Archive for July, 2013

Writing programs using ordinary language: Implications for computing education

Once upon a time, all computer scientists understood how floating point numbers were represented in binary.  Numerical methods was an important part of every computing curriculum.  I know few undergraduate programs that require numerical methods today.

Results like the below make me think about what else we teach that will one day become passé, irrelevant, or automatized.  The second result is particularly striking.  If descriptions from programming competitions can lead to automatic program generation, what does that imply about what we’re testing in programming competitions — and why?

The researchers’ recent papers demonstrate both approaches. In work presented in June at the annual Conference of the North American Chapter of the Association for Computational Linguistics, Barzilay and graduate student Nate Kushman used examples harvested from the Web to train a computer system to convert natural-language descriptions into so-called “regular expressions”: combinations of symbols that enable file searches that are far more flexible than the standard search functions available in desktop software.

In a paper being presented at the Association for Computational Linguistics’ annual conference in August, Barzilay and another of her graduate students, Tao Lei, team up with professor of electrical engineering and computer science Martin Rinard and his graduate student Fan Long to describe a system that automatically learned how to handle data stored in different file formats, based on specifications prepared for a popular programming competition.

via Writing programs using ordinary language – MIT News Office.

July 31, 2013 at 1:36 am 2 comments

Taking a test is better than studying, even if you just guess: We need to flip the flipped classroom

The benefits of testing for learning are fascinating, and the result described below makes me even more impressed with the effect.  It suggests even more strongly that the critical feature of learning is trying to understand, trying to generate an answer, even more than reading an answer.

Suppose, for example, that I present you with an English vocabulary word you don’t know and either (1) provide a definition that you read (2) ask you to make up a definition or (3) ask you to choose from among a couple of candidate definitions. In conditions 2 & 3 you obviously must simply guess. (And if you get it wrong I’ll give you corrective feedback.) Will we see a testing effect?

That’s what Rosalind Potts & David Shanks set out to find, and across four experiments the evidence is quite consistent. Yes, there is a testing effect. Subjects better remember the new definitions of English words when they first guess at what the meaning is–no matter how wild the guess.

via Better studying = less studying. Wait, what? – Daniel Willingham.

These results mesh well with a new study from Stanford.  They found that the order of events in a “flipped” classroom matters — the problem-solving activity (in the classroom) should come before the reading or videos (at home). The general theme is the same in both sets of studies: problem-solving drives learning, and it’s less true that studying prepares one for problem-solving.

A new study from the Stanford Graduate School of Education flips upside down the notion that students learn best by first independently reading texts or watching online videos before coming to class to engage in hands-on projects. Studying a particular lesson, the Stanford researchers showed that when the order was reversed, students’ performances improved substantially.

via Classes should do hands-on exercises before reading and video, Stanford researchers say.

July 30, 2013 at 1:47 am 15 comments

The challenges of integrated engineering education

I spent a couple days at Michigan State University (July 11-12) learning about integrated engineering education. The idea of integrated engineering education is to get students to see how the mathematics and physics (and other requirements) fit into their goals of becoming engineers. In part, it’s a response to students learning calculus here and physical principles there, but having no idea what role they play when it comes to design and solving real engineering problems. (Computer science hasn’t played a significant role in previous experiments in integrated engineering education, but if one were to do it today, you probably would include CS — that’s why I was invited, as someone interested in CS for other disciplines.)  The results of integrated engineering education are positive, including higher retention (a pretty consistent result across all the examples we saw), higher GPA’s (often), and better learning (some data).

But these programs rarely last. A program at U. Massachusetts-Dartmouth is one of the longest running (9 years), but it’s gone through extensive revision — not clear it’s the same program. These are hard programs to get set up. It is an even bigger challenge  to sustain them.

The programs lie across a spectrum of integration. The most intense was a program at Rose-Hulman that lasted for five years. All the core first year engineering courses were combined in a single 12 credit hour course, co-taught by faculty from all the relevant disciplines. That’s tight integration. On the other end is a program at Wright State University, where the engineering faculty established a course on “Engineering Math” that meets Calculus I requirements for Physics, but is all about solving problems (e.g., using real physical units) that involve calculus. The students still take Calculus I, but later. The result is higher retention and students who get the purpose for the mathematics — but at a cost of greater disconnect between Engineering and mathematics. (No math faculty are involved in the Engineering Math course.)

My most significant insight was: The greater the integration, the greater the need for incentives. And the greater the need for the incentives, the higher in the organization you need support. If you just want to set up a single course to help Engineers understand problem-solving with mathematics, you can do that with your department or school, and you only have to provide incentives to a single faculty member each year. If you want to do something across departments, you need greater incentives to keep it going, and you’ll need multiple chairs or deans. If you want a 12 credit hour course that combines four or five disciplines, maybe you need the Provost or President to make it happen and keep it going.

Overall, I wasn’t convinced that integrated engineering education efforts are worth the costs. Are the results that we have merely a Hawthorne effect?  It’s hard to sustain integrated anything in American universities (as Cuban told us in “How Scholars Trumped Teachers”). (Here’s an interesting review of Cuban’s book.) Retention is good and important (especially of women and under-represented students), but if Engineering programs are already over-subscribed (which many in the workshop were), then why improvement retention of students in the first year if there is no space for them in the latter years? Integration probably leads to better learning, but there are deeper American University structural problems to fix first, which would reduce the costs in doing the right things for learning.

July 29, 2013 at 1:41 am 4 comments

Call for papers for first ACM Conference on Learning at Scale

The First Annual ACM Conference on Learning at Scale will be held March 4-5,
2014 in Atlanta, GA (immediately prior to and collocated with SIGCSE-14).

The Learning at Scale conference is intended to promote scientific exchange
of interdisciplinary research at the intersection of the learning sciences
and computer science. Inspired by the emergence of Massive Open Online
Courses (MOOCs) and the accompanying huge shift in thinking about education,
this conference was created by ACM as a new scholarly venue and key focal
point for the review and presentation of the highest quality research on how
learning and teaching can change and improve when done at scale.

“Learning at Scale” refers to new approaches for students to learn and for
teachers to teach, when engaging large numbers of students, either in a
face-to-face setting or remotely, whether synchronous or asynchronous, with
the requirement that the techniques involve large numbers of students (where
“large” is preferably thousands of students, but can also apply to hundreds
in in-person settings). Topics include, but are not limited to: Usability
Studies, Tools for Automated Feedback and Grading, Learning Analytics,
Analysis of Log Data, Studies of Application of Existing Learning Theory,
Investigation of Student Behavior and Correlation with Learning Outcomes,
New Learning and Teaching Techniques at Scale.

IMPORTANT DATES
—————
November 8, 2013: Paper submissions due
November 8, 2013: Tutorial proposals due
December 23, 2013: Notification to authors of full papers
January 2, 2014: Works-in-progress submissions due (posters and demos)
January 14, 2014: Notification to authors of acceptance of works-in-progress
January 17, 2014: All revised and camera-ready materials due
March 4-5, 2014: Learning at Scale meeting

Additional information is available at: http://learningatscale.acm.org/

July 29, 2013 at 1:22 am Leave a comment

Congressional Panels Dump on STEM Reshuffling Plan

Will TUES exist again?  Will STEM-C get created?  Looks like it’s all up in the air now.

A bill approved yesterday by the House of Representatives science committee to reauthorize NASA programs, for example, rejects the two key elements of what the administration has proposed—stripping the agency of most of its STEM education agencies and putting the rest under one roof. “The administration may not implement any proposed STEM education and outreach-related changes proposed [for NASA] in the president’s 2014 budget request,” the bill flatly declares. “Funds devoted to education and public outreach should be maintained in the [science, aeronautics, exploration, and mission] directorates, and the consolidation of those activities within the Education Directorate is prohibited.”

Likewise, the House version of the CJS spending bill would restore money for STEM education activities at NASA and the National Oceanic and Atmospheric Administration and put the kibosh on a realignment of undergraduate STEM education programs at NSF. “The committee supports the concept of improving efficiency and effectiveness, through streamlining and better coordination, but does not believe that this particular restructuring proposal achieves that goal,” the legislators explain in a report this week accompanying the spending bill. The report also notes that “the ideas presented in the budget request lack any substantive implementation plan and have little support within the STEM education community.”

via Congressional Panels Dump on STEM Reshuffling Plan – ScienceInsider.

More from the Senate report on the STEM Consolidation:

“While the Committee maintains its support of greater efficiencies and consolidation – as evident by adopting some of the STEM consolidation recommendations made by the administration’s budget request – the Committee has concerns that the proposal as a whole has not been thoroughly vetted with the education community or congressional authorizing committees, and lacks thorough guidance and input from Federal agencies affected by this proposal, from both those that stand to lose education and outreach programs and from those that stand to gain them. The administration has yet to provide a viable plan ensuring that the new lead STEM institutions – the National Science Foundation, the Department of Education, and the Smithsonian Institution – can support the unique fellowship, training, and outreach programs now managed by other agencies. Conversely, what is proposed as a consolidation of existing STEM programs from NOAA, NASA, and NIST into the new lead STEM agencies is really the elimination of many proven and successful programs with no evaluation on why they were deemed duplicative or ineffective.

via FY 2014 Senate Appropriations: STEM Consolidation and Public Access.

The STEM-C program was recommended by one committee, but not CAUSE (the program created instead of TUES). Said the House report, “Consistent with the Committee’s position on the proposed STEM education restructuring, the recommendation does not support the establishment of the new CAUSE program or the transition of the GRF program into the interagency National GRF.”

July 26, 2013 at 1:57 am Leave a comment

More women pass AP CS than AP Calculus

Barbara Ericson has generated her 2012 Advanced Placement Computer Science report. http://home.cc.gatech.edu/ice-gt/321 has all of her reports. http://home.cc.gatech.edu/ice-gt/548 has her more detailed analysis just of 2012. Since one of our concerns with GaComputes and ECEP is on pass rates, not just test-takers, she dug deeper into pass rates.  For a point of comparison, she looked up AP Calculus pass rates.  What she found is somewhat surprising — below is quoted from her page.

Comparison of AP CS A to AP Calculus AB in 2012

  • The number of students that take the exam per teacher is much higher for AP Calculus AB at 21 students per teacher versus 11 for Computer Science A

  • The number of schools that teach Calculus is 11,694 versus 2,103

  • AP CS A had a higher pass rate than Calculus – 63% versus 59%

  • AP CS A had a higher female pass rate than Calculus – 56% versus 55%

  • AP CS A had a higher Hispanic pass rate than Calculus – 39.8% versus 38.4%

  • AP Calculus had a higher black pass rate than CS – 28.7% versus 27.3%

  • Calculus had a much higher percentage of women take the exam than CS – 48.3% versus 18.7%

  • Calculus had a higher percentage of black students take the exam than CS – 5.4% versus 4.0%

  • Calculus had a higher percentage of Hispanic/Latino students take the exam than CS – 11.5% versus 7.7%

July 26, 2013 at 1:39 am 1 comment

Starting with Robots: Linking Spatial Ability and Learning to Program

Stuart Wray has a remarkable blog that I recommend to CS teachers.  He shares his innovations in teaching, and grounds them in his exploration of the literature into the psychology of programming.  The quote and link below is an excellent example, where his explanation led to me a paper I’m eager to dive into.  Stuart has built an interesting warm-up activity for his class that involves robots.  What I’m most intrigued by is his explanation for why it works as it does.  The paper that he cites by Jones and Burnett is not one that I’d seen before, but it explores an idea that I’ve been interested in for awhile, ever since I discovered the Spatial Intelligence and Learning Center:  Is spatial ability a pre-requisite for learning in computer science?  And if so, can we teach it explicitly to improve CS learning?

The game is quite fun and doesn’t take very long to play — usually around a quarter of an hour or less. It’s almost always quite close at the end, because of course it’s a race between the last robot in each team. There’s plenty of opportunity for delaying tactics and clever blocking moves near the exit by the team which is behind, provided they don’t just individually run for the exit as fast as possible.

But turning back to the idea from James Randi, how does this game work? It seems from my experience to be doing something useful, but how does it really work as an opening routine for a programming class? Perhaps first of all, I think it lets me give the impression to the students that the rest of the class might be fun. Lots of students don’t seem to like the idea of programming, so perhaps playing a team game like this at the start of the class surprises them into giving it a second chance.

I think also that there is an element of “sizing the audience up” — it’s a way to see how the students interact with one another, to see who is retiring and who is bold, who is methodical and who is careless. The people who like clever tricks in the game seem often to be the people who like clever tricks in programming. There is also some evidence that facility with mental rotation is correlated with programming ability. (See Spatial ability and learning to program by Sue Jones and Gary Burnett in Human Technology, vol.4(1), May 2008, pp.47-61.) To the extent that this is true, I might be getting a hint about who will have trouble with programming from seeing who has trouble making their robot turn the correct direction.

via On Food and Coding: The Robots Game.

July 25, 2013 at 1:12 am 6 comments

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