peer mentoring and a community of support, Romero said.

The college worked hard over the last decade to change the attitude toward tutoring, so students who most needed help would not feel stigmatized. Such cooperative learning is a key skill for future engineers.

“There is no such thing as the ‘lone engineer.’ Engineers always work through problems in groups,” Romero said.

As in computer science, there have been efforts to change the image of engineering, she said. “We talk about women in engineering, we talk about the impact of women on the engineering profession, we talk about how engineering can also be a helping profession. 

We want young women to be able to see themselves.”

The college also has developed pipeline programs to attract women and other underrepresented students to engineering. Camp Badger brings middle school students to campus for a weeklong residential program. Engineering Summer Camp is a six-week program for high school juniors and seniors.
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