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Tony Richards

The Atlantic Online | January/February 2010 | What Makes a Great Teacher? | Amanda Ripley - 0 views

www.theatlantic.com/...good-teaching

what makes a good teacher good article read

shared by Tony Richards on 04 Feb 10 - Cached
  • Tony Richards on 04 Feb 10
     
    "What Makes a Great Teacher? Image credit: Veronika Lukasova Also in our Special Report: National: "How America Can Rise Again" Is the nation in terminal decline? Not necessarily. But securing the future will require fixing a system that has become a joke. Video: "One Nation, On Edge" James Fallows talks to Atlantic editor James Bennet about a uniquely American tradition-cycles of despair followed by triumphant rebirths. Interactive Graphic: "The State of the Union Is ..." ... thrifty, overextended, admired, twitchy, filthy, and clean: the nation in numbers. By Rachael Brown Chart: "The Happiness Index" Times were tough in 2009. But according to a cool Facebook app, people were happier. By Justin Miller On August 25, 2008, two little boys walked into public elementary schools in Southeast Washington, D.C. Both boys were African American fifth-graders. The previous spring, both had tested below grade level in math. One walked into Kimball Elementary School and climbed the stairs to Mr. William Taylor's math classroom, a tidy, powder-blue space in which neither the clocks nor most of the electrical outlets worked. The other walked into a very similar classroom a mile away at Plummer Elementary School. In both schools, more than 80 percent of the children received free or reduced-price lunches. At night, all the children went home to the same urban ecosystem, a zip code in which almost a quarter of the families lived below the poverty line and a police district in which somebody was murdered every week or so. Video: Four teachers in Four different classrooms demonstrate methods that work (Courtesy of Teach for America's video archive, available in February at teachingasleadership.org) At the end of the school year, both little boys took the same standardized test given at all D.C. public schools-not a perfect test of their learning, to be sure, but a relatively objective one (and, it's worth noting, not a very hard one). After a year in Mr. Taylo
Rhondda Powling

Tony Vincent's Learning in Hand - Blog - Guide to Using Free Apps to Support ... - 3 views

learninginhand.com/...-higher-order-thinking-sk.html

apps m-learning skills ipads e-learning Educational-Technology thinking

shared by Rhondda Powling on 05 Mar 12 - No Cached
  • Rhondda Powling on 05 Mar 12
     
    Links to the apps used in the activities published an eBook titled Hot Apps 4 HOTS: A Guide to Using Free Apps to Support Higher Order Thinking Skills by Lisa Johnson and Yolanda Barker. The book includes nine step-by-step activities that focus on each level of Bloom's taxonomy and includes loads of links to further resources.
emmamatthews

The Mililani high school - 26 views

The Mililani high school offers the best program in robotics for school students in Mililani, HI. We have created a friendly community that involves students, instructors, and alumni in creating ro...

started by emmamatthews on 23 Sep 21 no follow-up yet
Pam Thompson

ThinkShop: thinking skills resources: Question Starters- A4 posters as ebook - 0 views

www.thinks.co.nz/...merchant.mvc

ThinkingSkills resources

shared by Pam Thompson on 16 Dec 08 - Cached
  • rasel ahmed on 05 Jan 14
     
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Tony Searl

What is data science? - O'Reilly Radar - 1 views

radar.oreilly.com/...what-is-data-science.html

data datascience trends collaboration learning resources LAK11

shared by Tony Searl on 22 Dec 10 - Cached
  • how to use data effectively -- not just their own data, but all the data that's available and relevant
  • Increased storage capacity demands increased sophistication in the analysis and use of that data
  • Once you've parsed the data, you can start thinking about the quality of your data
  • ...20 more annotations...
  • It's usually impossible to get "better" data, and you have no alternative but to work with the data at hand
  • The most meaningful definition I've heard: "big data" is when the size of the data itself becomes part of the problem
  • Precision has an allure, but in most data-driven applications outside of finance, that allure is deceptive. Most data analysis is comparative:
  • Storing data is only part of building a data platform, though. Data is only useful if you can do something with it, and enormous datasets present computational problems
  • Hadoop has been instrumental in enabling "agile" data analysis. In software development, "agile practices" are associated with faster product cycles, closer interaction between developers and consumers, and testing
  • Faster computations make it easier to test different assumptions, different datasets, and different algorithms
  • It's easer to consult with clients to figure out whether you're asking the right questions, and it's possible to pursue intriguing possibilities that you'd otherwise have to drop for lack of time.
  • Machine learning is another essential tool for the data scientist.
  • According to Mike Driscoll (@dataspora), statistics is the "grammar of data science." It is crucial to "making data speak coherently."
  • Data science isn't just about the existence of data, or making guesses about what that data might mean; it's about testing hypotheses and making sure that the conclusions you're drawing from the data are valid.
  • The problem with most data analysis algorithms is that they generate a set of numbers. To understand what the numbers mean, the stories they are really telling, you need to generate a graph
  • Visualization is crucial to each stage of the data scientist
  • Visualization is also frequently the first step in analysis
  • Casey Reas' and Ben Fry's Processing is the state of the art, particularly if you need to create animations that show how things change over time
  • Making data tell its story isn't just a matter of presenting results; it involves making connections, then going back to other data sources to verify them.
  • Physicists have a strong mathematical background, computing skills, and come from a discipline in which survival depends on getting the most from the data. They have to think about the big picture, the big problem. When you've just spent a lot of grant money generating data, you can't just throw the data out if it isn't as clean as you'd like. You have to make it tell its story. You need some creativity for when the story the data is telling isn't what you think it's telling.
  • It was an agile, flexible process that built toward its goal incrementally, rather than tackling a huge mountain of data all at once.
  • we're entering the era of products that are built on data.
  • We don't yet know what those products are, but we do know that the winners will be the people, and the companies, that find those products.
  • They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: "here's a lot of data, what can you make from it?"
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