ITiCSE 2016 mini report & photos

It’s the final day of the 21st Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE) here in Arequipa, Peru. It has been a really informative conference in a stunning location. There was a great amount of diversity this year with attendees from 37 countries, and the first time that ITiCSE was held outside Europe. I met a lot of Latin/South American delegates who normally can’t make it to ITiCSE in Europe which was great. Alison, Ernesto, and the rest of the committee really pulled out all the stops. From top to bottom – venue, organization, amazingly thought out excursions, and the spectacular conference dinner – they left covered every detail. And of course the program was packed full of great CSEd research.

The venue is the Universidad Católica San Pablo, who have also put a load of pictures up on their Facebook page, and have a nice article on their webpage (in Espanol) – Headline translation: Experts from more than 35 countries meet in Arequipa to analyze education in Computer Science.

For most of the conference I have been occupied with my working group (WG6) – see here for abstract. I also presented a paper A New Metric to Quantify Repeated Compiler Errors for Novice Programmers. Being pretty busy I haven’t been able to compile much in terms of an in person report of many papers, but here I present my take on three before we get to the photos. All three of these talks really impacted (and at times challenged) my perceptions of some CSED topics.

Mehran Sahami‘s keynote Statistical Modeling to Better Understand CS Students (abstract here) was very insightful. It considered developing statistical models to give insight into the dynamics of student populations. The first case study focused on gender balance and demonstrated that focusing on simple metrics such as percentages can be misleading, and that there are better ways to capture how program changes are impacting the dynamics of gender balance. The second looked at the performance of populations that are experiencing rapid growth. This case study showed one answer to the common statement/observation “the number of weak students is increasing”, and the answer was somewhat surprising – performance during a stage of unprecedented growth was quite stable.

Andrew Luxton-Reilly gave an excellent talk on his paper Learning to Program is Easy. This really challenges the notion that ‘programming is hard’ which is upheld by much of the literature and he argues, the community. This talk really caused me to reconsider my own beliefs about teaching programming, and to question my own expectations, assessment, module learning outcomes, and even program learning outcomes.

Mark Zarb presented a paper he authored with Roger McDermott, Mats Daniels, Asa Cajander and Tony Clear titled Motivation, Optimal Experience and Flow in First Year Computing Science. The authors examined motivation from the perspective of Self Determinism Theory and also considered the optimal state known as Flow – also colloquially known as being in the zone. After discussion how these concepts can be measured they presented preliminary results looking at motivation and flow in a first year computing class. The results were extremely encouraging and made me realize that there is a lot we don’t know about how students think ‘unconsciously’ while learning.

And now, the promised pictures. Don’t forget that there are many more here. I also included some photos from the conference bus tour which to say was all-encompassing is an understatement. We stopped about a half-dozen times at places like town squares, miniature farms (with alpacas of course) and a charming restored colonial-era mansion.

See you in Bolognia for ITiCSE 2017!

A1
Welcome
A8
Mehran Sahami’s keynote
WG6
Working Group 6!
A7
Main auditorium
A2
Lunch tent
A3
Lunch tent
A4
Lunch tent
A6
View from outside the lunch tent with the help of a little zoom
A5
View from outside the lunch tent with the help of a little zoom
C1
Bus tour
C2
Bus tour
C3
Bus tour
C4
Bus tour
C5
Bus tour
C6
Bus tour – La Mansion del Fundador
C7
Bus tour – La Mansion del Fundador
C8
Llama? Alpaca? I really should know by now…
D1
Tour of Santa Catalina Monastery
D2
Tour of Santa Catalina Monastery
D3
Conference Dinner and Peruvian entertainment
E1
Basílica Catedral de Arequipa
E2
Basílica Catedral de Arequipa
E3
Plaza de Armas de Arequipa
E4
Plaza de Armas de Arequipa
E5
Alley off Plaza de Armas de Arequipa
F1
Conference closing – more Peruvian entertainment!
F2
Conference closing – the conference committee joins in the dancing
F3
Adios! Off to Cusco and Machu Picchu!

Update – I couldn’t resist adding a few photos I took in Cusco today:

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DSCN0587

DSCN0419

DSCN0686

DSCN0790

And a parting shot from Machu Picchu Mountain…

ITiCSE Machu

‘Computer Science is Future-Proof’

In the end I chose to put quotes around the title of this post. Initially I didn’t, but I gave in to my reluctance of stating that anything, even my beloved profession, is future-proof.

U.S. News & World report didn’t use the quotes, but they had a subtitle to put a little more context to such a bold claim:


Computer Science is Future-Proof

No matter how technology transforms the jobs market, computer science expertise will be crucial.

Lisette Partelow, theauthor of the above article used a quote (below) which was said to her recently by an educator to exemplify skepticism surrounding all of the attention that CS has been getting lately:

uncertainty about the future job market means that giving students opportunities to learn computer science, while trendy, is essentially pointless. Whatever students learn now will be as out of date as MS-DOS and car phones by the time they can put it to use

Of course Lisette went on to back up her headline, but before I go over that, I would like to put forward two responses to the educator quoted above.

1. A quote from this article in Forbes should do to begin:

 Just because computer languages have a way of becoming obsolete, that doesn’t mean that we don’t need to know how to code. There is an underlying logic to the digital world and we must be capable of operating within that logic in order to function in it.

2. I learned to navigate the MS DOS command prompt around 25 years ago. This included basic commands, navigating the file system, and using system files like autoexec.bat and config.sys. This experience served me well. When I started learning linux, I moved straight in. In fact autoexec.bat and config.sys are very similar to .bashrc and other startup scripts, which I occasionally still use today. This is a great example of a technology becoming outdated, but the skill not, and reflects Lisette’s position below. 

So, back to Lisette, who notes that the U.S. Bureau of Labor Statistics has reported an estimate that there will be three jobs available for every new college graduate from a computer science program in 2016. She also states that “the programming languages my classmates learned in high school and college are probably defunct by now. Yet those who pursued computer science back in those dark ages still managed to get jobs at Google and other prestigious tech firms and kept these jobs as technology changed. Like the rest of us, they learned to adapt on the job as their field shifted.”

I would argue that almost all people who work in or with technology, are not working with the same tools or languages that they first learned. However, they did learn technology. Isn’t this how it is: Learn a technology (then abandon), learn another (then abandon), etc. Nothing puts you in a good position to learn in a fast-paced, constantly changing environment like learning something fast-paced, and constantly changing. In fact, the College Board has found that If a student takes AP computer science in high school, that student is eight times more likely to major in computer science in college.

The above makes the point that exposure is the key here. Exposing students to technology, even fast-paced, constantly-changing, going-to-be-obsolete-tomorrow technology.

I just love how Lisette wraps up her article. This is almost priceless:

Maybe the naysayers are right that the jobs of the future will be super-strange and that many of them won’t require coding skills that look anything like what we are teaching students now. However, computers and computing are taking over nearly every aspect of our lives – Americans look at their smartphones an average of 46 times per day, for example – so it’s likely that some basic understanding of how these systems work and can be leveraged will be an asset, even for intergalactic pilots and teleporter engineers.

I’ll have a go at my own wrap-up:

Technologies (almost by definition) become obsolete. People who can learn, cope, and change with these inevitable obsolescences don’t.