I have written about this Dagstuhl Seminar (see earlier post). The formal report is now available.
This seminar discussed educational outcomes for first-year (university-level) computer science. We explored which outcomes were widely shared across both countries and individual universities, best practices for assessing outcomes, and research projects that would significantly advance assessment of learning in computer science. We considered both technical and professional outcomes (some narrow and some broad) as well as how to create assessments that focused on individual learners. Several concrete research projects took shape during the seminar and are being pursued by some participants.
Probably my favorite session from the CRA Snowbird conference this last summer (see agenda with links to all talks) was a session on creating Colleges or Schools of Computer Science. Should we? Why?
The most compelling two talks in the session were from Randy Bryant and Rich LeBlanc, because they were so similar in structure. They both argued that you don’t make the argument for a high-level College or School of Computing because you’re big and important. You make it because you have a driving definition of computing that makes it unique.
- Randy told the story of how CMU’s School of Computer Science was driven by the original definition of computer science from Newell and Simon, and how that definition was broader than most people’s definition of CS today. I recently blogged on that definition.
- Rich told the story of how Georgia Tech’s College of Computing was driven by the ACM report The Future of Computing (led by Peter Denning) which showed how Computing crossed science, mathematics, and engineering. Of course, Rich’s story was particularly powerful for me because I lived that definition — that was the vision that drove the College of Computing when I first got here in 1993. Rich told the story of how that definition convinced faculty and administrators at Georgia Tech that Computing couldn’t be contained within the Colleges of Engineering or Science. It needed to be its own entity. (I may also be biased because Rich quoted me from this blog🙂
Many of the people in the audience wanted to know, “How can I turn my Department into a School or College?” One audience member said, “My CS department is the biggest one in the College of Engineering. How do I break apart into my own College.” All the panelists told him, “You can’t.” No Dean will allow its biggest department to leave — that would be crazy. Some participants (from U. Michigan and U. Washington, in particular) pointed out why they don’t have a College or School of Computing — they have successful multi-department collaborations that make it unnecessary. A new College or School is expensive. Don’t do it unless you have to.
Every University Student should Learn to Program: Guzdial Arguing for CS for All in Higher Education
A colleague recently approached me and said, “It would be useful if Universities got involved in this CS for All effort. All Universities should offer courses aimed at everyone on campus. There should be a systematic effort to get everyone to take those classes.”
I agree, and have been making this argument for several years now. I spent a few minutes gathering the papers, blog posts, and book where I’ve made that argument over the last decade and a bit.
In 2002, Elliot Soloway and I argued in CACM that we needed a new way to engage students in intro programming: Teaching the Nintendo Generation to Program.
In 2003, I published the first paper on Media Computation: A media computation course for non-majors.
In 2004, Andrea Forte led the team studying the Media Computation class at GT:Computers for communication, not calculation: Media as a motivation and context for learning and A CS1 course designed to address interests of women.
In 2005, Andrea Forte and I presented empirical evidence about the courses we’d designed for specific audiences: Motivation and nonmajors in computer science: identifying discrete audiences for introductory courses. I published a paper in CACM about how the courses came to be at Georgia Tech: Teaching computing to everyone.
In 2008, I offered the historical argument for teaching everyone to program: Paving the Way for Computational Thinking.
We’ve published several papers about our design process: Imagineering inauthentic legitimate peripheral participation: an instructional design approach for motivating computing education and Design process for a non-majors computing course.
My 2013 ICER paper was a review of a decade’s worth of research on Media Computation: Exploring hypotheses about media computation
My keynote at VL/HCC 2015 was on how computing for all is a requirement for modern society: Requirements for a computing-literate society
My 2015 book is, to a great extent: an exploration of how to achieve CS for All: Learner-Centered Design of Computing Education: Research on Computing for Everyone.
In blog posts, it’s been a frequent topic of conversation:
- In 2011, I argued that it makes more sense to require CS at universities before pushing into K-12, because then all pre-service teachers have some CS which makes later PD much easier and cheaper: https://computinged.wordpress.com/2015/11/30/require-cs-at-universities-before-k-12-computational-community-for-everyone/ and https://computinged.wordpress.com/2011/05/17/if-you-want-cs-in-high-school-require-cs-in-college/
- In 2013, I pointed out that CS is becoming increasingly valuable outside of CS: https://computinged.wordpress.com/2013/12/10/why-are-english-and-lots-of-other-majors-studying-computer-science/
- One of my earlier Blog@CACM posts was on how students learn things in MediaComp that informs them about their world, not just about CS: http://cacm.acm.org/blogs/blog-cacm/26343-media-computation-for-creativity-and-surprises/fulltext
- On how CS is a value-added to a liberal education: http://cacm.acm.org/blogs/blog-cacm/101738-computer-science-as-value-added-to-a-liberal-education/fulltext
I don’t know how to convince University CS departments to do just about anything, but here are my contributions to the dialogs that I hope are happening at Colleges and Universities worldwide about how to prepare students to engage in computational literacy.
Learning Curves, Given vs Generated Subgoal Labels, Replicating a US study in India, and Frames vs Text: More ICER 2016 Trip Reports
My Blog@CACM post for this month is a trip report on ICER 2016. I recommend Andy Ko’s excellent ICER 2016 trip report for another take on the conference. You can also see the Twitter live feed with hashtag #ICER2016.
I write in the Blog@CACM post about three papers (and reference two others), but I could easily write reports on a dozen more. The findings were that interesting and that well done. I’m going to give four more mini-summaries here, where the results are more confusing or surprising than those I included in the CACM Blog post.
This year was the first time we had a neck-and-neck race for the attendee-selected award, the “John Henry” award. The runner-up was Learning Curve Analysis for Programming: Which Concepts do Students Struggle With? by Kelly Rivers, Erik Harpstead, and Ken Koedinger. Tutoring systems can be used to track errors on knowledge concepts over multiple practice problems. Tutoring systems developers can show these lovely decreasing error curves as students get more practice, which clearly demonstrate learning. Kelly wanted to see if she could do that with open editing of code, not in a tutoring system. She tried to use AST graphs as a sense of programming “concepts,” and measure errors in use of the various constructs. It didn’t work, as Kelly explains in her paper. It was a nice example of an interesting and promising idea that didn’t pan out, but with careful explanation for the next try.
I mentioned in this blog previously that Briana Morrison and Lauren Margulieux had a replication study (see paper here), written with Adrienne Decker using participants from Adrienne’s institution. I hadn’t read the paper when I wrote that first blog post, and I was amazed by their results. Recall that they had this unexpected result where changing contexts for subgoal labeling worked better (i.e., led to better performance) for students than keeping students in the same context. The weird contextual-transfer problems that they’d seen previously went away in the second (follow-on) CS class — see below snap from their slides. The weird result was replicated in the first class at this new institution, so we know it’s not just one strange student population, and now we know that it’s a novice problem. That’s fascinating, but still doesn’t really explain why. Even more interesting was that when the context transfer issues go away, students did better when they were given subgoal labels than when they generated them. That’s not what happens in other fields. Why is CS different? It’s such an interesting trail that they’re exploring!
Mike Hewner and Shitanshu Mishra replicated Mike’s dissertation study about how students choose CS as a major, but in Indian institutions rather than in US institutions: When Everyone Knows CS is the Best Major: Decisions about CS in an Indian context. The results that came out of the Grounded Theory analysis were quite different! Mike had found that US students use enjoyment as a proxy for ability — “If I like CS, I must be good at it, so I’ll major in that.” But Indian students already thought CS was the best major. The social pressures were completely different. So, Indian students chose CS — if they had no other plans. CS was the default behavior.
One of the more surprising results was from Thomas W. Price, Neil C.C. Brown, Dragan Lipovac, Tiffany Barnes, and Michael Kölling, Evaluation of a Frame-based Programming Editor. They asked a group of middle school students in a short laboratory study (not the most optimal choice, but an acceptable starting place) to program in Java or in Stride, the new frame-based language and editing environment from the BlueJ/Greenfoot team. They found no statistically significant differences between the two different languages, in terms of number of objectives completed, student frustration/satisfaction, or amount of time spent on the tasks. Yes, Java students got more syntax errors, but it didn’t seem to have a significant impact on performance or satisfaction. I found that totally unexpected. This is a result that cries out for more exploration and explanation.
There’s a lot more I could say, from Colleen Lewis’s terrific ideas to reduce the impact of CS stereotypes to a promising new method of expert heuristic evaluation of cognitive load. I recommend reviewing the papers while they’re still free to download.
Ruthe Farmer at the Office of Science and Technology Policy (OSTP) has been working furiously towards today’s announcements. The Obama Administration is aiming to achieve the goal of CSforAll, and with only a few months left before the new Administration takes off, they’re showing what they’ve put in place today. The full details on all the announcements are here. There’s a webcast at 1 pm EDT today here. The biggest deal to me is the establishment of the CSforAll Consortium (see website here) which is meant to carry on the initiative, no matter who wins in November.
To mark this progress, and celebrate new commitments in support of the President’s initiative, the White House is hosting a summit on Computer Science for All. Key announcements being made today include:
More than $25 million in new grants awarded from the National Science Foundation (NSF) to expand CS education;
A new CSforAll Consortium of more than 180 organizations, which will connect stakeholders with curriculum and resources, as well as track progress towards the goal of Computer Science for All; and
New commitments from more than 200 organizations, ranging from expanded CS offerings within the Girl Scouts of the USA that could reach 1.4 million girls per year, to Code.org supporting professional development for 40,000 additional teachers, to new collaborations to bring CS to students in a variety of settings from African-American churches to family coding nights to tribal Head Start programs to students as Chief Science Officers.
ICER 2016 was just held in Melbourne, Australia, so I found the article linked at the bottom (and from which these images come from) particularly relevant and interesting.
Australia is facing a boom in primary school students, which creates additional demand for teachers. As has been mentioned here previously, there is a shortage of teachers. The shortage isn’t distributed across fields. In particular, over 30% of computing teachers in Australia are teaching without qualification (see image below). When considering other shortages (e.g., declining number of computing teachers in Scotland, as described in the last post), it’s clear that the pipeline of CS teachers is going to be an impediment to CS for all.
But an influx of new students isn’t the only problem our school system needs to address.Shortages in specific subject areas mean that many students are being taught by teachers working outside of their qualifications.
Losing CS Teachers in Scotland: Latest report on CS teacher numbers from Computing At School Scotland
If you can forgive the bias in the graph (what looks like a 90% drop is actually a 25% drop), you will find this to be an important and interesting report. Scotland has one of the strongest computing at schools efforts in the world (see site here), with an advanced curriculum and a large and well-designed professional development effort (PLAN-C). Why are they losing CS teachers?
When I wrote about this in 2014 (the trend has only continued), I pointed out that part of the problem is teachers refusing to shift from teaching Office applications to computer science. The current report doesn’t give us much more insight into why. The point I found most interesting was that Scottish student numbers dropped 11%, and teacher numbers in the other disciplines are also declining (e.g., mathematics teachers are declining by 6% over the same period), but at a much slower rate than the CS decline of 25%. That makes sense too — if you’re a teacher and things are getting tough, stick with the “core” subjects, not the “new” one. It’s worth asking, “How do we avoid this in the US?” and “Can we avoid it?”
We know too little about what happens to CS teachers in the US after professional development. I know of only one study of CS teacher retention in the US, and the observed attrition rate in that study was far worse than 25%. Do we know what US retention rate is for CS teachers? Maybe Scotland is actually doing better than the US?
Today we launch our latest report into the numbers of Computing Science teacher numbers across Scotland. We have carried out this survey in 2012, 2014 and now 2016 as we are concerned about the decreasing number in Computing teachers in Scottish schools. Nationally we now have 17% of schools with no computing specialist and a quarter of Secondary schools have only one CS teacher.