Category Archives: Critical Thinking

• Critically evaluating information and arguments
• Seeing patterns and connections
• Constructing meaningful knowledge
• Applying meaningful knowledge construction in the real world.

Today’s news: Real or fake? [Infographic]

Today Students have a blizzard of information at the ready: on devices in their pockets, at school, in their homes, by their bedsides on their wrists… It’s almost a constant information “on” world.

Information and content floods to their eyes and ears in never-ending streams, torrents, downloads, feeds, & casts. How do they determine what is real an what is not. What matters and what doesn’t? Here’s a cheat sheet to help out.


At a time when misinformation and fake news spread like wildfire online, the critical need for media literacy education has never been more pronounced. The evidence is in the data:

  • 80% of middle schoolers mistake sponsored content for real news.
  • 3 in 4 students can’t distinguish between real and fake news on Facebook.
  • Fewer than 1 in 3 students are skeptical of biased news sources.

Students who meet the ISTE Standards for Students are able to critically select, evaluate and synthesize digital resources. That means understanding the difference between real and fake news.

There are several factors students should consider when evaluating the validity of news and resources online. Use the infographic below to help your students understand how to tell them apart.

Click on the infographic to open a printable PDF.

Media-Literacy_Real-News-Infographic_11_2017

Learn more about teaching K-12 students how to evaluate and interpret media messages in the book Media Literacy in the K-12 Classroom by Frank Baker.

via www.iste.org http://ift.tt/2yq5zBQ

The end of the cloud is coming

Viktor Charypar is a Tech Lead at UK-based digital consultancy Red Badger.

We’re facing the end of the cloud. It’s a bold statement, I know, and maybe it even sounds a little mad. But bear with me.

The conventional wisdom about running server applications, be it web apps or mobile app backends, is that the future is in the cloud. Amazon, Google, and Microsoft are adding layers of tools to their cloud offerings to make running server software more and more easy and convenient, so it would seem that hosting your code in AWS, GCP, or Azure is the best you can do — it’s convenient, cheap, easy to fully automate, you can scale elastically … I could keep going. So why am I predicting the end of it all?

A few reasons:

It can’t meet long-term scaling requirements. Building a scalable, reliable, highly available web application, even in the cloud, is pretty difficult. And if you do it right and make your app a huge success, the scale will cost you both money and effort. Even if your business is really successful, you eventually hit the limits of what the cloud, the web itself can do: The compute speed and storage capacity of computers are growing faster than the bandwidth of the networks. Ignoring the net neutrality debate, this may not be a problem for most (apart from Netflix and Amazon) at the moment, but it will be soon. The volumes of data we’re pushing through the network are growing massively as we move from HD, to 4k to 8k, and soon there will be VR datasets to move around.

This is a problem mostly because of the way we’ve organized the web. There are many clients that want to get content and use programs and only a relatively few servers that have those programs and content. When someone posts a funny picture of a cat on Slack, even though I’m sitting next to 20 other people who want to look at that same picture, we all have to download it from the server where it’s hosted, and the server needs to send it 20 times.

As servers move to the cloud, i.e. onto Amazon’s or Google’s computers in Amazon’s or Google’s data centers, the networks close to these places need to have incredible throughput to handle all of this data. There also have to be huge numbers of hard drives that store the data for everyone and CPUs that push it through the network to every single person that wants it. This gets worse with the rise of streaming services.

All of that activity requires a lot of energy and cooling and makes the whole system fairly inefficient, expensive, and bad for the environment.

It’s centralized and vulnerable. The other issue with centrally storing our data and programs is availability and permanence. What if Amazon’s data center gets flooded, hit by an asteroid, or destroyed by a tornado? Or, less drastically, what if it loses power for a while? The data stored on its machines now can’t be accessed temporarily or even gets lost permanently.

We’re generally mitigating this problem by storing data in multiple locations, but that only means more data centers. That may greatly reduce the risk of accidental loss, but how about the data that you really, really care about? Your wedding videos, pictures of your kids growing up, or the important public information sources, like Wikipedia. All of that is now stored in the cloud — on Facebook, in Google Drive, iCloud, or Dropbox and others. What happens to the data when any of these services go out of business or lose funding? And even if they don’t, it is pretty restricting that to access your data, you have to go to their service, and to share it with friends, they have to go through that service too.

It demands trust but offers no guarantees. The only way for your friends to trust that the data they get is the data you sent is by trusting the middleman and their honesty. This is okay in most cases, but websites and networks we use are operated by legal entities registered in nation states, and the governments of these nations have the power to force them to do a lot of things. While most of the time, this is a good thing and is used to help solve crime or remove illegal content from the web, there are also many cases where this power has been abused.

Just a few weeks ago, the Spanish government did everything in its power to stop an independence referendum in the Catalonia region, including blocking information websites telling people where to vote. Blocking inconvenient websites or secretly modifying content on its way to users has long been a standard practice in places like China. While free speech is probably not a high-priority issue for most Westerners, it would be nice to keep the internet as free and open as it was intended to be and have a built-in way of verifying that content you are reading is the content the authors published.

It makes us — and our data — sitting ducks. The really scary side of the highly centralized internet is the accumulation of personal data. Large companies that provide services we all need to use in one way or another are sitting on monumental caches of people’s data — data that gives them enough information about you to predict what you’re going to buy, who you’re going to vote for, when you are likely to buy a house, even how many children you’re likely to have. Information that is more than enough to get a credit card, a loan, or even buy a house in your name.

You may be ok with that. After all, they were trustworthy enough for you to give them your information in the first place, but it’s not them you need to worry about. It’s everyone else. Earlier this year, credit reporting agency Equifax lost data on 140 million of its customers in one of the biggest data breaches in history. That data is now public. We can dismiss this as a once in a decade event that could have been prevented if we’d been more careful, but it is becoming increasingly clear that data breaches like this are very hard to prevent entirely and too dangerous to tolerate. The only way to really prevent them is to not gather the data on that scale in the first place.

So, what will replace the cloud?

An internet powered largely by client-server protocols (like HTTP) and security based on trust in a central authority (like TLS), is flawed and causes problems that are fundamentally either really hard or impossible to solve. It’s time to look for something better — a model where nobody else is storing your personal data, large media files are spread across the entire network, and the whole system is entirely peer-to-peer and serverless (and I don’t mean “serverless” in the cloud-hosted sense here, I mean literally no servers).

I’ve been reading extensively about emerging technologies in this space and have become pretty convinced that peer-to-peer is where we’re inevitably going. Peer-to-peer web technologies are aiming to replace the building blocks of the web we know with protocols and strategies that solve most of the problems I’ve outlined above. Their goal is a completely distributed, permanent, redundant data storage, where each participating client in the network is storing copies of some of the data available in it.

Above: Source: Wikimedia Commons (http://ift.tt/2xzBAaf)

If you’ve heard about BitTorrent, the following should all sound familiar. In BitTorrent, users of the network share large data files split into smaller blocks (each with a unique ID) without the need for any central authority. In order to download a file, all you need is a “magic” number — a hash — a fingerprint of the content. The BitTorrent client will then find peers that have pieces of the file and download them, until you have all the pieces.

The interesting part is how the peers are found. BitTorrent uses a protocol called Kademlia for this. In Kademlia, each peer on the network has a unique ID number, which is of the same length as the unique block IDs. It stores a block with a particular ID on a node whose ID is “closest” to the ID of the block. For random IDs of both blocks and network peers, the distribution of storage should be pretty uniform across the network. There is a benefit, however, to not choosing the block ID randomly and instead using a cryptographic hash — a unique fingerprint of the content of the block itself. The blocks are content-addressable. This also makes it easy to verify the content of the block (by re-calculating and comparing the fingerprint) and provides the guarantee that given a block ID, it is impossible to download any other data than the original.

The other interesting property of using a content hash for addressing is that by embedding the ID of one block in the content of another, you link the two together in a way that can’t be tampered with. If the content of the linked block is changed, its ID would change and the link would be broken. If the embedded link is changed, the ID of the containing block would change as well.

This mechanism of embedding the ID of one block in the content of another makes it possible to create chains of such blocks (like the blockchain powering Bitcoin and other cryptocurrencies) or even more complicated structures, generally known as Directed Acyclic Graphs, or DAGs for short. (This kind of link is called a Merkle link after the inventor Ralph Merkle. So if you hear someone talking about Merkel DAGs, you know roughly what they are.) One common example of a Merkle DAG is git repositories. Git stores the commit history and all directories and files as blocks in a giant Merkle DAG.

And that leads us to another interesting property of distributed storage based on content-addressing: It’s immutable. The content cannot change in place. Instead, new revisions are stored next to existing ones. Blocks that have not changed between revisions get reused, because they have, by definition, the same ID. This also means identical files cannot be duplicated in such a storage system, translating into efficient storage. So on this new web, every unique cat picture will only exist once (although in multiple redundant copies across the swarm).

Protocols like Kademlia together with Merkle chains and Merkle DAGs give us the tools to model file hierarchies and revision timelines and share them in a large scale peer-to-peer network. There are already protocols that use these technologies to build a distributed storage that fits our needs. One that looks very promising is IPFS.

The problem with names and shared things

Ok, so with the above techniques, we can solve quite a few of the problems I outlined at the beginning: We get distributed, highly redundant storage on devices connected to the web that can keep track of the history of files and keep all the versions around for as long as they are needed. This (almost) solves the availability, capacity, permanence, and content verification problem. It also addresses bandwidth problems — peers send data to each other, so there are no major hotspots/bottlenecks.

We will also need a scalable compute resource, but this shouldn’t be too difficult: Everyone’s laptops and phones are now orders of magnitude more powerful than what most apps need (including fairly complex machine learning computations), and compute is generally pretty horizontally scalable. So as long as we can make every device do the work necessary for its user, there shouldn’t be a major problem.

So now that cat image I want to see on Slack can come from one of my coworkers sitting next to me instead of from the Slack servers (and without crossing any oceans in the process). In order to post a cat picture, though, I need to update a channel in place (i.e., the channel will no longer be what it was before my message, it will have changed). This fairly innocuous sounding thing turns out to be the hard part. (Feel free to skip to the next section if this bit gets too technical.)

The hard part: Updating in place

The concept of an entity that changes over time is really just a human idea to give the world some order and stability in our minds. We can also think about such an entity as just an identity — a name — that takes on a series of different values (which are static, immutable) as time progresses (Rich Hickey explains this really well in his talks Are we there yet? and The value of values). This is a much more natural way of modelling information in a computer, with more natural consequences. If I tell you something, I can no longer change what I told you, or make you unlearn it. Facts, e.g. who the President of the United States is, don’t change over time; they just get superseded by other facts referred to by the same name, the same identity. In the git example, a ref (branch or tag) can point to (hold an ID and thus a value of) a different commit at different times, and making a commit replaces the value it currently holds. The Slack channel would also represent an identity whose values over time are growing lists of messages.

The real trouble is, we’re not alone in the channel. Multiple people try to post messages and change the channel, sometimes simultaneously, and someone needs to decide what the result should be.

In centralized systems, such as pretty much all current web apps, there is a single central entity deciding this “update race” and serializing the events. Whichever event reaches it first wins. In a distributed system, however, everyone is an equal, so there needs to be a mechanism that ensures the network reaches a consensus about the “history of the world.”

Consensus is the most difficult problem to solve for a truly distributed web supporting the whole range of applications we are using to today. It doesn’t only affect concurrent updates, but also any other updates that need to happen “in-place” — updates where the “one source of truth” is changing over time. This issue is particularly difficult for databases, but it also affects other key services, like the DNS. Registering a human name for a particular block ID or series of IDs in a decentralized way means everyone involved needs to agree about a name existing and having a particular meaning, otherwise two different users could see two different files under the same name. Content-based addressing solves this for machines (remember a name can only ever point to one particular piece of matching content), but not humans.

A few major strategies exist for dealing with distributed consensus. One of them involves selecting a relatively small “quorum” of managers with a mechanism for electing a “leader” who decides the truth (if you’re interested, look at the Paxos and Raft protocols). All changes then go through the manager. This is essentially a centralized system that can tolerate a loss of the central deciding entity or an interruption (a “partition”) in the network.

Another approach is a proof-of-work based system like Bitcoin blockchain, where consensus is ensured by making peers solve a puzzle in order to write an update (i.e. add a valid block to a Merkle chain). The puzzle is hard to solve but easy to check, and some additional rules exist to resolve a conflict if it still happens. Several other distributed blockchains use a proof-of-stake based consensus while reducing the energy demands required to solve a puzzle. If you’re interested, you can read about proof of stake in this whitepaper by BitFury.

Yet another approach for specific problems revolves around CRDTs — conflict-free replicated data types, which, for specific cases, don’t suffer from the consensus problem at all. The simplest example is an incrementing counter. If all the updates are just “add one,” as long as we can make sure each update is applied just once, the order doesn’t matter and the result will be the same.

There doesn’t seem to be a clear answer to this problem just yet and there may never be only one, but a whole lot of clever people are working on it, and there are already a lot of interesting solutions out there to pick from. You just need to select the particular trade-off you can afford. The trade-off generally lies in the scale of a swarm you’re aiming for and picking a property of the consensus you’re willing to let go of at least a little — availability or consistency (or, technically, network partitioning, but that seems difficult to avoid in a highly distributed system like the ones we’re talking about). Most applications seem to be able to favor availability over immediate consistency — as long as the state ends up being consistent in reasonable time.

Privacy in the web of public files

One obvious problem that needs addressing is privacy. How do we store content in the distributed swarm of peers without making everything public? If it’s enough to hide things, content addressed storage is a good choice, since in order to find something, you need to know the hash of its content (somewhat like private Gists on Github). So essentially we have three levels of privacy: public, hidden, and private. The answer to the third one, it seems, is in cryptography — strongly encrypting the stored content and sharing the key “out of band” (e.g. physically on paper, by touching two NFC devices, by scanning a QR code, etc.).

Relying on cryptography may sound risky at first (after all, hackers find vulnerabilities all the time), but it’s actually not that much worse than what we do today. In fact, it’s most likely better in practice. Companies and governments generally store sensitive data in ways that aren’t shareable with the public (including the individuals the data is about). Instead, it’s accessible only to an undisclosed number of people employed by the organizations holding the data and is protected, at best, by cryptography based methods anyway. More often than not, if you can gain access to the systems storing this data, you can have all of it.

But if we move instead to storing private data in a way that’s essentially public, we are forced to protect it (with strong encryption) so that it is no good to anyone who gains access to it. This idea is roughly the same as the one behind making security-related software open source so that anyone can look at it and find problems. Knowing how the security works shouldn’t help you break it.

An interesting property of this kind of access control is that once you’ve granted someone access to some data, they will have it forever for that particular revision of the data. You can always change the encryption key for future revisions, of course. This is also no worse than what we have today, even though it may not be obvious: Given access to some data, anyone can always make a private copy of it.

The interesting challenge in this area is coming up with a good system of establishing and verifying identities and sharing private data among a group of people that needs to change over time, e.g. a group of collaborators on a private git repository. It can definitely be done with some combination of private-key cryptography and rotating keys, but making the user experience smooth is likely going to be a challenge.

From the cloud to a … fog

Hard problems to solve notwithstanding, our migration away from the cloud will be quite an exciting future. First, on the technical front, we should get a fair number of improvements out of a peer-to-peer web. Content-addressable storage provides cryptographic verification of content itself without a trusted authority, hosted content is permanent (for as long as any humans are interested in it), and we should see fairly significant speed improvements, even at the edges in the developing world (or even on another planet!), far away from data centers.

At some point even data centers may become a thing of the past. Consumer devices are getting so powerful and ubiquitous that computing power and storage (a computing “substrate”) is almost literally lying in the streets.

For businesses running web applications, this change should translate to significant cost savings and far fewer headaches building reliable digital products. Businesses will also be able to focus less on downtime risk mitigation and more on adding customer value, benefitting everyone. We are still going to be a need for cloud hosted servers, but they will only be one of many similar peers. We could also see heterogeneous applications, where not all the peers are the same — where there are consumer-facing peers and back office peers as part of the same application “swarm” and the difference in access is only in access level based on cryptography.

The other large benefit for both organizations and customers is in the treatment of customer data. When there’s no longer any need to centrally store huge amounts of customer information, there’s less risk of losing such data in bulk. Leaders in the software engineering community (like Joe Armstrong, creator of Erlang, whose talk from Strange Loop 2014 is worth a watch) have long argued that the design of the internet where customers send data to programs owned by businesses is backwards and that we should instead send programs to customers to execute on their privately held data that is never directly shared. Such a model seems much safer and doesn’t in any way prevent businesses from collecting useful customer metrics they need.

And nothing prevents a hybrid approach with some services being opaque and holding on to private data.

This type of application architecture seems a much more natural way to do large scale computing and software services — an Internet closer to the original idea of open information exchange, where anyone can easily publish content for everyone else and control over what can be published and accessed is exercised by consensus of the network’s users, not by private entities owning servers.

This, to me, is hugely exciting. And it’s why I’d like to get a small team together and, within a few weeks, build a small, simple proof of concept mobile application, using some of the technologies mentioned above, to show what can be done with the peer-to-peer web. The only current idea I have that is small enough to build relatively quickly and interesting enough to demonstrate the properties of such approach is a peer-to-peer, truly serverless Twitter clone, which isn’t particularly exciting.

If you’ve got a better idea (which isn’t too hard!), or if you have anything else related to peer-to-peer distributed web to talk about, please tweet at me; I’d love to hear about it!

Viktor Charypar is a Tech Lead at UK-based digital consultancy Red Badger.

via VentureBeat http://ift.tt/2y3loKF

Is AR Good 4 Teaching & Learning? Or should we get real?

Augmented Reality is nothing new for youth. It has been a part of student’s social experience in apps like Snapchat and it made a big splash when Pokemon Go made its debut. But when it comes to learning, does it have a place?

While seeing an object, insect, or animal up close in an augmented reality is certainly preferably to reading about it in your science text, is it really the best way to help students learn?

Is learning via AR it better than that?

Well, yeah. Probably. It will engage kids with the wow factor for a bit, but then what?

And what about the source? Who wants us to buy into this? A textbook provider? A publisher? A testing company? A hardware or software provider?

What’s in it for them?

And, what about all the other ways to learn? Is it better than that? Is it cost effective?

AR: The Verdict? It depends.

When compared to textbooks, most would agree that AR improves upon the learning experience. It can also help make a textbook a bit more interactive and give it some life.

But what about other options? A powerful novel? A game? A MagniScope? A PBS documentary? A YouTube expert?

To help think about this, I turned to my friends at Modern Learners for some insights.
When thinking about AR, VR, mixed reality, and etc, Gary Stager, asks, are we “investing in reality first” before we invest in such technologies?

That’s a good question. Especially for kids who live in big cities like where I work. In New York City we have cultural neighbourhoods, experiences, some of the finest museums, zoos, gardens, and experts right in the backyard of our schools. Are we taking students there? Or if we aren’t in such communities, are we using resources like Facebook Live, Periscope, and Skype to connect and interact with real people and places in other parts of the world?

When I served as a library media specialist in an inner city school in Harlem, we had immersive experiences in places like Chinatown, Little Italy, and Spanish Harlem. We visited places like El Museo Del Bario and the Tenement Museum. We had scavenger hunts around the neighbourhoods and the museums were happy to freely open their doors to our inner city youth visiting on weekdays.

Of course there are times when a real experience can not occur in place of a virtual experience. For example, a trip to Mars or the Titanic are out of reach. Engaging in or witnessing a dangerous activity for a newbie such as driving a car, plane, train, are other examples.

But even with such extremes, there may be a movie, field trip, game, or museum experience that might provide a better learning experience.

In his Modern Learners podcast Will Richardson puts it this way. If for some reason we really can’t invest in realities, then yes, these “halfway measures for poor kids” make sense, but only if it really is not possible to bring students more authentic opportunities.

But let’s make sure those real experiences are not available before jumping into augmented ones.

Consider this…

When trying to determine what is best for students, here are some questions you can ask:

  • How would a student use this outside of school?

  • Does it help a young person create agency over learning?

  • Does this have a real-life use?

  • Is this better than…

  • Reading about it?

  • Watching it?

  • Doing it?

When you consider those questions, you will be better positioned to determine and explain if augmented reality should become a reality for the students where you teach.

via Lisa Nielsen: The Innovative Edu… http://ift.tt/2yI8Xax

10 Reasons Kids Should Learn to Code

Learning about Computational Thinking, often referred to as coding (which is really the “written” part of process), is a new literacy that is overlooked for myriad reasons: “It’s too hard”, “I don’t understand it so, it will be impossible to teach”, “It doesn’t fit into any curricular area”, “There is no math in it at all”, “It’s just not appropriate for little ones”. I’ve pretty much heard the gamut of reasons why this process, not dissimilar to Design Thinking or Inquiry processes taking placing in Making/Tinkering and STEAM environments, is not viable in classrooms today. The reality is that computation thinking is a YAIEP or Yet Another Inquiry Entry Point. This should be a comforting thing for most. Inquiry and more recently Design Thinking are processes have been used extensively in the STEAM and Maker Movements that has swept educational institutions. These programs feature pedagogy that empower students to take more responsibility for their learning pathway; directing their learning through questions and personal perspectives; try to find and solve unique problems that have meaning and importance them; collaborating together to makes sense of data collected; communicating with authentic audiences and experts to share and obtain information; demonstrate their understandings in unique ways. This is Computational Thinking at it’s best as well. But there are added benefits as well and the article highlights these beautifully….  (Keith Strachan)


Word Splash of Coding Words

10 Reasons Kids Should Learn to Code

When it comes to preparing your children for the future, there are few better ways to do so than to help them learn to code! Coding helps kids develop academic skills, build qualities like perseverance and organization, and gain valuable 21st century skills that can even translate into a career. From the Tynker blog, here are the top 10 reasons kids should learn to code:

Coding Improves Academic Performance

  1. Math: Coding helps kids visualize abstract concepts, lets them apply math to real-world situations, and makes math fun and creative!
  2. Writing: Kids who code understand the value of concision and planning, which results in better writing skills. Many kids even use Tynker as a medium for storytelling!
  3. Creativity: Kids learn through experimentation and strengthen their brains when they code, allowing them to embrace their creativity.
  4. Confidence: Parents enthusiastically report that they’ve noticed their kids’ confidence building as they learn to problem-solve through coding!

Coding Builds Soft Skills

  1. Focus and Organization: As they write more complicated code, kids naturally develop better focus and organization.
  2. Resilience: With coding comes debugging – and there’s no better way to build perseverance and resilience than working through challenges!
  3. Communication: Coding teaches logical communication, strengthening both verbal and written skills. Think about it: learning code means learning a new language!

Coding Paves a Path to the Future

  1. Empowerment: Kids are empowered to make a difference when they code – we’ve seen Tynkerers use the platform to spread messages of tolerance and kindness!
  2. Life Skills: Coding is a basic literacy in the digital age, and it’s important for kids to understand – and be able to innovate with – the technology around them.
  3. Career Preparation: There’s a high demand for workers in the tech industry; mastering coding at a young age allows kids to excel in any field they choose!

Tynker makes it fun and easy for kids to learn how to code! Kids use Tynker’s visual blocks to begin learning programming basics, then graduate to written programming languages like Python, Javascript, and Swift. Our guided courses, puzzles, and more ensure that every child will find something that ignites their passion for learning. Explore our plans and get your child started coding today!

via www.tynker.com http://ift.tt/2i2cGVZ

Supporting Students Efforts in Determining Real from Fake News

Our students use the web every day—shouldn’t we expect them to do better at interpreting what they read there? Perhaps, but not necessarily. Often, stereotypes about kids and technology can get in the way of what’s at stake in today’s complex media landscape. Sure, our students probably joined Snapchat faster than we could say “Face Swap,” but that doesn’t mean they’re any better at interpreting what they see in the news and online.

As teachers, we’ve probably seen students use questionable sources in our classrooms, and a recent study from the Stanford History Education Group confirms that students today are generally pretty bad at evaluating the news and other information they see online. Now more than ever, our students need our help. And a big part of this is learning how to fact-check what they see on the web.

In a lot of ways, the web is a fountain of misinformation. But it also can be our students’ best tool in the fight against falsehood. An important first step is giving students trusted resources they can use to verify or debunk the information they find. Even one fact-checking activity could be an important first step toward empowering students to start seeing the web from a fact-checker’s point of view.

Here’s a list of fact-checking resources you and your students can use in becoming better web detectives.

FactCheck.org

A project of the Annenberg Public Policy Center at the University of Pennsylvania, the nonpartisan, nonprofit FactCheck.org says that it “aims to reduce the level of deception and confusion in U.S. politics.” Its entries cover TV ads, debates, speeches, interviews, and news releases. Science teachers take note: The site includes a feature called SciCheck, which focuses on false and misleading scientific claims used for political influence. Beyond individual entries, there also are articles and videos on popular and current topics in the news, among a bevy of other resources.

PolitiFact

From the independent Tampa Bay Times, PolitiFact tracks who’s telling the truth—and who isn’t—in American politics. Updated daily, the site fact-checks statements made by elected officials, candidates, and pundits. Entries are rated on a scale that ranges from “True” to “Pants on Fire” and include links to relevant sources to support each rating. The site’s content is written for adult readers, and students may need teachers’ help with context and direction.

Snopes

The popular online resource Snopes is a one-stop shop to fact-check internet rumors. Entries include everything from so-called urban legends to politics and news stories. Teachers should note that there’s a lot here on a variety of topics—and some material is potentially iffy for younger kids. It’s a great resource for older students—if you can keep them from getting distracted.

OpenSecrets.org

OpenSecrets.org is a nonpartisan organization that tracks the influence of money in U.S. politics. On the site, users can find informative tutorials on topics such as the basics of campaign finance—not to mention regularly updated data reports and analyses on where money has been spent in the American political system. While potentially useful for fact-finding, the site is clearly intended for more advanced adult readers and is best left for older students and sophisticated readers.

Internet Archive Wayback Machine

This one isn’t a site that performs fact-checking. Instead, the Internet Archive Wayback Machine is a tool you can use yourself to fact-check things you find online. Like an internet time machine, the site lets you see how a website looked, and what it said, at different points in the past. Want to see Google’s home page from 1998? Yep, it’s here. Want to see The New York Times’ home page on just about any day since 1996? You can. While they won’t find everything here, there’s still a lot for students to discover. Just beware: The site can be a bit of a rabbit hole—give students some structure before they dive in, because it’s easy to get lost or distracted.

Want to take your students’ knowledge of fact-checking a step further? Engage them in discussions around why these sites and organizations are seen as trusted (and why others might not be trusted as much). Together, look into how each site is funded, who manages it, and how it describes its own fact-checking process.

via Edutopia http://ift.tt/2yiJzak