Should I go to Missouri? (#SIGCSE #SIGCSE2015)

The amazing PBL team (Raja, Zbsyzek, Ed and me) have been accepted to run a Puzzle-Based Learning workshop at SIGCSE 2015 in KC, Missouri. I was really excited about this until the recent news about Anita Sarkeesian in Utah broke and it suddenly occurred to me to check to see whether Missouri had concealed carry laws that applied the same way and whether the SIGCSE people had a policy to prevent guns being carried into the auditorium space.

As it turns out, open carry  (for handguns, not long weapons) is permitted in Missouri as of October 11, 2014, (yes, that’s 5 days ago) and these state laws override any local laws on open carry. Concealed carry is also an option but you have to have your permit with you at all times – not carrying the permit will attract a $35 fine! Goodness! I know that would put me off – that’s almost $40 in Australian currency and is nearly 20 minutes of consulting work.

From wikipedia, a gun locked out for safe transport.

From wikipedia, a gun locked out for safe transport.

Missouri also has no permit to purchase, no firearm registration, no owner licensing, no assault weapon law, no magazine capacity restriction and no restriction on “NFA weapons” – which means machine guns.

As far as I can see, SIGCSE has an anti-harassment policy (which is great) but I can’t find anything about guns. I think that far too little heed is paid to the intimidatory nature of someone with a visible gun starting a discussion/argument with a speaker. If we are prepared to stop speakers being stalked, why are we prepared, as an educational community, to allow them to be intimidated by visible firearms?

I am, genuinely, considering whether I should be attending conferences in the US in places where the gun control laws are so at odds with what I’m used to at home. I have a lot to think about on this one and I wonder if this has been brought up with the convenors? Should the international community be thinking more about this as an ongoing issue with attending conferences in the US?

(Please, if I’ve got any of the facts wrong – leap in below and I’ll fix them. Note that “Oh, but KC is perfectly safe” is not actually a fact as KC is in the top 25 most-dangerous cities for gun violence in the US.)


SIGCSE Day 3, “MOOCs”, Saturday, 10:45-12:00pm, (#SIGCSE2014)

This session is (unsurprisingly) of very great interest to the Adelaide Computer Science Education Research group and, as the expeditionary force of CSER, I’ve been looking forward to attending. (I’d call myself an ambassador except I’m not carrying my tuxedo.) The opening talk was “Facilitating Human Interaction in an Online Programming Course” presented by Joe Warren, from Rice University. They’ve been teaching a MOOC for a while and they had some observations to share on how to make things work better. The MOOC is an introduction to interactive programming in Python, based one that Joe had taught for years, which was based on building games. First on-line session was in Fall, 2012, after  face-to-face test run. 19,000 students completed three offerings over Fall ’12, Spring ’13 and Fall ’13.

The goal was to see how well they could put together a high quality on-line course. They sussed recorded videos and machine-graded quizzes, with discussion forums and peer-assessed mini projects. They provided a help desk manned by course staff. CodeSkulptor was the key tool to enable human interaction, a browser based IDE for Python, which was easy to set up, and cloud-saved URLs for code, which were easy to share. (It’s difficult to have novices install tools without causing problems and code visibility is crucial for sharing.) Because they needed a locally run version of Python for interactivity (games focus) so they used Skulpt which translated Python into JavaScript, combined it with CodeMirror, an editor, and then ran it in the browser. CodeSkulptor was built on top.

Students could write code and compile it in the browser, but when they save it a hash is generated for unique storage in a cloud-based account with an access URL – anyone can run your code if you share the URL. (The URL includes a link to CodeSkulptor.org) CodeSkulptor has about 12 million visits with 4 million files saved, which is pretty good. The demo shown had keyboard input, graphic images and sound output – and for those of you know about these things, this is a great result without having to install a local compiler – the browser-based solution works pretty well.

Peer assessment occurred at weekly mini-projects, where the Coursera course provided URLs for CodeSkulptor and a grading rubric which gets sent to students in a web-form. The system isn’t anonymised but students knew it was shared and were encouraged to leave out any personal details in their comments if they wanted to be anonymous (as the file handles were anonymised). (Apparently, the bigger problem was inappropriate content, rather than people worrying about anonymity.) The students run it, assess it in about 10 minutes so it takes about an hour to assess 6 peers. The big advantage is that the code form your URL is guaranteed to run on the grader’s machine because it’s the same browser-based environment. A very detailed rubric was required to ensure good grading: lots of small score items with little ambiguity. The rubric did’t leave much room for assessment – the students were human machines. Why? Having humans grade it was an educational experience and learned from reading and looking at each other’s programs. Also, machine graders have difficulty with animated games, so this is a generalisable approach.

The Help Desk addressed the problem of getting timely expert help for the students – this is a big issue for students. The Code Clinic had custom e-mail that focuses on coding problems (because posting code was not allowed under the class Honour Code). Solutions for common problems were then shared to the rest of the class via the forum. (It looked like the code hash changed every time it got saved? That is a little odd from a naming perspective if true.)

How do CodeClinic work? in Spring 2013 they had about 2,500 help requests. On due days, response time was about 15 minutes (usually averaged 40+), overall handling time average was 6 minutes (open e-mail, solve problem respond). Over 70 days, 3-4 staff sent about 4,000 e-mails. That handling time for a student coding request is very short and it’s a good approach to handling problems at scale. That whole issue about response time going DOWN on due date days is important – that’s normally where I get slammed and slow down! It’s the most popular class at the Uni, which is great!

The chose substantial human-human interaction, using traditional methods on line with peer assessment nd help desks. MOOCs have some advantages over in-person classes – the forums are active because of their size and the help desk scaling works really effectively because it’s always used and hence it makes sense to always staff it. The takeaway is that you have to choose your tools well and you’ll be able to do some good things.

The second talk was “An Environment for Learning Interactive Programming”, also from Rice, and presented by Terry Tang. There was a bit of adverblurb at the start but Terry was radiating energy so I can’t really blame him. He was looking at the same course as mentioned in the previous talk (which saves me some typing, thank you session organisers!). In this talk, Terry was going to focus on SimpleGUI, a browser-based Python GUI library, and Viz mode, a program visualisation tool. (A GUI is a  Graphical User Interface. When you use shapes and windows to interact with a computer, that’s the GUI.)

Writing games requires a fully-functional GUI library so, given the course is about games, this had to be addressed! One could use an existing Python library but these are rarely designed to support Python in the browser and many of them are too complicated as APIs for novice programmers (good to see this acknowledged!).  Desired features of the new library: event-driven support, drawing support and enable students to be able to create simple but interesting programs. So they wrote SimpleGUI . Terry presented a number of examples of this and you can read about it in the talk. (Nick code for “I’m not writing that bit.”) The program was only 227 lines long because a lot of the tricky stuff was being done in the GUI.

Terry showed some examples of student code, built from scratch, on the SimpleGUI, and showed us a FlappyBirds clone – scoring 3, which got a laugh from the crowd.

Terry then moved on to Viz mode, to meet the requirement of letting students visualise the execution of their own code. One existing solution is the Online Python Tutor, which runs code on a server, generates a log file and then ships the trace to some visualisation code in the browse (in JavaScript) to process the trace and produce a state diagram. The code, with annotations, is presented back to the user and they can step through the code, with the visualisations showing the evolution of state and control over time. The resulting visualisation is pretty good and very easy to follow. Now this is great but it runs on a backend server, which could melt on due dates, and OPT can’t visualise event-driven programs (for those who don’t know, game programming is MOSTLY event-driven). So they wrote Viz mode.

From CodeSkulptor, you can run your program in regular or Viz mode. In Viz mode, a new panel with state diagrams shows up, and a console that shows end tags. This is all happening in the browser, which scales well although there are limits to computation in this environment, and is all integrated with the existing CodeSkulptor environment. Terry then showed some more examples.

An important note is that event handlers don’t automatically fire in Viz mode so an GUI elements will have additional buttons to explicitly fire events (like Draw for graphical panes or Timers for events like that). It’s a pretty good tool, from what we were shown. Overall, the Rice experience looks very positive but their tool set and approach to support appears to be the keys to their success. Only some of the code is open source, which is a pity.

Barb Ericson asked a great question: could you set up something where the students are forced to stop and then guess what is going to happen next? They haven’t done it yet but, as Joe said, they might do it now!

The final talk was not from Rice but was from Australia, woohoo (Melbourne and NICTA)! “Teaching Creative Problem Solving in a MOOC” was presented by Carleton Coffrin from NICTA. Carleton was at Learning@SCale earlier and what has been seen over the past year is MOOCs 1.0 – scaling content delivery, with linear delivery, multiple-choice questions and socialisation only in the forums. What is MOOC 2.0? Flexible delivery, specific assessments, gamification, pedagogy, personalised and adaptive approaches. Well, it turns out that they’ve done it so let’s talk about it with a discrete optimisation MOOC offered on Coursera by University of Melbourne. Carleton then explained what discrete optimisation was – left to you to research in detail, dear reader, but it’s hard and the problems are very complex (NP-hard for those who care about such things). Discrete optimisation in practice is trying to explain known techniques to complicated real-world problems. Adaptation and modification of existing skills is a challenge.

How do we prepare students for new optimisation problems that we can’t anticipate? By teaching general problem-solving skills.

What was the class design? The scope of the course was over six areas in the domain, which you can find in the paper, and five assignments of NP-hard complexity. In the lectures, the lecturer used a weatherman format with a lecturer projected over the slides with a great deal of enthusiasm – and a hat. (The research question of the global optimum for hats was not addressed.) The lecturer was very engaging and highly animated, which added to the appeal of the recorded lectures. The instructor constructs problems, students write code and generate solution, encode the solution in a standard format, this is passed back, graded and feedback is returned. Students get the feedback and can then resubmit until the student is happy with their grade. (Yay! I love to see this kind of thing.) I will note that the feedback told them what quality of solution that had to present rather than suggestions of how to do it. Where constraint violations occurred, there was some targeted feedback. Overall, the feedback was pretty reasonable but what you’d expect in good automated feedback. The students did demonstrate persistence in response to this feedback.

From a pedagogical perspective, discovery-based learning was seen to be very important as part of the course. Rather than teach mass, volume and density by using a naked formula, exemplars were presented using water and floating (or sinking) objects to allow the students to explore the solutions and the factors. The material is all in the lectures but it’s left to the students to find the right approach to find solutions to new problems – they can try different lecture ideas on different problems.

The instructor can see all of the student results, rank them, strip out the results and then present a leader board to show quality. This does allow students to see that higher numbers are achieved but I’m not sure that there’s any benefit beyond what’s given in the hints. They did add a distribution graph for really large courses as the leader board got too long. (I’m not a big fans of leader boards, but you know that.)

The structure of the course was suggested, with introductory materials, but then students could bounce around. On-line doesn’t require a linear structure! The open framework was effectively require for iterative improvement of the assignments.

How well did it work? 17,000 people showed up. 795 stayed to the end, which is close to what we’d expect from previous MOOC data but still a bit depressing. However, only 4,000 only tried to do the assignments and, in the warm up, a lot of people dropped out after the warm-up assignment. Looking at this, 1,884 completed the warm-up and stayed (got qualified), which makes the stay rate about 42%. (Hmm, not sure I agree with this numerical handling but I don’t have a better solution.)

Did students use the open framework for structure? It looks like there was revision behaviour, using the freedom of the openness to improve previous solutions with new knowledge. The actual participation rate was interesting because some students completed in 20, and some in 60.

Was it a success or a failure? Well, the students love it (yeah, you know how I feel about that kind of thing). Well, they surveyed the students at the end and they had realised that optimisation takes time (which is very, very true). The overall experience was positive despite the amount of work involved, and the course was rated as being hard.  The students were asked what their favourite part of the course and this was presented as a word cloud. Programming dominated (?!) followed by assignments (?!?!?!?!).

Their assignment choice was interesting because they deliberately chose examples that would work for one solution approach but not another. (For example, the Travelling Salesman Problem was provided at a scale where the Dynamic Programming solution wouldn’t fit into memory.)

There’s still a lot of dependency on this notion that “leaderboards are motivating”. From looking at the word cloud, which is a very high level approach, the students enjoyed the assignments and were happy to do programming, in a safe, retry-friendly (and hence failure tolerant) environment. In my opinion, the reminder of the work they’ve done is potentially more likely to be the reason they liked leader boards rather than as a motivating factor. (Time to set up a really good research study!)

Anyway, the final real session was a corker and I greatly enjoyed it! On to lunch and FRIED CHICKEN.


SIGCSE, day 2, Le déjeuner des internationaux (#SIGCSE2014)

We had a lunch for the international contingent at SIGCSE, organised by Annemieke Craig from Deakin and Catherine Lang from Latrobe (late of Swinburne). There are apparently about 80 internationals here and we had about 24 at the lunch. Australians were over-represented but there were a lot of familiar faces and that’s always nice in a group of 1300 people.

Lots of fun and just one more benefit of a good conference. The group toasted Claudia Szabos’ success with the Best Paper award, again. We’re still having a lot of fun with that.


SIGCSE Day 2, Keynote 2, “Transforming US Education with Computer Science”, (#SIGCSE2014)

Today’s keynote, “Transforming US Education with Computer Science”, is being given by Hadi Partovi from Code.org. (Claudia and I already have our Code.org swag stickers.)

There are 1257 registered attendees so far, which gives you some idea of the scale of SIGCSE. This room is pretty full and it’s got a great vibe. (Yeah, yeah, I know, ‘vibe’. If that’s the worst phrase I use today, consider yourself lucky, D00dz.) The introductory talk included a discussion of the SIGCSE Special Projects small grant program (to US$5,000). They have two rounds a year so go to SIGCSE’s website and follow the links to see more. (Someone remind me that it’s daylight saving time on Saunday morning, the dreaded Spring forward, so that I don’t miss my flight!)

SIGCSE 2015 is going to be in Kansas City, by the way, and I’ve heard great things about KC BBQ – and they have a replica of the Arch de Triomphe so… yes. (For those who don’t know, Kansas City is in Missouri. It’s name after the river which flows through it, which is named after the local Kansa tribe. Or that’s what this page says. I say it’s just contrariness.) I’ve never been to Missouri, or Kansas for that matter, so I could tick off two states in the one trip… of course, then I’d have to go to Topeka, well just because, but you know that I love driving.

We started the actual keynote with the Hour of Code advertising movie. I did some of the Hour of Code stuff from the iOS app and found it interesting (I’m probably being a little over-critical in that half-hearted endorsement. It’s a great idea. Chill out, Nick!)

Hadi started off referring to last year’s keynote, which questioned the value of code.org, which started as a hobby. He decided to build a larger organisation to try and realise the potential of transforming the untapped resource into a large crop of new computer scientists.

Who.what is Code.org?

  • A marketing organisation to make videos with celebrities?
  • A coalition of tech companies looking for employees?
  • A political advocacy group of educations and technologies?
  • Hour of code organisers?
  • An SE house that makes tutorials
  • Curriculum organisers?
  • PD organisation?
  • Grass roots movement?

It’s all of the above. Their vision is that every school should teach it to every student or at least give them the opportunity. Why CS? Three reasons: job gap, under-represented students and CS is foundational for every student in the 21st Century. Every job uses it.

Some common myths about code.org:

  • It’s all hype and Hour of Code – actually, there are many employees and 15 of them are here today.
  • They want to go it alone – they have about 100 partners who are working with the,
  • They are only about coding and learning to code – (well, the name doesn’t help) they’re actually about teaching fundamentals of Computer Science
  • This is about the software industry coming in to tell schools how to do their jobs – no, software firms fund it but they don’t run the org, which is focused on education, down to the pre-school level

Hmm, the word “disrupt” has now been used. I don’t regard myself as a disruptive innovator, I’m more of a seductive innovator – make something awesome and you’ll seduce people across to it, without having to set fire to anything. (That’s just me, though.)

Principle goals of Code.org start with “Educate K-12 students in CS throughout the US”. That’s their biggest job. (No surprise!) Next one is to Advocate to remove legislative barriers and the final pillar is to Celebrate CS and change perceptions.

Summary of first year – hour of code, 28 million students in 35,000 classrooms with 48% girls (applause form the audience), in 30 languages over 170 countries. 97% positive ratings of the teacher experience versus 0.2% negative. In their 20 hour K-8 Intro Course, 800,000 students in 13,000 students, 40% girls. In school district partnerships they have 23 districts with PD workshops for about 500 teachers for K-12. In their state advocacy role, they’ve changed policy in 5 states. Their team is still pretty lean with only 20 people but they’re working pretty hard with partnerships across industry, nonprofit and government. Hadi also greatly appreciated the efforts of the teachers who had put in the extra work to make this all happen in the classroom.

They’re working on a full curriculum with 20 hour modules all the way up to middle school, aligned with common core. From high school up, they go into semester courses. These course are Computer Science or leverage CS to teach other things, like maths. (Obviously, my ears pricked up because of our project with the Digital Technologies National Curriculum project in Australia.)

The models of growth include an online model, direct to teachers, students and parents (crucial), fuelled by viral marketing, word-of-mouth, volunteers, some A/B testing, best fit for elementary school and cost effectiveness. (On the A/B testing side, there was a huge difference in responses between a button labelled “Start” and a button labelled “Get started”. Start is much more successful! Who knew?) Attacking the problem earlier, it’s easy to get more stuff into the earlier years because they are less constrained in requirements to teach specific content.

The second model of growth is in district partnerships, where the district provides teachers, classrooms and computers. Code.org provide stipends, curriculum, marketing. Managing costs for scale requires then to aim for US$5-10K per High School, which isn’t 5c but is manageable.

The final option for growth is about certification exams, incentives, scholarships and schools of Ed.

Hadi went on to discuss the Curriculum, based on blockly, modified and extended. His thoughts on blended learning were that they achieved making learning feel like a game with blended learning (The ability to code Angry Birds is one of the extensions they developed for blackly) On-line and blended learning also makes a positive difference to teachers. On-line resources most definitely don’t have to remove teachers, instead, done properly, they support teachers in their ongoing job. Another good thing is to make everything web-based, cross-browser, which reduces the local IT hassle for CS teachers. Rather than having to install everything locally, you can just run it over the web. (Anyone who has ever had to run a lab knows the problem I’m talking about. If you don’t know, go and hug your sys admin.) But they still have a lot to learn: about birding game design and traditional curriculum, however they have a lot of collaborations going on. Evaluation is, as always, tricky and may combine traditional evaluation and large-scale web analytics. But there are amazing new opportunities because of the wealth of data and the usage patterns available.

He then showed three demos, which are available on-line, “Building New Tutorial Levels”, new tutorials that show you how to create puzzles rather than just levels through the addition of event handing (with Flappy Bird as the example), and the final tutorial is on giving hints to students. (Shout outs to all of the clear labelling of subgoals and step achievement…) That last point is great because you can say “You’re using all the pieces but in the wrong way” but with enough detail to guide a student, adding a hint for a specific error. There are about 11,000,000 submissions for providing feedback on code – 2,000,000 for correct, 9,000,000 for erroneous. (Code.org/hints)

So how can you help Code.org?

If tour in a Uni, bring a CS principles course to the Uni, partner with your school of Ed to bring more CS into the Ed program (ideally a teaching methods course). Finally, help code. org scale by offering K-5 workshops for them. You can e-mail univ@code.org if you’re interested. (Don’t know if this applies in Australia. Will check.) This idea is about 5 weeks old so write in but don’t expect immediate action, they’re still working it out.

If you’re just anyone, Uni or not? Convince your school district to teach CS. Code.org will move to your region in if 30+ high schools are on board. Plus you can leap into and give feedback on the curriculum or add hints to their database. There are roughly a million students a week doing Hour of Code stuff so there’s a big resource out there.

Hadi moved on to the Advocate pillar. Their overall vision is that CS is foundational – a core offering one very school rather than a vocational specialisation for a small community. The broad approach is to change state policy. (A colleague near me muttered “Be careful what you wish for” because that kind of widespread success would swamp us if we weren’t prepared. Always prepare for outrageous success!)

At the national level, there is a CS Education Act with bi-partisan sponsors in both house, to support STEM funding to be used as CS, currently before the house. In the NCAA, there’s a new policy published from an idea spawned at SIGCSE, apparently by Mark! CS can now count as an NCAA scholarship, which is great progress. At the state level, Allowing CS to satisfy existing high school math/science graduation requirements but this has to be finalised with the new requirement for Universities to allow CS to meet their math/science requirements as well! In states where CS counts, CS enrolment is 50% higher (Calc numbers are unchanged), with 37% more minority representation. The states with recent policy changed are are small but growing. Basically, you can help. Contact Code.org if your state or district has issues recognising CS. There’s also a petition on the code.org site which is state specific for the US, which you can check out if you want to help. (The petition is to seek recognition that everyone in the US should have the opportunity to learn Computer Science.)

Finally, on the Celebrate pillar, they’ve come a long way from one cool video, to Hour of Code. Tumblr took 3.5 years to reach 15,000,000, Facebook took 3 years, Hour of Code took 5 days, which is very rapid adoption. More girls participated in CS in US schools in one week than in the previous 70 years. (Hooray!) And they’re  doing it again in CSEd Week from December 8-14. Their goal is to get 100  million students to try the Hour of Code. See if you can get it on the Calendar now – and advertise with swag. 🙂

In closing, Hadi believes that CS is at an incredible inflection pint, with lots of opportunities, so now is the time to try stuff or, if it didn’t work before, try it again because there’s a lot of momentum and it’s a lot easier to do now. We have growing and large numbers. When we work together towards a shared goal, anything is possible.

Great talk, thanks, Hadi!


SIGCSE 2014: Collecting and Analysing Student Data 1, Paper 2, Thursday 3:15 – 5:00pm (#SIGCSE2014)

Whoo! I nearly burnt out a digit writing up the first talk but it’s a subject close to my heart. I’ll try to be a little more terse for these next two talks.

The second talk in this session was “Blackbox: A Large Scale Repository of Novice Programmers’ Activity” by the amazing Blackbox team at Kent, Neil Brown, Michael Kölling, Davin McCall, and Ian Utting. The Blackbox data is the anonymised student data from students coding into the BlueJ Java programming environment. It’s a rich source of information on how students code and Mark and I have been scheming to do something with the Blackbox data for some time. With Ian and Neil here, it’s a good opportunity to steal their brains. I tried to get Ian to agree to doing all the work but it turns out that he’s been in the game long enough to not say “yes” when someone asks him to without context. (Maybe it’s just me.)

Michael was presenting, with some help from Neil, and reviewed the relationship between Blackbox and BlueJ. BlueJ is an educational programming environment for CS education using Java, dating back to the original Blue in 1996. (For those who don’t know, that’s old for this kind of thing. We should throw it a party.) BlueJ is a graphically operated development environment so novice programmers can drag things out to build programs. It’s a well-established and widely used environment.

(Hey, that means BlueJ is 18. Someone buy BlueJ a beer.)

BlueJ has about 2,000,000 users in 2013, who use it for about three months and then move on (it’s not a production tool, it’s a learning environment). The idea of Blackbox came out of SIGCSE sessions about three years ago where some research questions were raised, nice set-up, good design and really small student groups. One of our common problems is having enough students to actually do a big study and, frankly, all of us are curious about how students code. (It’s really hard to tell this from the final program, trust me.) So BlueJ has lots of users, can we look at their data and then share this with people?

Of course, the first question is “what do we collect?” Normally, we’d collect what we need to answer a research question but this data was going to be used to support lots of different (and currently unasked) research questions. The community was consulted at SIGCSE in 2012 but there has been an evolution of this over time. There are a lot of things collected – go and look at them in the paper because Michael flicked past that slide! 🙂

From an ethical standpoint, participation is an explicit decision made by the student to have their data collected or not. (This does raise the spectre of bias, especially as all the students must be over 16 for legal reasons.) So it’s opt in and THEN anonymised just to make it totally tasty from an ethical perspective.

Session data is collected for each session: start time, end time, project, path and userID (centrally anonymised for tracking).

So much for keeping it short, hey? Here’s a quick picture to give you a break.

dog-ate-my-homework

Other things that can be captured are object creation and invocation among many other useful measures.  For me, the fact that you can see how and when students are testing is fascinating, as it allows us to evaluate the whole expectation, observation and reflection scientific cycle in action.

The Blackbox project has already been running for 9 months. The opt-in rate is 40% (higher than I or anyone else expected). This means that there’s data from 250,000 users, recording roughly 11 events per second, over more than 1,000,000 projects and 20,000,000 compilations. What a fantastic resource! Michael then handed over to Neil to talk about the challenges.

Neil talked about tracking users, starting front he problem that one machine profile does not necessarily correspond to one user. Another problem is anonymisation, stripping project paths and the code where possible. You can’t guarantee anonymisation, because people sometimes use their own names as variable or class names, but they do what they can. It’s made clear on opt-in what’s going to happen. Data integrity is another challenge. Is it complete? No – it’s client side and there’s no guarantee of completeness, or even connectivity. But the Data that you do have for each session you is consistent. So the data is consistent but not complete. If you want, locally, you can tie your local data to the Blackbox data that they have on your students but then ethics becomes your problem. This can be done by Experiment and Participant Identifiers as part of the set-up so your students can be grouped. More example mini analyses are in the paper.

Looking at Error Frequency, Neil talked about certain errors and how their frequency changes over the weeks of 2013 (Semicolon expected, unknown variable). Over time, the syntax errors decreased (suggesting a learning effect) but others stay more constant.

The data is not completely open, and you need to request access as a researcher, sign a privacy and access restriction agreement. Students need not apply! There’s a SIGCSE workshop on this Saturday but I can’t go as my Puzzle Based Learning workshop is on at the same time. Great resource, go and check it out!

The final talk was “Using CodeBrowser to Seek Difference Between Novice Programmers” by Kenny Heinonen, Kasper Hirvikoski, Matti Luukkainen, and Arto Vihavainen, University of Helsinki,


SIGCSE 2014: Collecting and Analysing Student Data 1, “AP CS Data”, Thursday 3:15 – 5:00pm (#SIGCSE2014)

The first paper, “Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale using AP CS Data”, from Barbara Ericson and Mark Guzdial, has been mentioned in these hallowed pages before, as well as Mark’s blog (understandably). Barb’s media commitments have (fortunately) slowed down btu it was great to see so many people and the media taking the issue of under-representation in AP CS seriously for a change. Mark presented and introduced the Advanced Placement CS program which is the only nationwide measure of CS education in the US. This allows us tto use the AP CS to compare with other AP exams, find who is taking the AP CS exams and how well do they perform. Looking longitudinally, how has this changed and what influences exam-taking? (There’s been an injection of funds into some states – did this work?)

The AP are exams you can take while in secondary school that gives you college credit or placement in college (similar to the A levels, as Mark put it). There’s an audit process of the materials before a school can get accreditation. The AP exam is scored 1-5, where 3 is passing. The overall stats are a bit worrying, when Back, Hispanic and female students are grossly under-represented. Look at AP Calculus and this really isn’t as true for female students and there is better representation for Black and Hispanic students. (CS has 4% Black and 7.7% Hispanic students when America’s population is 13.1% Black students and 16.9% Hispanic) The pass rates for AP Calculus are about the same as for AP CS so what’s happening?

Looking at a (very cool) diagram, you see that AP overall is female heavy – CS is a teeny, tiny dot and is the most male dominated area, and 1/10th the size of calculus. Comparing AP CS to the others. there has been steady growth since 1997 in Calculus, Biology, Stats, Physics, Chem and Env Science AP exams – but CS is a flat, sunken pancake that hasn’t grown much at all. Mark then analysed the data by states, counting the number of states in each category along features such as ‘schools passing audit/10K pop’, #exams/pop and % passing exams. Mark then moved onto diversity data: female, Black and Hispanic test takers. It’s worth noting that Jill Pala made the difference to the entire state she taught in, raising the number of women. Go, Jill! (And she asked a really good question in my talk, thanks again, Jill!)

How has this changed overtime? California and Maryland have really rapid growth in exam takers over the last 6 years, with NSF involvement. But Michigan and Indiana have seen much less improvement. In Georgia, there’s overall improvement, but mostly women and Hispanic students, but not as much for Black students. The NSF funding appears to have paid off, GA and MA have improved over the last 6 years but female test takers have still not exceeded 25% in the last 6 years.

Why? What influences exam taking?

  1. The wealth in the state influences number of schools passing audit
  2. Most of the variance in the states comes from under-representation in certain groups.

It’s hard to add wealth but if you want more exam takers, increase your under-represtentation group representation! That’s the difference between the states.

Conclusions? It’s hard to compare things most of the time and the AP CS is the best national pulse we have right now. Efforts to improve are having an effect but wealth matters, as in the rest of education.

All delivered at a VERY high speed but completely comprehensible – I think Mark was trying to see how fast I can blog!

 


SIGCSE Keynote #1 – Computational Thinking For All, Robert M. Panoff, Shodor Education Foundation

Bob Panoff is the wonder of the 2014 SIGCSE Award for Outstanding Contribution to Computer Science Education and so he gets to give a keynote, which is a really good way to do it rather than delaying the award winners to the next year.

Bob kicked off with good humour, most of which I won’t be able to capture, but the subtext of hits talk is “The Power and the Peril”, which is a good start to the tricky problem of Comp thinking for all. What do we mean by computational thinking? Well, it’s not teaching programming, we can teach programming to enhance computational thinking but thinking is the key word here. (You can find his slides here: http://shodor.org/talks/cta/)

Bob has faced the same problem we all have: that of being able to work on education when your institution’s focus is research. So he went to start an independent foundation where CS Ed where such activities could be supported. Bob then started talking about expectation management, noting that satisfaction is reality divided by expectations – so if you lower your expectations. (I like that and will steal it.)

Where did the name Shodor come from? Bob asked us if we knew and then moved to put us through a story, which would answer this question. As it turns out, he name came from a student’s ungenerous pattern characterisation of Bob, whose name he couldn’t remember, as “short and kinda dorky looking”.

I need to go and look at the Shodor program in detail because they have a lawyered apprenticeship model, teaching useful thinking and applied skills, to high schoolers, which fills in the missing math and 21st century skills that might prevent them from going further in education. Many of the Shodor apprentices end up going on as first-in-family to college, which is a great achievement.

Now, when we say Computational Science Education, is it Computational (Science Education) or (Computational Science) Education? (This is the second slide in the pack). The latter talks about solving the right problem, getting the problem solved in the right way and actually being right.

Right Answer = Wrong Answer + Corrections

This is one of the key issues in modelling over finite resources, because we have to take shortcuts in most systems to produce a model that will fit. Computationally, if we have a slightly wrong answer (because of digital approximations or so on), then many iterations will make it more and more wrong. If we remember to adjust for the corrections, we can still be right. How helpful is it to have an exact integral that you can’t evaluate, especially when approximations make that exact integral exceedingly unreliable? (The size of the Universe is not 17cm, for example.)

Elegant view of science: Expectation, Observation and Reflection. What do you expect to see? What do you see? What does it actually mean Programming is a useful thought amplifier because we can get a computer to do something BUT before you get to the computer, what do you expect the code to work and how will you now what it’s doing? Verification and validation are important job skills, along with testing, QA and being able to read design documents. Why? Because then you have to be able to Expect, Observe and Reflect. Keyboard skills do not teach you any of this and some programming ‘tests’ are more keyboard skills than anything else.

(If you ever have a chance to see Bob talk, get there. He’s a great speaker and very clever and funny at the same time.)

Oh dear.

Oh dear.

Can we reformable the scientific method and change the way that we explain science to people? What CAN I observe? What DO I observe? How do I know that it’s right? How am I sure? Why should I care? A lot of early work was driven by wonder (Hey, that’s cool) rather than hypothesis driven (which is generally what we’re supposed to be doing.) (As a very bad grounded theorist, this appeals.)

How do we produce and evaluate models? Well, we can have an exact solution to an exact model, an exact solution to an approximate model (not real but assessable), an approximate solution to an exact model and an approximate solution to an approximate model. Some of the approximation in the model is the computing itself, with human frailty thrown into the mix.

What does Computational Thinking allow you to? To build and explore a new world where new things are true and other things are false, because this new universe is interesting to us. “The purpose of computing is insight, not numbers” — R. Hamming, “If you can’t trust the numbers, you won’t get much insight” — R. Panoff. Because the computer is dumb, we have to do more work and more thinking to make up for the fast and accurate moron that does what we order it to do.

“Killing off the big lie” – every Math class you have, you see something on page 17 showing a graph and an equation which has “as you can see from the graph” starting it. Bob’s lament is that he CAN’T see from the graph and not many other people can either. We just say that but, many times, it’s a big lie. Pattern recognition and characterisation are more important than purely manipulating numbers. (All of this is on the Shodor website) Make something dynamic and interactive and student can explore, which allows them to think about what happens when they change things – set an expectation, observe and reflect, change conditions and do it again.

Going to teachers, they know that teaching mathematics is frequently teaching information repetitively with false rules so that simple assessment can be carried out. (Every histogram must have 10 bars and so many around the mean, etc) Using computing to host these sorts of problems allows us to change the world and then see what happens. Rather than worry about how long it takes students to produce one histogram on paper, they can make one in an on-line environment and play with it. There are better and worse ways to represent data so let’s use computational resources to allow everyone to do this, even when they’re learning. This all comes down to different models as well as different representations. (There is value to making kids work up a histogram by hand but there are many ways to do this and we can change the question and the support and remove the tedium of having to use paper and pen to do one, when we could use computing to do the dull stuff.)

Bob emphasised the importance of drawing pictures and telling stories, they hand-waving that communicates site complicated concepts to people. “What’s this?” “I don’t know but here comes a whole herd of them!”

The four things we need for computational thinking are: Quantitative Reasoning, Algorithm Thinking, Analogic Thinking, and Multi-scale Modelling. Bob showed an interesting example of calculating a known result when you don’t know the elements by calculating the relative masses of the Earth and Pluto using Google and just typing “mass of the earth / mass of pluto” Is this right? What is our reason for believing it? You would EXPECT things to be well-know but what do you OBSERVE? Hmm, time to REFLECT. (As the example, the earth mass value varies dramatically between sources – Google tells you where it gets the information but a little digging reveals that things don’t align AND the values may change over time. The answer varies depends upon the model you use and how you measure it. All of the small differences add up.)

The next example is the boiling point of Radium, given as 1,140C by Google, but the matching source doesn’t even agree with this! If you can’t trust the numbers then this is yet another source of uncertainty and error in our equations.

Even “=” has different interpretations – F = ma is the statement that force occurs as mass accelerates. In nRT = PV, we are saying that energy is conserved in these reactions. dR/dT = bR – the number of rabbits having bunnies will affect the rate of change of rabbits. No wonder students have trouble with what “s=3” means, on occasion. Speaking of meaning, Bob played this as an audio clip, but I attach the text here:

The missile knows where it is at all times. It knows this because it knows where it isn’t. By subtracting where it is from where it isn’t, or where it isn’t from where it is (whichever is greater), it obtains a difference, or deviation. The guidance subsystem uses deviations to generate corrective commands to drive the missile from a position where it is to a position where it isn’t, and arriving at a position where it wasn’t, it now is. Consequently, the position where it is, is now the position that it wasn’t, and it follows that the position that it was, is now the position that it isn’t.

In the event that the position that it is in is not the position that it wasn’t, the system has acquired a variation, the variation being the difference between where the missile is, and where it wasn’t. If variation is considered to be a significant factor, it too may be corrected by the GEA. However, the missile must also know where it was.

The missile guidance computer scenario works as follows. Because a variation has modified some of the information the missile has obtained, it is not sure just where it is. However, it is sure where it isn’t, within reason, and it knows where it was. It now subtracts where it should be from where it wasn’t, or vice-versa, and by differentiating this from the algebraic sum of where it shouldn’t be, and where it was, it is able to obtain the deviation and its variation, which is called error.

Try reading that out loud! Bob then went on to show us some more models to see how we can experiment with factors (parameters) in a dynamic visualisations in a way that allows us to problem solve. So schoolkids can reduce differential equations to simple statements relating change and then experiment – without having to know HOW to solve differential equations (what you have now is what you had then, modified by change). This is model building without starting with programming, it’s starting with modelling, showing what they can do and then exposing how this approach can be limited – which provides a motivation to learn how to program so you can fix the problems in this model.

Overall, an excellent talk about an interesting project attacking the core issue of getting students to think in the right way, instead of just getting them to conform to some dry mechanistic programming approaches. The National Computer Science Institute is doing work across the US (if they come and do a workshop, you have to give them a mug and they have a lot of mugs). NCSI are looking for summer workshop hosts so, if you’re interested, you should contact them (not me!) Here’s one of the quotes from the end:

“It was once conjectured that a million monkeys typing on a million typewriters could eventually produce all of the works of Shakespeare. Now, thanks to the Internet, we know that this is not true” (Bob Willinsky (possible attribution, spelling may be wrong))

What would happen if the Internet went away? That’s a big question and, sadly, Bob started to run out of time. Our world runs in parallel so we need to have be able to think in parallel as well. Distributed computation requires us to think in different ways and that gets hard, quickly.

Bob wrapped it up by saying that Shodor was a village, a lot of fun and was built upon a lot of funding. Great talk!