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NSF announces new awards for quantum research, technologies

News From NSF - Mon, 09/24/2018 - 13:00

The National Science Foundation (NSF) has awarded $31 million for fundamental quantum research that will enable the United States to lead a new quantum technology revolution. The awards are announced as NSF joins other federal agencies and private partners at a White House summit on quantum information science today.

"The quantum revolution is about expanding the definition of what's possible for the technology of tomorrow," said NSF Director France Córdova. "NSF-supported ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296699&WT.mc_id=USNSF_51&WT.mc_ev=click


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Boeing, National Science Foundation announce partnership for workforce development and diversity in STEM

News From NSF - Mon, 09/24/2018 - 10:00

The National Science Foundation (NSF) and Boeing today announced a new, $21 million partnership through which Boeing will invest $11 million to accelerate training in critical skill areas and increase diversity in science, technology, engineering and math (STEM) fields. Boeing becomes the first business to contribute at a national level to NSF INCLUDES, which aims to enhance U.S. innovation leadership through a commitment to broadening participation.

"We are grateful to Boeing for ...
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The Backstory on Barbie the Robotics Engineer: What might that change?

ComputingEd - Mon, 09/24/2018 - 07:00

Professor Casey Fiesler has a deep relationship with Barbie, that started with a feminist remix of a book.  I blogged about the remix and Casey’s comments on Barbie the Game Designer in this post. Now, Casey has helped develop a new book “Code Camp with Barbie and Friends” and she wrote the introduction. She tells the backstory in this Medium blog post.

In her essay, Casey considers her relationship with Barbie growing up:

I’ve also thought a lot about my own journey through computing, and how I might have been influenced by greater representation of women in tech. I had a lot of Barbies when I was a kid. For me, dolls were a storytelling vehicle, and I constructed elaborate soap operas in which their roles changed constantly. Most of my Barbies dated MC Hammer because my best friend was a boy who wasn’t allowed to have “girl” dolls, and MC was way more interesting than Ken. I also wasn’t too concerned about what the box told me a Barbie was supposed to be; otherwise I’d have had to create stories about models and ballerinas and the occasional zookeeper or nurse. My creativity was never particularly constrained, but I can’t help but think that even just a nudge — a reminder that Barbie could be a computer programmer instead of a ballerina — would have influenced my own storytelling.

I’ve been thinking about how Barbie coding might influence girls’ future interest in Tech careers.  I doubt that Barbie is a “role model” for many girls. Probably few girls want to grow up to be “like Barbie.” What a coding Barbie might do is to change the notion of “what’s acceptable” for girls.

In models of how students make choices in academia (e.g., Eccles’ expectancy-value theory) and how students get started in a field (e.g., Alexander’s Model of Domain Learning), the social context of the decision matters a lot. Students ask themselves “Do I want to do this activity and why?” and use social pressure and acceptance to decide what’s an appropriate class to take.  If there are no visible girls coding, then there is no social pressure. There are no messages that programming is an acceptable behavior.  A coding Barbie starts to change the answer to the question, “Can someone like me do this?”

NSF awards $15 million to understand how humans can better interact with the environment

News From NSF - Mon, 09/24/2018 - 06:00

A toxic red tide, or harmful algae bloom, is killing swaths of marine life and affecting the health of people living along Florida's southwest coast. Nationwide, harmful algae blooms cost an estimated $50 million each year. Excess nutrients such as nitrogen and phosphorus flowing downstream act as fertilizer, sparking these blooms in waterbodies such as the Gulf of Mexico, Lake Erie and Chesapeake Bay.

Paul Leisnham of the University of Maryland, College Park, is working to find out ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296562&WT.mc_id=USNSF_51&WT.mc_ev=click


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NSF announces new awards for Understanding the Rules of Life

News From NSF - Fri, 09/21/2018 - 10:00

The National Science Foundation (NSF) announced 29 awards in support of Understanding the Rules of Life, one of the agency's "10 Big Ideas for Future NSF Investments." The awards, totaling $15 million, demonstrate NSF's commitment to address some of the greatest challenges in understanding the living world, in all of its complex levels of organization, ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296660&WT.mc_id=USNSF_51&WT.mc_ev=click


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Why Don’t Women Want to Code? Better question: Why don’t women choose CS more often?

ComputingEd - Fri, 09/21/2018 - 07:00

Jen Mankoff (U. Washington faculty member, and Georgia Tech alumna) has written a thoughtful piece in response to the Stuart Reges blog post (which I talked about here), where she tells her own stories and reframes the question.

Foremost, I think this is the wrong question to be asking. As my colleague Anna Karlin argues, women and everyone else should code. In many careers that women choose, they will code. And very little of my time as an academic is spent actually coding, since I also write, mentor, teach, etc. In my opinion, a more relevant question is, “Why don’t women choose computer science more often?”

My answer is not to presume prejudice, by women (against computer science) or by computer scientists (against women). I would argue instead that the structural inequalities faced by women are dangerous to women’s choice precisely because they are subtle and pervasive, and that they exist throughout a woman’s entire computer science career. Their insidious nature makes them hard to detect and correct.

Source: Why Don’t Women Want to Code? Ask Them! – Jennifer Mankoff – Medium

What makes a mammal a mammal? Our spine, say scientists

News From NSF - Thu, 09/20/2018 - 14:00

Mammals are unique in many ways. We're warm-blooded and agile in comparison with our reptilian relatives.

But a new study, funded by the National Science Foundation (NSF) and led by Harvard University researchers Stephanie Pierce and Katrina Jones, suggests we're unique in one more way -- the makeup of our spines. The researchers describe their finding in a paper published this week in the journal Science.

"The spine is basically like a series of beads on a string, ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296527&WT.mc_id=USNSF_51&WT.mc_ev=click


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Media invited to NSF for distinguished lecture with Boston University’s Michael Dietze

News From NSF - Thu, 09/20/2018 - 10:06

Is nature predictable? If so, how can we better manage and conserve ecosystems? Near-term ecological forecasting is an emerging interdisciplinary research area that aims to improve researchers' ability to predict ecological processes on timescales that can be validated and updated.

The National Science Foundation's (NSF) Directorate for Biological Sciences invites media and members of the public to a distinguished lecture series with Michael Dietze of Boston University. An ecologist ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296646&WT.mc_id=USNSF_51&WT.mc_ev=click


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NSF awards 6 Louis Stokes regional centers of excellence to broaden participation in STEM

News From NSF - Wed, 09/19/2018 - 17:00

The National Science Foundation (NSF) announced awards for six Louis Stokes regional centers of excellence (LSRCEs) that will support recruitment and retention of minority undergraduate and graduate students studying science, technology, engineering and mathematics (STEM).

The centers will conduct broadening participation research and STEM implementation activities that lead to degree completion for minority students traditionally underrepresented in the STEM marketplace. The goal is ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296504&WT.mc_id=USNSF_51&WT.mc_ev=click


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NSF announces new measures to protect research community from harassment

News From NSF - Wed, 09/19/2018 - 12:00

The National Science Foundation (NSF) has taken the next steps in its agency-wide effort to ensure the research and learning environments it supports are free from harassment, publishing a term and condition that requires awardee organizations to report findings and determinations of sexual harassment, as well as establishing a secure online portal for submitting harassment notifications.

On Sept. 21, 2018, NSF will publish a term and condition for awards, to become effective 30 days ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296610&WT.mc_id=USNSF_51&WT.mc_ev=click


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NSF: Next steps against harassment

News From NSF - Wed, 09/19/2018 - 12:00

For more information, see NSF's press release.

What NSF is doing:

The National Science Foundation (NSF) will release a term and condition requiring awardee organizations to report findings of sexual harassment. It will be posted in the Federal Register Sept. 21, 2018 and go into effect Oct. 21, 2018.

Why NSF is doing this:

As the primary ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296671&WT.mc_id=USNSF_51&WT.mc_ev=click


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New NSF funding to build research infrastructure across the country

News From NSF - Tue, 09/18/2018 - 10:00

The National Science Foundation (NSF) has awarded nearly $140 million to seven jurisdictions through the Established Program to Stimulate Competitive Research (EPSCoR), which builds research and development capacity in jurisdictions that demonstrate a commitment to research but have thus far lacked the levels of investment seen in other parts of the country.

The new EPSCoR Research Infrastructure Improvement (RII) Track-1 awards will bolster science and engineering research ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=296632&WT.mc_id=USNSF_51&WT.mc_ev=click


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International effort to improve data science in schools

ComputingEd - Mon, 09/17/2018 - 07:00

I’ve been involved in this project over the last few months. (Where “involved” means, “a couple of phone conversations, and a set of emails about frameworks, standards, and curricula, and I missed every physical meeting.”) Nick Fisher has drawn together an impressive range of experts and professional societies to back the effort. It’s not clear where it’s going, but it is indicative of a growing worldwide interest in “data science” in schools.

The definition of “data science” is fuzzy for me, almost as fuzzy as the term “computational thinking.”  Does data science include computer science? statistics? probability? I think the answer is “yes” to all of those, but then it might be too big to easily teach in secondary schools. If we’re struggling to teach CS to teachers, how do we teach them CS and statistics and probability?

And if budgets and schedules are are a zero-sum game, what do we give up in order to teach data science?  For example, teacher preparation programs are packed full. What do we not teach in order to teach teachers about data science?

This group of experts knows a lot about what works in data science. Their opinion on what students need to know creates a useful measuring stick with which to look at the several data science classes that are being created (such as Unit 5 in Exploring CS). There’s some talk about this group of experts might develop their own course. I’m not sure that it’s possible to create a course to work internationally — school systems and expectations vary dramatically. But a framework is useful.

The aim of the International Data Science in Schools Project (IDSSP) is to transform the way data science is taught the last two years of secondary school. Its objectives are:

1. To ensure that school children develop a sufficient understanding and appreciation of how data can be acquired and used to make decisions so that they can make informed judgments in their daily lives, as children and then as adults

2. To inspire mathematically able school students to pursue tertiary studies in data science and its related fields, with a view to a career.

“In both cases, we want to teach people how to learn from data,” Dr Fisher said.

Two curriculum frameworks are being created to support development of a pre-calculus course in data science that is rigorous, engaging and accessible to all students, and a joy to teach.

  • Framework 1 (Data Science for students). This framework is designed as the basis for developing a course with a total of some 240 hours of instruction.
  • Framework 2 (Data Science for teachers). As a parallel development, this framework is designed as the basis for guiding the development of teachers from a wide variety of backgrounds (mathematics, computer science, science, economics, …) to teach a data science course well.

Dr Fisher said that the draft frameworks will be published for widespread public consultation in early 2019 before completion by August.

“We envisage the material will be used not just in schools, but also as a valuable source of information for data science courses in community colleges and universities and for private study.” For further information: idssp.info@gmail.com, or visit www.idssp.org

Applying diSessa’s Knowledge in Pieces Framework to Understanding the Notional Machine

ComputingEd - Fri, 09/14/2018 - 07:00

In Lauren Margulieux’s blog where she summarizes papers from learning sciences and educational psychology, she takes on Andy diSessa’s 1993 paper “Toward an epistemology of physics” where diSessa applies his “knowledge in pieces” framework to how students develop an understanding of physics.  (See blog post here.)

The idea is that humans assemble their understanding of complex phenomenon out of knowledge of physical experiences, p-prims. Quoting Lauren:

Elements: P-prims are knowledge structures that are minimal abstractions of common phenomena and typically involve only a few simple parts, e.g., an observed phenomenon, like a person hitting a pen and that pen rolling across the table, and an explanation, like when people hit things, they move. P-prims are both phenomenological, meaning that they are interpretations of reality, and primitive, meaning that are (1) based on often rudimentary self-explanations and (2) an atomic-level mental structure that is only separated into parts by excessive force.

Cognitive Mechanism: P-prims are only activated when the learner recognizes similarities between a p-prim and the current phenomena. Recognition is impacted by many different features, such as cuing, frequency of activation, suppression, salience, and reinforcement. Because activation of p-prims depends on contextual features of phenomena, novices often fail to recognize relevant p-prims unless the contextual features align.

I find diSessa’s framework fascinating, and I’ve always wondered how we could apply it to students learning the notional machine (see blog post here on notional machine). My guess is that students use p-prims to develop their mental model of how the computer works, because — what else could they use? In the end, isn’t all our understanding grounded in physical experiences?  But using p-prims will likely lead to misconceptions since the notional machine is not based in the physical world.

Maybe this is a source of common misconceptions in learning computing.  The list of misconceptions that students have about variables, loops, scope, conditionals, and data structures is long and surprisingly consistent — across languages, over time.  What could possibly be the common source of all those misconceptions?  Maybe it’s physical reality.  Maybe students generally apply the same p-prims when trying to understand computing, and that’s why the same misconceptions arise. It’s sort of like using a metaphor to understand something in computing, but then realizing that the metaphor itself is leading to misconceptions.  And the metaphor that’s getting in our way is the use of physical world primitives for understanding the computational world.

Colleen Lewis, as a student of diSessa’s, uses the Knowledge in Pieces framework in her work.  In her terrific ICER 2012 paper, she does a detailed analysis of students’ debugging to identify misconceptions that they have about state. State is an interesting concept to study from a KiP perspective. It’s a common issue in CS, but less common in Physics. It’s not clear to me how students connect computational state to state in the real world.  Is it state like water being frozen or liquid, or state like being painted blue?  Do they get that state is malleable?

This is a rich space to explore in computing education. What are the p-prims for understanding the notional machine? How do students use the physical world to understand the computational one?

Read more of Lauren’s post here: Article Summary: diSessa (1993) Knowledge in Pieces Framework

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