What is metacognition?

Metacognition, a term that was first defined by John H. Flavell in 1979, is basically thinking about thinking. With metacognition, we become aware of our own learning experiences and the activities we involve ourselves in our paths toward personal and professional growth. We are better able to understand ourselves in the whole process of learning and can develop skills to think about, connect with, and evaluate our learning and interactions each day. But how and why is metacognition important in education?

It has been identified as an essential skill for learner success. Therefore, do we need to design specific lessons focused on metacognition for use in our classrooms each day? And if so, how can we make this happen?


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Sleep is crucial for our survival, and many diseases are linked to long-term poor sleep quality. Before we can use sleep to enhance our health and performance and alleviate diseases associated with poor sleep, a greater understanding of sleep regulation is necessary. Researchers had identified a while ago a mutation in the β 1-adrenergic receptor gene (ADRB1) in humans who require fewer hours of sleep than most. In vitro, this mutation leads to decreased protein stability and dampened signaling in response to agonist treatment. In vivo, the mice carrying the same mutation demonstrated short sleep behavior. The scientists found that this receptor is highly expressed in the dorsal pons and that these ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness. Activating these neurons can lead to wakefulness, and the activity of these neurons is affected by the mutation. These results highlight the important role of β 1-adrenergic receptors in sleep/wake regulation.



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Farmers have always done their best to maximize yield, whether that be cramming another row of corn onto a field or breeding beefier cattle, but pig farmers in China are really taking things to a new level. As Bloomberg reports, some pig farmers in the country have bred their animals into an almost unrecognizable state, and boy are they big.


On a farm owned by a man named Pang Cong, one particularly supersized pig weighs in at over 1,100 pounds, making it bigger than a polar bear. Pork prices are currently high in many parts of China due to an illness that rapidly swept through farms and claimed the lives of countless animals. Now, the farms with healthy pigs are doing their best to meet the demand, and are getting top dollar for their animals as a bonus.



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Professor Michel Mayor and Professor Didier Queloz, both working in Switzerland, were awarded for discovering the first exoplanet orbiting a sun-like star other than our own in 1995. 51 Pegasi b is a gaseous ball similar to Jupiter and was discovered by the professors at the Haute-Provence Observatory in southern France in 1995. Since this initial discovery, over 4,000 exoplanets have been found – 1,900 of which have been confirmed. Professor Queloz and Professor Mayor’s discovery is now regarded as a pivotal moment in astronomy that changed our understanding of our place in the universe. No planet other than those in our own solar system had ever been found before. 



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Quantum computers with the ability to perform complex calculations, encrypt data more securely and more quickly predict the spread of viruses, may be within closer reach thanks to a new discovery by Johns Hopkins researchers.


"We’ve found that a certain superconducting material contains special properties that could be the building blocks for technology of the future," says Yufan Li, a postdoctoral fellow in the Department of Physics & Astronomy at The Johns Hopkins University and the paper’s first author. The findings will be published October 11, 2019, in the journal Science.



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A tapeworm is essentially a very long, parasitic towel with a grappling hook for a head. It attaches itself to the internal organs of its host with its fiendish head spines, and it absorbs nutrients through its tagliatelle-shaped body. Once fastened, it does very little. It has no mouth or gut, no circulatory or respiratory systems. Its sparse nerves culminate in a cluster that could barely be called a brain. And yet, this very simple creature can manipulate the minds of more complex animals—even without infecting them.



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Last week Google released several thousand deepfake videos to help researchers build tools that use artificial intelligence to spot altered videos that could spawn political misinformation, corporate sabotage, or cyberbullying.


Google’s videos could be used to create technology that offers hope of catching deepfakes in much the way spam filters catch email spam. In reality, though, technology will only be part of the solution. That’s because deepfakes will most likely improve faster than detection methods, and because human intelligence and expertise will be needed to identify deceptive videos for the foreseeable future.


Deepfakes have captured the imagination of politicians, the media, and the public. Video manipulation and deception have long been possible, but advances in machine learning have made it easy to automatically capture a person’s likeness and stitch it onto someone else. That’s made it relatively simple to create fake porn, surreal movie mashups, and demos that point to the potential for political sabotage.


There is growing concern that deepfakes could be used to sway voters in the 2020 presidential election. A report published this month by researchers at NYU identified deepfakes as one of eight factors that may contribute to disinformation during next year’s race. A recent survey of legislation found that federal and state lawmakers are mulling around a dozen bills to tackle deepfakes. Virginia has already made it illegal to share nonconsensual deepfake porn; Texas has outlawed deepfakes that interfere with elections.


Tech companies have promoted the idea that machine learning and AI will head off such trouble, starting with simpler forms of misinformation. In his testimony to Congress last October, Mark Zuckerberg promised that AI will help it identify fake news stories. This would involve using algorithms trained to distinguish between accurate and misleading text and images in posts.

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