Government Elearning! Magazine

DEC 2015 - JAN 2016

Elearning! Magazine: Building Smarter Companies via Learning & Workplace Technologies.

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Page 19 of 51

20 Winter 2016 Government Elearning! 2 sigma problem and teaching entities, as well as where we have an opportunity to provide more solu- tions. If students get lost in a subject, and they can't get help, that teaching methodol- ogy will be plagued with dropouts, cheat- ing, or just plain lack of performance on the student side of the equation. Tere are a lot of other solutions starting to take form in peer collaboration, reinforce- ment, and other areas that technology can enable. If you read through the Bloom article that I linked to and under the frst fgure, you're going to see a table (above). Te table shows all of the variables that produce more than a 0.2 sigma efect on student achieve- ment. Each of those variables can be a poten- tial "player" in our pursuit of a technology- enabled solution that moves us closer to solving the 2 Sigma Problem. Take reinforcement provided by the teacher — number 2 on the list, afer one- on-one mentoring. Tat one seems undo- able when you have a large class size and the burden falls on a single instructor. But there are some other technology- enabled solutions that help deal with re- inforcement, if you broaden the notion of reinforcement. An example of an alterna- tive learning reinforcement tool is a prod- uct called Trivie. It's based on the old game, Trivial Pursuit. In the corporate learning world, Trivie uses technology to send out questions to students both during and af- ter a training scenario. As the student gets each answer right, he or she is rewarded with points or positive comments or other incentives that you stipulate. And we all know what incentives (like scoring points) mean to us in the world of gamifcation: "peer envy" (which is also "sort of " on the list as "peer group infuence"). Tis kind of game and learning rein- forcement technology can use leader- boards, prescriptive paths when wrong answers are given, or predefned coaching prods if you don't get the right answer. And some even employ some sort of electronic "Great job!" to reinforce your learning. A 1.2 sigma impact. Not bad, even if it does sound a little hokey. But I think we've only just begun our quest. If you've been wondering why I've been giv- ing so much air time to AI (artifcial intel- ligence) and the Internet of Tings (IoT), it's because I think this technology will eventu- ally lead us to the "holy grail" solution. Do you remember the youngest keynote speaker we every had at one of our ELCE conferences? Her name was Bina48. She was 2-1/2 years old back in 2010, and she was the most advanced social robot in existence then. You could ask her questions, and her AI framework allowed her to interact with you in conversational mode. To me, she was the precursor of the personal tutor — espe- cially when the inventor thought she would one day sell for less than an iPhone at Best Buy. Tat price point could potentially mean a personal tutor in every home. Well guess where AI is coming on strong: it's your phone. You can now ask ques- tions like, "How do you say 'Holy Grail' in French?" Or you can ask Alexa, Amazon's new toy, "How tall is Mount Everest?" Or "How do you spell 'supercalifragilisticexpiali- docious?" All of this is thanks to AI algorithms. So at least I'm convinced that in the next decade we're going to be able to solve the elusive "2 Sigma Problem" with AI. And if a Bina48-lookalike is not by your side answering your every informational need, maybe it'll be a wearable device that will provide your coaching, tutoring and men- toring: "You're not in the correct position to lif that much weight." Or it might just be integrated into your work environment or equipment like navi- gation is now integrated into your car. "In 800 feet, make a U-turn to proceed to your destination," without the added attraction of having a critical passenger adding the rest of the sentence: "…Dummy!'" —Sources: readings/bloom-two-sigma.pdf, http:// prweb13121657.htm, http://blog.udacity. com/2013/09/meet-udacity-coach-for-intro- ductory.html, V8yk, TABLE 1. Effect of selected alterable variables on student achievement Effect size Percentile equivalent D a Tutorial instruction 2.00 98 D Reinforcement 1.20 A Feedback-corrective (ML) 1.00 84 D Cues and explanations 1.00 (A)D Student classroom participation 1.00 A Student time on taxk 1.00 b A Improved reading/study skills 1.00 C Cooperative learning .80 79 D Homework (graded) .80 D Classroom morale .60 73 A Initial cognitive prerequisites .60 C Home environment intervention .50 b 69 D Homework (assigned) .30 62 D Higher order questions .30 (D)B New science & math curricula .30 b D Teacher expentancy .30 C Peer group infuence .20 58 B Advance organizers .20 Socio-economic status (for contrast) .25 60 Note. This table was adapted from Walberg (1984) by Bloom. Column 1: Object of change process–A-Learning, B-Instructional Material; C-Home environment or peer group; D-Teacher Column 3: Averaged or estimated from correlational data or fram several effect sizes.

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