How statistics education has evolved just in the last ten years is quite remarkable. Contained in this repository are examples of how one educator has changed the way statistics is taught at the introductory level - with very little prior math background assumed. But we have a long way to go in order to convince the thousands of statistics teachers that still resist change.
Link to talk slides: https://mrshrbrmstr.github.io/RStudioConf2019/pres/Marys_slides-rev/assets/player/KeynoteDHTMLPlayer.html#35
Apparently I am one of the few mathematicians that can say yes to all of the following:
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As math department chair at a community college, I created the nation's first Certificate in Data Science
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Educational background includes pure mathematics (PhD level), statistics and undergraduate computer science
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Helped develop white papers on Data Science curriculum in the 21st century:
South Big Data Hub Report https://drive.google.com/file/d/14l_PGq4AxOP9fhJbKqA2necsJZ-gdiKV/view
Summit on Data Science at Two-Year Colleges https://www.amstat.org/ASA/Education/Two-Year-College-Data-Science-Summit.aspx
Okay, so I am the only mathematician that can say yes to all of the above. Read on.
In the vast majority of educational institutions, "the handheld calculator", priced anywhere from $60 to $160, is still the tool that students and their teachers are using all across America. One only needs to take a look at the manuals and instructions for "doing" statistics on these devices and quickly the claim that "there is too much of a learning curve" for R begins to look weak. It is included here for comparison and to show what the majority of America is "comfortable" with.
For a long time, this was considered to be the "Grown Up" in the room. When I taught Intro Statistics at a community college this was the software that was already part of a student's repertoire. Facing facts, "Excel skills" still looks good on a resume. I happen to be among those that believe Excel is still useful for some things. Having basic knowledge in Excel is definitely still a plus. In this folder are templates that horrifyingly were part of statistics education (perhaps still are in some parts).
Community college statistics teachers are mostly adjuncts, not getting paid for the work they do outside the classroom. The big publishing houses - Cengage and Pearson namely - offer teachers the ability to create their entire syllabus with online applications-driven homework and assessment in an online platform that even does the grading. In this folder is a sample lesson from the textbook publisher and a lesson that I created for a room of educators on the same topic.
Of note: the elephant in the room. When I go around talking about data-driven statistics education, I am greeted with this question all the time: Will students be able to do this on their smartphone? If the answer to that is anything but "yes" I lose the audience.
AMATYC stands for "American Mathematical Association of Two Year Colleges" and its members consist of mathematics and statistics educators and those who wish to work with mathematics and statistics educators at the nation's two year colleges. Anyone can join. The annual conference is held in different regions of the country from year to year - always in November - and speakers come from industry, education, government & non-profit philanthropic organizations.
How R is revolutionizing statistics education in America - Shiny is making it easier to develop training materials for statistics educators. AMATYC members and others are working in collaboration, funded by an NSF grant, to provide in-person, hands on training for those whose computer expertise consists of email, web browsing, MS Office and perhaps a few other cloud-based applications.
Initially, the grant funds three years of summer workshops but there is a potential for more IF they can get this off the ground and get more people involved. The workshop is called StatPREP (http://statprep.org/) and HERE are the folks that are working to make R for Statistics at two year colleges around the country possible. http://statprep.org/the-statprep-team/
Along with their efforts I am working on a booklet that would serve as a supplement to any Intro Stats textbook that an educator might choose for their course, hence this repository.
AMATYC is partner with many of the more well-known organizations involved in the mathematical and statistics education we all experience in our institutions of higher learning. The list is here: https://amatyc.site-ym.com/page/AMATYCAffiliateOrg
The committee on statistics education is very active. It influences community college educators all across the country. Members of AMATYC can access webinars and professional development opportunities as well as the annual conference held in November each year. In this folder are two presentations I gave in 2017. Some useful links:
AMATYC Statistics Resources: https://amatyc.site-ym.com/page/StatsResources? Data Science Subcommittee: https://amatyc.site-ym.com/page/DataResources
AMATYC Conference 2017 Talk Titled "So You Wanna Build a Data Science Program" Useful for anyone at the beginning stages of creating either a certificate or associate degree program in data literacy with the purpose of creating a more data literate population.
AMATYC Conference 2017 Talk titled "Visualize This: Some Plots in R" Useful for Intro Stats lesson planning and illustration of topics like linear regression.
The course number is irrelevant. This is what many of us believe ought to be a student's introduction to data science. Not everything is in this repository, but the main projects are. The course itself, when students are gathered with the professor, ought to be largely discussion driven. It helps to have students that do the readings and work out the sample code prior to class. Ethical concerns are a large part of class discussion, as are things like debugging/error handling.
I consider myself to be somewhat of an expert on "how people learn" new things. Not just new things, but how we go from concrete to abstract concepts, and how we learn mathematical concepts - like graphing for example. If we (by "we" I mean the R Community at large) want to see R and RStudio a household name, then it is going to take grassroots effort to make that happen. Consider "Texas Instruments". How is it that every classroom and every kid that is planning to take the SAT has one of these $100 things that becomes completely useless after high school?
Answer: Provide free training for educators with free equipment, provide classroom materials free, work with Big Standardized Testing Agency - College Board - and get them to allow your platform during testing.
Since I can't do any of those things, I decided to start by making RStudio easier to use than a graphing calculator. I've written code snippets that can be copied and pasted into R Console or New R File, then by changing just a few numbers a student can have output that allows for more time to think about the meaning behind the result and less time trying to use a complicated formula or trying to look something up in a confusing table.
Browse around but be careful not to spill coffee on anything. Constructive feedback is welcome.