1) What is Data
When students are taking tests on data analysis what they see is this:
But, what they will find when they actually start using data analysis in the "real world" is this:
Our schools and universities puts Calculus at the top of the heap. Don't get me wrong, Calculus is fun, amazing, and an excellent challenge in analytical thinking. While many careers require it, many more do not. When I went to school, the "lower tracked" math class was called Statistics and Data Analysis. Therefore, I did not get any in depth understanding of this topic until college and beyond. In fact, I have heard some admissions counselors say that a student who takes statistics is less competitive than a student who took calculus! A worrisome example of how tradition trumps what's best for the student.
A gap in what kids are learning and what they need is what drove me to make these lessons.
In the Google Computational Thinking lessons and those that follow, students will learn to use math and science in a way Gailieo, Newton, and Maxwell could have only dreamed of. Up until the last 50 years, our problems have always been larger than our potential to solve them. Those that labored to solve some our world's biggest mysteries did so at great expense to their time. Think of all they could have done if they had faster methods available to solve them.
Our students today spend so much time calculating instead of analyzing that they leave the class filled with skills that they have no understanding of or use for. With Computational Thinking, students create algorithms capable of solving big problems (e.g. how to drive a car across the country, what is the optimal shape of an enzyme) and then let the computer quickly calculate it for them. This gives students time to make mistakes, refine their understanding, and most of all draw conclusions and analyze big picture questions.
Computational Thinking has 4 components (see explanation here):
2) Pattern Recognition
3) Pattern Generalization and Abstraction
4) Algorithm Design
By using these 4 practices students can do incredible things with large amounts of data, create simulations and models of their world, and begin to understand the concepts and skills through real world use.
Our hope is for these lessons to inspire you to adapt them to your own classroom and content. Some of them involve physics while others focus on biology but they can be adapted to any content even beyond science and math. Computational Thinking is not necessarily computer science. Many of these lessons require no programming experience at all, so you can get started with them right away. Of course if you want to start using programming with your students to supplement these lessons it will give them another applicable skill.
If you have resources, questions, or want to find out more you can connect with others on the Google Computational Thinking forum, there are quite a few educators out there using Computational Thinking. During my time as Google's Curriculum Fellow I plan to have G+ Hangouts, discussions, and district training to help classrooms implement Computational Thinking. There is awesome technology out there, I hope to see it being used to help students create and innovate rather than do the same things they could have done with paper and pencil.
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