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NSF's Big Ideas

News From NSF - Tue, 11/28/2017 - 12:01

In 2016, NSF unveiled a set of "Big Ideas" -- 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering. With its broad portfolio of investments, NSF is uniquely suited to advance this set of cutting-edge research agendas and processes that will require collaborations with industry, private foundations, other agencies, science academies and societies, and universities and the education sector.
More at https://www.nsf.gov/news/special_reports/big_ideas/index.jsp?WT.mc_id=USNSF_51


This is an NSF News item.

2017-2018 NSF Distinguished Lectures in Mathematical and Physical Sciences

News From NSF - Tue, 11/28/2017 - 10:00

The National Science Foundation's (NSF) Directorate for Mathematical and Physical Sciences (MPS) invites media and members of the public to a series of lectures intended to promote discussion of issues that scientists expect to shape their research in the coming years.

MPS' mission is to harness the collective efforts of the mathematical and physical sciences communities to address compelling questions and push the boundaries of scientific frontiers. All of the 2017-2018 MPS ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=243806&WT.mc_id=USNSF_51&WT.mc_ev=click


This is an NSF News item.

CS Teacher Interview: Emmanuel Schanzer on Integrating CS into Other Subjects

ComputingEd - Mon, 11/27/2017 - 07:00

I love that Bootstrap is building on their great success with algebra to integrate CS into Physics and Social Studies. I’m so looking forward to hearing how this works out.  I’m working on related projects, following Bootstrap’s lead.

Lots of governors, superintendents and principals made pledges to bring CS to every child, but discovered that dedicated CS electives and required CS classes were either incredibly expensive (hiring/retaining new teachers), logistically impossible (adding a new class given finite hours in the day and rooms in the building), or actively undermined equity (opt-in classes are only taken by students with the means and/or inclination). As a result, they started asking how they might integrate CS into other subjects — and authentic integration is our special sauce! Squeezing CS into math is something folks have been trying to do for decades, with little success. Our success with Bootstrap:Algebra means we’ve got a track record of doing it right, which means we’ve been approached about integration into everything from Physics to Social Studies.

Source: Computer Science Teacher: CS Teacher Interview: Emmanuel Schanzer–The Update


Tagged: computing education, mathematics education, physics education

Universities aren’t preparing enough computer science teachers, and we have no path to get there

ComputingEd - Fri, 11/24/2017 - 07:00

Not really a surprising claim, but I still think that we’re not talking enough about this. No K-12 subject is taught nationwide without producing teachers from universities. We simply cannot create sustainable K-12 CS education without universities producing CS teachers (called “pre-service teacher professional development”). Currently, we produce new CS teachers by recruiting existing teachers from other subjects (called “in-service teacher professional development”). None of our models for growing CS nationwide currently have a plan to replace in-service with pre-service (as described in this blog post).

Looking for answers, we examined the state-by-state data on the number of graduates prepared to teach various subjects. We found that in 2016, only 75 teachers graduated from universities equipped to teach computer science. Compare that to the number of graduating teachers prepared in mathematics (12,528) and the sciences (11,917 across general science, biology, chemistry, physics, and earth science).

Source: Universities aren’t preparing enough computer science teachers


Tagged: computer science teachers, computing education, in-service, pre-service

Antarctic detector offers first look at how Earth stops high-energy neutrinos in their tracks

News From NSF - Wed, 11/22/2017 - 13:00

An interdisciplinary team of researchers using the IceCube Neutrino Observatory in Antarctica has measured how certain high-energy neutrinos are absorbed by the Earth, as opposed to passing through matter as most neutrinos do. The finding could help expand scientists' understanding of the fundamental forces of the universe.

Funded and managed by the National Science Foundation (NSF), the IceCube Neutrino Observatory conducts research into these nearly massless ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=243782&WT.mc_id=USNSF_51&WT.mc_ev=click


This is an NSF News item.

Futures of the Scientific Imagination

News From NSF - Tue, 11/21/2017 - 13:05

Fantastical thinking, grounded in real, NSF-funded science and engineering research, helps shape tomorrow's technologies. Imagination, science and technology together will inevitably change our lives.
More at https://www.nsf.gov/news/special_reports/futures/index.jsp?WT.mc_id=USNSF_51


This is an NSF News item.

Keeping the Machinery in Computing Education: Back to the Future in the Definition of CS

ComputingEd - Mon, 11/20/2017 - 07:00

I’ve been excited to see this paper finally come out in CACM. Richard Connor, Quintin Cutts, and Judy Robertson are leaders in the Scotland CAS effort. Their new curriculum re-emphasizes the “computer” in computer science and computational thinking. I have bold-faced my favorite sentence in the quote below. I like how this emphasis reflects the original definition of computer science: “Computer science is the study of computers and all the phenomena surrounding them.”

We do not think there can be “computer science” without a computer. Some efforts at deep thinking about computing education seem to sidestep the fact that there is technology at the core of this subject, and an important technology at that. Computer science practitioners are concerned with making and using these powerful, general-purpose engines. To achieve this, computational thinking is essential, however, so is a deep understanding of machines and languages, and how these are used to create artifacts. In our opinion, efforts to make computer science entirely about “computational thinking” in the absence of “computers” are mistaken.

As academics, we were invited to help develop a new curriculum for computer science in Scottish schools covering ages 3–15. We proposed a single coherent discipline of computer science running from this early start through to tertiary education and beyond, similar to disciplines such as mathematics. Pupils take time to develop deep principles in those disciplines, and with appropriate support the majority of pupils make good progress. From our background in CS education research, we saw an opportunity for all children to learn valuable foundations in computing as well, no matter how far they progressed ultimately.

Source: Keeping the Machinery in Computing Education | November 2017 | Communications of the ACM


Tagged: CAS, computing education, curriculum, K12

NSF-funded scientists to present on long-term ecological research findings at AGU fall meeting

News From NSF - Mon, 11/20/2017 - 06:00

Find related stories on NSF's Long-Term Ecological Research Program at this link.

Hurricanes Harvey, Irma and Maria this fall. Wildfires that raged across California and British Columbia this summer. Unseasonable cold snaps in South Florida in past winters. How do such events shape and re-shape ecosystems?

And how do events from past decades affect the ways in which ...
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=243731&WT.mc_id=USNSF_51&WT.mc_ev=click


This is an NSF News item.

Predicting Seasonal Weather

News From NSF - Fri, 11/17/2017 - 15:46

Large-scale weather patterns play a large role in controlling seasonal weather. Knowing the conditions of these atmospheric oscillations in advance would greatly improve long-range weather predictions.
More at https://www.nsf.gov/news/special_reports/autumnwinter/index.jsp?WT.mc_id=USNSF_51


This is an NSF News item.

Parsons Problems have same Learning Gains as Writing or Fixing code, in less time: Koli Calling 2017 Preview

ComputingEd - Fri, 11/17/2017 - 07:00

On Saturday, Barbara Ericson will be presenting at Koli Calling her paper (with Lauren Margulieux and Jeff Rick), “Solving Parsons Problems Versus Fixing and Writing Code.”

The basic design of her experiment is pretty simple.  Everybody gets a pretest where they answer multiple-choiced questions, write some code, fix some code, and solve some Parsons problems.  (I’ve written about Parsons Problems here before.)

Then there are three instructional treatments with three different kinds of problem-solving practice:

  • One group gets Parsons Problems with distractors in them — blocks that should not be dragged into the solution.
  • One group gets the same code to fix — same code as in the Parsons Problems but all the distractors are there.  They have to fix the broken code in the distractor to get to the same code as the correct block in the Parsons.
  • One group gets to write the code to solve the same problem.

Then they take an isomorphic (same basic problems with context and constants changed) post-test, go away, and come back one week later for a retention test (which is isomorphic to both the pretest and the first posttest: multiple choice questions, Parsons, fix code, write code).  So we have students who study with Parsons Problems getting tested by writing and fixing code.

Here’s the bottom line from their abstract: “We found that solving two-dimensional Parsons problems with distractors took significantly less time than fixing code with errors or than writing the equivalent code. Additionally, there was no statistically significant difference in the learning performance, or in student retention of the knowledge one week later.”

That’s it. It’s simple but profound.  Below is the timing table from the paper. The Parsons Problems took effort, but always less time — sometimes they took only half the time of fixing or writing code, and other times it was only a few percentage less. But it was always less.

One takeaway idea is: If Parsons leads to the same learning in less time, why wouldn’t every teacher use more Parsons problems?  A second one that we’ve been thinking alot about is: Can we provide more Parsons problems so that in the same amount of time that students were writing code, they actually learn more? Efficiency matters, as Elizabeth Patitsas’s work suggests — more efficient learning may mean less belief in Geek Gene by CS teachers.


Tagged: computing education research, efficiency, Parsons Problems

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