STEM in America: Data, Approaches, and Questions (Part II)

trends in pursuit of engineering degrees 2002-2011 by race and ethnicity

If you missed Part I of this series, find it here.

Part II: Trends

In Part I, we looked at some numbers: majors and graduates in US four-year institutions in and out of STEM fields. Those data gave us an overview of the state of STEM education in 2012 – who’s pursuing it (mostly men, to varying degrees depending on race and ethnicity) and who isn’t (mostly women across all race and ethnicity groups).

In this segment, we have two objectives. First, I’d like to provide a little more context for the discussion by looking at the value of STEM versus non-STEM degrees for the graduates who hold them. Then, we’ll look at data on higher education in the US from 2002 and 2012 to set up a longer perspective on how disparities have been changing (if indeed they have). In our efforts to make college more accessible to all Americans, have we been making progress? And when we look at STEM in particular, do we see similar patterns? Or are disparities in STEM widening when compared to higher education as a whole? Continue reading

STEM in America: Data, Approaches, and Questions (Part I)

Our economy needs STEM skills.

Not only does it need STEM skills, but it rewards people who have them – significantly more than those who don’t.

As educators, we have two major responsibilities on the STEM front:

  • Create STEM exposure opportunities for students who might lack access, and
  • Make those opportunities as rich and engaging as possible.

Determining how to do those things successfully and on a large scale (and not just on a case-by case basis) requires looking at some large-scale data – some of which we have, and some of which we don’t.

This multi-part series will look at some of the data we have, some of the problems those data suggest, some solutions currently in use and development, and where to look to answer our next questions.

Part I: The State of STEM

For reference, in 2012 the US resident population of 18-24 years old was 63% white, 5% Asian and Pacific Islander, 12% black, 17% Hispanic or Latino, less than 1% (0.7%) Native American/Alaskan Native, and 2% mixed-race (non-Hispanic). [Note: to my knowledge, all race and ethnicity data is self-reported, and Hispanic/Latino may refer to any race. Some citizens identify as more than one race or ethnicity, and percentages may not add up to 100 in all data sets.] Data used for the charts in this section comes from the NSF, and is freely available here. (Thanks, NSF!)

The 4-year college and university population in 2012 looked like this:Pie chart showing US 4-year college population by race, ethnicity, sex, and citizenship Continue reading

What Games do Teachers Play?

It’s getting easier to find statistics about how many students are playing games and even what game genres and specific titles they play. But data about what teachers are playing? Not so easy to find.

The Joan Ganz Cooney Center has organized teacher surveys to gather data about which teachers use games, and how (Games & Learning reports on game use in class). But they’ve also asked some questions about teachers’ personal recreational gameplay, and that’s what I’m interested in today. After all, it’s hard to imagine asking someone with no interest in books to inspire kids to have rich reading experiences; if we’re going to expect teachers to use games in their classrooms — and increasingly, we do — shouldn’t we invest in the gaming lives of teachers?

Data Theory Play Llamas in RPG costumes Continue reading

How Game-Savvy are our Students?

Our students are playing video games.

A lot of video games, in fact.

According to a Pew study of 2008 data, 97% of American 12-17 year-olds play digital games on a computer, console, or mobile device. That’s 99% of boys, and 94% of girls.

pew games study finding: 97% of teens play digital gamesThe Pew study involved over a thousand kids aged 12 to 17. While the 97% statistic may not come as a surprise, the study abounds in interesting, less-expected findings and I recommend reading the whole report. That said, a few points stood out in particular. If you’re considering using digital games in your program, you’ll want to get to know this data. Continue reading

Convincing Data: What Makes Learning Games Work Better? [Infographic]

Do you need to make a case for using learning games in your classroom or school? Data is your friend. Though studies on the benefits of learning with games are still scarce, the Gates-Foundation-funded GlassLab (“Glass” is for Games and Learning Assessment) managed to put together a terrific meta-analysis of 69 studies measuring the effects of digital games on learning, involving a total of 6,868 unique participants. And it’s available to the public! This infographic sums up the principal findings:

GlassLab Meta-Analysis games and learning effects study infographic

Not only does the meta-analysis ask whether or not games have an effect on learning outcomes (they do), but it also looks at some of the factors that make games better or worse at helping you teach. I highly recommend reading the study summary on their website – it’s well-written, and the methodology is clearly explained, along with many subtleties the infographic doesn’t convey.

Would You Like Some Data With That Learning Game?

UPDATE, JULY 5 2015:

I went looking again for the research paper when I found that the pre-publication draft was no longer available through BrainQuake. A new version of the results is now downloadable through their “Backed by Science” page; after taking a look at it, I must disappointedly confess that I find it to be deliberately misleading regarding the kinds of conclusions that can be drawn from the study. It isn’t simply that language such as “dramatic math learning results that no one had believed were possible” are outlandish overstatements. It is hand-waving over the definition of “comparison group,” and corresponding outright dishonesty about the study’s rigor.


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