Integrated R labs for high school students by Amelia McNamara.
From the webpage:
Amelia McNamara, James Molyneux, Terri Johnson
- Project website: http://www.mobilizingcs.org/
- Curriculum page: http://www.mobilizingcs.org/curriculum
- GitHub organization: https://github.com/mobilizingcs
- mobilizr package: https://github.com/mobilizingcs/mobilizr
- Lab code: https://github.com/mobilizingcs/ids_labs
- Wiki: https://wiki.mobilizingcs.org/
- Video of teachers and students: http://stemforall2016.videohall.com/presentations/790
This looks like a very promising approach for capturing the interests of high school students in statistics and R.
From the larger project, Mobilize, curriculum page:
Mobilize centers its curricula around participatory sensing campaigns in which students use their mobile devices to collect and share data about their communities and their lives, and to analyze these data to gain a greater understanding about their world.Mobilize breaks barriers by teaching students to apply concepts and practices from computer science and statistics in order to learn science and mathematics. Mobilize is dynamic: each class collects its own data, and each class has the opportunity to make unique discoveries. We use mobile devices not as gimmicks to capture students’ attention, but as legitimate tools that bring scientific enquiry into our everyday lives.
Mobilize comprises four key curricula: Introduction to Data Science (IDS), Algebra I, Biology, and Mobilize Prime, all focused on preparing students to live in a data-driven world. The Mobilize curricula are a unique blend of computational and statistical thinking subject matter content that teaches students to think critically about and with data. The Mobilize curricula utilize innovative mobile technology to enhance math and science classroom learning. Mobilize brings “Big Data” into the classroom in the form of participatory sensing, a hands-on method in which students use mobile devices to collect data about their lives and community, then use Mobilize Visualization tools to analyze and interpret the data.
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I like the approach of having the student collect their own and process their own data. If they learn to question their own data and processes, hopefully they will ask questions about data processing results presented as “facts.” (Since 2016 is a presidential election year in the United States, questioning claimed data results is especially important.)
Enjoy!