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Troy Patterson

8 Ways to Level Up Game Based Learning in the Classroom - 0 views

www.coolcatteacher.com/ased-learning-in-the-classroom

msm315 gaming

shared by Troy Patterson on 21 Nov 15 - No Cached
  • 1. Make Your Whole Class a Game Experience
  • 2. Engage with Minecraft: Let Kids Build in the Sandbox
  • 4. Play Games for Social Good: Have a Point, Don’t Just Earn Them
  • ...5 more annotations...
  • 3. Build a Game Experience into Learning: Live It and Learn It
  • 5. Game Based Platforms for Learning
  • 6. Experience Learning: Immerse Yourself in the Experience
  • 7. Go Offline or Outside: You Don’t Need Tech to Teach
  • 8. Create Solutions as You Learn: Gifts from the Hour of Code
Shawn McGirr

Arcademic Skill Builders: Online Educational Games - 4 views

www.arcademics.com

ISTE12 games interactive math

shared by Shawn McGirr on 02 Jul 12 - No Cached
  • Shawn McGirr on 02 Jul 12
     
    Academic games that track performance.
Ron King

Fourth Down Bot - N.F.L. Coverage - The New York Times - 0 views

www.nytimes.com/...fourth-downs

shared by Ron King on 06 Dec 13 - No Cached
  • Ron King on 06 Dec 13
     
    For every fourth down of every N.F.L. game in the 2013 season, NYT 4th Down Bot analyzes over 10 years of N.F.L. game data to determine whether a team would have been better off had it punted, attempted a field goal or gone for a first down. In general, it finds N.F.L. coaches far too conservative.
Troy Patterson

The Sabermetrics of Effort - Jonah Lehrer - 0 views

www.jonahlehrer.com/...the-sabermetrics-of-effort

msm299 blog.me

shared by Troy Patterson on 17 Feb 15 - No Cached
  • The fundamental premise of Moneyball is that the labor market of sports is inefficient, and that many teams systematically undervalue particular athletic skills that help them win. While these skills are often subtle – and the players that possess them tend to toil in obscurity - they can be identified using sophisticated statistical techniques, aka sabermetrics. Home runs are fun. On-base percentage is crucial.
  • The wisdom of the moneyball strategy is no longer controversial. It’s why the A’s almost always outperform their payroll,
  • However, the triumph of moneyball creates a paradox, since its success depends on the very market inefficiencies it exposes. The end result is a relentless search for new undervalued skills, those hidden talents that nobody else seems to appreciate. At least not yet.
  • ...14 more annotations...
  •  One study found that baseball players significantly improved their performance in the final year of their contracts, just before entering free-agency. (Another study found a similar trend among NBA players.) What explained this improvement? Effort. Hustle. Blood, sweat and tears. The players wanted a big contract, so they worked harder.
  • If a player runs too little during a game, it’s not because his body gives out – it’s because his head doesn’t want to.
  • despite the obvious impact of effort, it’s surprisingly hard to isolate as a variable of athletic performance. Weimer and Wicker set out to fix this oversight. Using data gathered from three seasons and 1514 games of the Bundesliga – the premier soccer league in Germany – the economists attempted to measure individual effort as a variable of player performance,
  • So did these differences in levels of effort matter? The answer is an emphatic yes: teams with players that run longer distances are more likely to win the game,
  • As the economists note, “teams where some players run a lot while others are relatively lazy have a higher winning probability.”
  • There is a larger lesson here, which is that our obsession with measuring talent has led us to neglect the measurement of effort. This is a blind spot that extends far beyond the realm of professional sports.
  • Maximum tests are high-stakes assessments that try to measure a person’s peak level of performance. Think here of the SAT, or the NFL Combine, or all those standardized tests we give to our kids. Because these tests are relatively short, we assume people are motivated enough to put in the effort while they’re being measured. As a result, maximum tests are good at quantifying individual talent, whether it’s scholastic aptitude or speed in the 40-yard dash.
  • Unfortunately, the brevity of maximum tests means they are not very good at predicting future levels of effort. Sackett has demonstrated this by comparing the results from maximum tests to field studies of typical performance, which is a measure of how people perform when they are not being tested.
  • As Sackett came to discover, the correlation between these two assessments is often surprisingly low: the same people identified as the best by a maximum test often unperformed according to the measure of typical performance, and vice versa.
  • What accounts for the mismatch between maximum tests and typical performance? One explanation is that, while maximum tests are good at measuring talent, typical performance is about talent plus effort.
  • In the real world, you can’t assume people are always motivated to try their hardest. You can’t assume they are always striving to do their best. Clocking someone in a sprint won’t tell you if he or she has the nerve to run a marathon, or even 12 kilometers in a soccer match.
  • With any luck, these sabermetric innovations will trickle down to education, which is still mired in maximum high-stakes tests that fail to directly measure or improve the levels of effort put forth by students.
  • After all, those teams with the hardest workers (and not just the most talented ones) significantly increase their odds of winning.
  • Old-fashioned effort just might be the next on-base percentage.
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