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"Understanding Our Ocean Connections": NSF symposium highlights links among people and marine ecosystems

News From NSF - Mon, 03/05/2018 - 09:30

On April 19, scientists from the National Science Foundation's (NSF) Long-Term Ecological Research (LTER) Network will take part in the symposium "Understanding Our Ocean Connections."

The researchers will present findings on the connections among humans and ocean ecosystems such as coral reefs, kelp forests, mangrove forests, salt marshes, sea ice and the continental shelf.

Questions that will be answered at the symposium include: Does the future of coral reefs depend on the ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244668&WT.mc_id=USNSF_51&WT.mc_ev=click

This is an NSF News item.

What can the Uber Gender Pay Gap Study tell us about improving diversity in computing?

ComputingEd - Mon, 03/05/2018 - 07:00

The gig economy offers the ultimate flexibility to set your own hours. That’s why economists thought it would help eliminate the gender pay gap. A new study, using data from over a million Uber drivers, finds the story isn’t so simple.

Source: What Can Uber Teach Us About the Gender Pay Gap? – Freakonomics

A fascinating Freakonomics podcast tells us about why women are paid less than men (by about 7%) on Uber.  They ruled out discrimination, after looking at a variety of sources.  They found that they could explain all of that 7% from three factors.

They found that even in a labor market where discrimination can be ruled out, women still earn 7 percent less than men — in this case, roughly 20 dollars an hour versus 21. The difference is due to three factors: time and location of driving; driver experience; and average speed.

The first factor is that women choose to be Uber drivers in different places and at different times than men.  Men are far more often to be drivers at 3 am on Saturday morning. The second factor is particularly interesting to me.  Men tend to stick around on Uber longer than women, so they learn how to work the system. The third factor is that men drive faster, so they get more rides per hour.

When someone from Uber was asked about how they might respond to these results, he focused on the second factor.

But for example, you could imagine that if we make our software easier to use and we can steepen up the learning curve, then if people learn more quickly on the system, then that portion of the gap could be resolved via some kind of intervention. But that’s just an example. And we’re not there yet with our depth of understanding, to just simply write off the gender gap as a preference.

Improving learning might help shrink the gender pay gap.  Obviously, I’m connecting this to computing education here.  What role could computing education play in reducing gaps between males and females in computing?  We have reason to believe that our inability to teach programming well led to the gender gap in computing.  Could we make things better if we could teach computing well?

Here are two thoughts exploring that question.

  1. We know (e.g., from Unlocking the Clubhouse) that men tend to sink more time into programming, which can give them a lead in undergraduate education (what Jane Margolis has called ‘preparatory privilege‘).  What if we could teach programming more efficiently?  Could we close that gap?  If we had a science of teaching programming, we could improve efficiency so that a few hours of focused effort in the classroom might lead to more effective learning of tens of hours of figuring out how to compile under Debian Linux.
  2. When I first started thinking about the “phonics of computing education” and our ebooks, I was inspired by work from Caroline Simard that suggested that helping female mid-level managers keep up their technical skills could help them to progress in the tech industry.  Female mid-level managers have less time to invest in technical learning, and at the mid-level, technical education still matters.  If you have a project that needs a new toolset, you’ll more likely give it to the manager who knows that toolset.  If we could teach female mid-level technical managers more effectively and efficiently, could they make it into the C-suite of tech companies?

Maybe better computing education could be an important part of improving diversity, along multiple paths.

Improving Undergraduate STEM Education (IUSE) Program Flyer

News From NSF - Fri, 03/02/2018 - 18:48

Available Formats:
PDF: https://www.nsf.gov/pubs/2018/nsf18049/nsf18049.pdf?WT.mc_id=USNSF_25&WT.mc_ev=click

Document Number: nsf18049

This is an NSF Program Announcements and Information item.

Improving Undergraduate STEM Education (IUSE) Program Flyer

News From NSF - Fri, 03/02/2018 - 18:48

Available Formats:
PDF: https://www.nsf.gov/pubs/2018/nsf18049/nsf18049.pdf?WT.mc_id=USNSF_179

Document Number: nsf18049

This is an NSF Publications item.

The Role of Encouragement for Success in Computing Education, and how that differs by demographics

ComputingEd - Fri, 03/02/2018 - 07:00

A new report from NSF tells us a story that we’ve heard before — encouragement is a critical aspect of developing the confidence to succeed in CS. We found this in our statewide study in 2010, and Joanne Cohoon found this to be critical in her work. In our work, we found that encouragement was more critical for under-represented group.  The new Google study tells us that the encouragement is not received equally.  The important part of Joanne’s work is that the encouragement could come from teachers of any agenda.  This report is part of the growing trend to study the importance of affect in succeeding in computing education.

Students who have been told by parents or teachers they would be good at computer science (CS) are 2.5 to three times more likely to be interested in learning CS in the future, but students do not receive this encouragement equally. Additionally, despite positive perceptions about the CS field, lower personal perceptions of skills in math and science and a self-perceived low ability to learn CS may contribute to a gap in interest in CS among underrepresented groups that starts as early as age 14. This report summarizes key differences in interest in and confidence to learn CS among seventh- to 12th-grade students from underrepresented groups — girls, Black students and Hispanic students — as well as the level of encouragement to learn CS that these groups receive from key influencers such as parents and teachers, based on 2015- 2016 surveys.

Source: Google Report: Encouraging Students Toward Computer Science Learning

Astronomers detect ancient signal from first stars in universe

News From NSF - Wed, 02/28/2018 - 14:03

For additional information on this breakthrough, NSF has produced the video
"The birth of the first stars."

For the first time, astronomers have detected a signal from stars emerging in the early universe. Using a radio antenna not much larger than a refrigerator, the researchers discovered that ancient suns were active within 180 million years of the Big Bang.

The astronomers, from ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244599&WT.mc_id=USNSF_51&WT.mc_ev=click

This is an NSF News item.

NSF Fiscal Year 2019 budget to advance innovation, infrastructure

News From NSF - Wed, 02/28/2018 - 13:30

The National Science Foundation (NSF) released more detailed information regarding President Donald J. Trump's Fiscal Year (FY) 2019 NSF budget request to Congress.

The FY2019 budget request would represent a $7.47 billion investment in strengthening the nation's economy, security and global leadership through research in cutting-edge science and engineering. At this proposed level of funding, steady with FY2017 ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244676&WT.mc_id=USNSF_51&WT.mc_ev=click

This is an NSF News item.

NSF invests $30 million to pursue transformative advances at frontiers of computing and information science

News From NSF - Tue, 02/27/2018 - 11:00

The National Science Foundation (NSF) announces three new Expeditions in Computing awards, each providing $10 million in funding over five years to multi-investigator research teams pursuing large-scale, far-reaching and potentially transformative research in computer and information science and engineering. This year's awards aim to enable game-changing advances in real-time decision making, quantum computing and non-invasive biomedical imaging.

"The Expeditions projects being ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244648&WT.mc_id=USNSF_51&WT.mc_ev=click

This is an NSF News item.

Hidden “rock moisture” possible key to forest response to drought

News From NSF - Mon, 02/26/2018 - 15:00

Find related stories on NSF's Critical Zone Observatories webpage.

A little-studied, underground layer of rock may provide a vital reservoir for trees, especially in times of drought, report scientists funded by the National Science Foundation (NSF) and affiliated with The University of Texas (UT) at Austin and the University of California, Berkeley.

The study, published today ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244513&WT.mc_id=USNSF_51&WT.mc_ev=click

This is an NSF News item.

Confusion over the forms of programming problems: Mathematics/Physics versus CS

ComputingEd - Mon, 02/26/2018 - 07:00

I’m teaching Media Computation again this semester, about 250 students (over 50% female) in a huge lecture hall.  It’s hard to wander the room to answer questions, especially during peer instruction, but we do it anyways.

I’ve been thinking a lot about one question on the first quiz in week #2 of the class.  All my students are Georgia Tech undergraduates but not-CS majors, so, very smart, lots of math and science background, but almost no programming background.

Here was one of the two problems that had the lowest score. Students were to write down what this program would print.

def computeSalary():  rate = 10.00  hours = 40  salary = hours * rate  hours = 30  print salary  increase = 1.00  salary = hours * (rate + increase)  print salary

I suspect most teachers would think that this is a pretty standard first semester, even first couple weeks problem. I certainly did. I even discussed almost the exact same program in lecture.

The most common wrong answer was to write only what the second print statement generated. The first print statement was ignored.

During the last 5-10 minutes of the quiz, almost everyone was gone, and I answered lots of questions from the remaining students — picking my way through the narrow aisles in the lecture hall. They were struggling, so one might expect that they would try to get clues by asking questions. This was a problem that they asked lots of questions about.

I was struck by the words that students used when they described what was the one number that they were writing. One told me that the number she wrote down was the “result” of the function. Another told me that that was the “answer.” I came away with the sense that the students were thinking about the program as a mathematics function or a physics story problem.

We often talk about how computer programs might lead to misconceptions for students because of flawed transfer from mathematics. For example, Python functions have mutable data, use the “=” sign in a non-mathematical way, and have side effects. I wonder if the confusion here is about the similarity in form of the problem. Students see a series of equations in math, or a description of a situation in physics, and the answer is a number, an answer, a single result. Not so with programs — it’s a series of statements (like those others), but there may be more than one thing that the program does. We’d need to do real research to see if there is anything to this, not just me noting anecdotal comments from a half dozen students.


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