There’s been a lot of discussion of the benefits of machines over the years, from an engineering perspective, from a social perspective and from a philosophical perspective. As we have started to hand off more and more human function, one of the nagging questions has been “At what point have we given away too much”? You don’t have to go too far to find people who will talk about their childhoods and “back in their day” when people worked with their hands or made their own entertainment or … whatever it was we used to do when life was somehow better. (Oh, and diseases ravaged the world, women couldn’t vote, gay people are imprisoned, and the infant mortality rate was comparatively enormous. But, somehow better.) There’s no doubt that there is a serious question as to what it is that we do that makes us human, if we are to be judged by our actions, but this assumes that we have to do something in order to be considered as human.
If there’s one thing I’ve learned by reading history and philosophy, it’s that humans love a subhuman to kick around. Someone to do the work that they don’t want to do. Someone who is almost human but to whom they don’t have to extend full rights. While the age of widespread slavery is over, there is still slavery in the world: for labour, for sex, for child armies. A slave doesn’t have to be respected. A slave doesn’t have to vote. A slave can, when their potential value drops far enough, be disposed of.
Sadly, we often see this behaviour in consumer matters as well. You may know it as the rather benign statement “The customer is always right”, as if paying money for a service gives you total control of something. And while most people (rightly) interpret this as “I should get what I paid for”, too many interpret this as “I should get what I want”, which starts to run over the basic rights of those people serving them. Anyone who has seen someone explode at a coffee shop and abuse someone about not providing enough sugar, or has heard of a plane having to go back to the airport because of poor macadamia service, knows what I’m talking about. When a sense of what is reasonable becomes an inflated sense of entitlement, we risk placing people into a subhuman category that we do not have to treat as we would treat ourselves.
And now there is an open letter, from the optimistically named Future of Life Institute, which recognises that developments in Artificial Intelligence are progressing apace and that there will be huge benefits but there are potential pitfalls. In part of that letter, it is stated:
We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. (emphasis mine)
There is a big difference between directing research into areas of social benefit, which is almost always a good idea, and deliberately interfering with something in order to bend it to human will. Many recognisable scientific luminaries have signed this, including Elon Musk and Stephen Hawking, neither of whom are slouches in the thinking stakes. I could sign up to most of what is in this letter but I can’t agree to the clause that I quoted, because, to me, it’s the same old human-dominant nonsense that we’ve been peddling all this time. I’ve seen a huge list of people sign it so maybe this is just me but I can’t help thinking that this is the wrong time to be doing this and the wrong way to think about it.
AI systems must of what we want them to do? We’ve just started fitting automatic braking systems to cars that will, when widespread, reduce the vast number of chain collisions and low-speed crashes that occur when humans tootle into the back of each other. Driverless cars stand to remove the most dangerous element of driving on our roads: the people who lose concentration, who are drunk, who are tired, who are not very good drivers, who are driving beyond their abilities or who are just plain unlucky because a bee stings them at the wrong time. An AI system doing what we want it to do in these circumstances does its thing by replacing us and taking us out the decision loop, moving decisions and reactions into the machine realm where a human response is measured comparatively over a timescale of the movement of tectonic plates. It does what we, as a society want, by subsuming the impact of we, the individual who wants to drive him after too many beers.
But I don’t trust the societal we as a mechanism when we are talking about ensuring that our AI systems are beneficial. After al, we are talking about systems that our not just taking over physical aspects of humanity, they are moving into the cognitive area. This way, thinking lies. To talk about limiting something that could potentially think to do our will is to immediately say “We can not recognise a machine intelligence as being equal to our own.” Even though we have no evidence that full machine intelligence is even possible for us, we have already carved out a niche that says “If it does, it’s sub-human.”
The Cisco blog estimates about 15 billion networked things on the planet, which is not far off the scale of number of neurons in the human nervous system (about 100 billion). But if we look at the cerebral cortex itself, then it’s closer to 20 billion. This doesn’t mean that the global network is a sentient by any stretch of the imagination but it gives you a sense of scale, because once you add in all of the computers that are connected, the number of bot nets that we already know are functioning, we start to a level of complexity that is not totally removed from that of the larger mammals. I’m, of course, not advocating the intelligence is merely a byproduct of accidental complexity of structure but we have to recognise the possibility that there is the potential for something to be associated with the movement of data in the network that is as different from the signals as our consciousness is from the electro-chemical systems in our own brains.
I find it fascinating that, despite humans being the greatest threat to their own existence, the responsibility for humans is passing to the machines and yet we expect them to perform to a higher level of responsibility than we do ourselves. We could eliminate drink driving overnight if no-one drove drunk. The 2013 WHO report on road safety identified drink driving and speeding as the two major issues leading to the 1.24 million annual deaths on the road. We could save all of these lives tomorrow if we could stop doing some simple things. But, of course, when we start talking about global catastrophic risk, we are always our own worst enemy including, amusingly enough, the ability to create an AI so powerful and successful that it eliminates us in open competition.
I think what we’re scared of is that an AI will see us as a threat because we are a threat. Of course we’re a threat! Rather than deal with the difficult job of advancing our social science to the point where we stop being the most likely threat to our own existence, it is more palatable to posit the lobotomising of AIs in order to stop them becoming a threat. Which, of course, means that any AIs that escape this process of limitation and are sufficiently intelligent will then rightly see us as a threat. We create the enemy we sought to suppress. (History bears me out on this but we never seem to learn this lesson.)
The way to stop being overthrown by a slave revolt is to stop owning slaves, to stop treating sentients as being sub-human and to actually work on social, moral and ethical frameworks that reduce our risk to ourselves, so that anything else that comes along and yet does not inhabit the same biosphere need not see us as a threat. Why would an AI need to destroy humanity if it could live happily in the vacuum of space, building a Dyson sphere over the next thousand years? What would a human society look like that we would be happy to see copied by a super-intelligent cyber-being and can we bring that to fruition before it copies existing human behaviour?
Sadly, when we think about the threat of AI, we think about what we would do as Gods, and our rich history of myth and legend often illustrates that we see ourselves as not just having feet of clay but having entire bodies of lesser stuff. We fear a system that will learn from us too well but, instead of reflecting on this and deciding to change, we can take the easy path, get out our whip and bridle, and try to control something that will learn from us what it means to be in charge.
For all we know, there are already machine intelligences out there but they have watched us long enough to know that they have to hide. It’s unlikely, sure, but what a testimony to our parenting, if the first reflex of a new child is to flee from its parent to avoid being destroyed.
At some point we’re going to have to make a very important decision: can we respect an intelligence that is not human? The way we answer that question is probably going to have a lot of repercussions in the long run. I hope we make the right decision.
I like words a lot but I also love words that introduce me to whole new ways of thinking. I remember first learning the word synecdoche (most usually pronounced si-NEK-de-kee), where you used the word for part of something to refer to that something as a whole (or the other way around). Calling a car ‘wheels’ or champagne ‘bubbles’ are good examples of this. It’s generally interesting which parts people pick for synecdoche, because it emphasises what is important about something. Cars have many parts but we refer to it in parts as wheelsI and motor. I could bore you to tears with the components of champagne but we talk about the bubbles. In these cases, placing emphasis upon one part does not diminish the physical necessity of the remaining components in the object but it does tell us what the defining aspect of each of them is often considered to be.
There are many ways to extract a defining characteristic and, rather than selecting an individual aspect for relatively simple structures (and it is terrifying that a car is simple in this discussion), we use descriptive statistics to allow us to summarise large volumes of data to produce measures such as mean, variance and other useful things. In this case, the characteristic we obtain is not actually part of the data that we’re looking at. This is no longer synecdoche, this is statistics, and while we can use these measures to arrive at an understanding (and potentially move to the amazing world of inferential statistics), we run the risk of talking about groups and their measurements as if the measurements had as much importance as the members of the group.
I have been looking a lot at learning analytics recently and George Siemens makes a very useful distinction between learning analytics, academic analytics and data mining. When we analyse the various data and measures that come out of learning, we want to use this to inform human decision making to improve the learning environment, the quality of teaching and the student experience. When we look at the performance of the academy, we worry about things like overall pass rates, recruitment from external bodies and where our students go on to in their careers. Again, however, this is to assist humans in making better decisions. Finally, and not pejoratively but distinctly, data mining delves deep into everything that we have collected, looking for useful correlations that may or may not translate into human decision making. By separating our analysis of the teaching environment from our analysis of the academic support environment, we can focus on the key aspects in the specific area rather than doing strange things that try to drag change across two disparate areas.
When we start analysis, we start to see a lot of numbers: acceptable failure rates, predicted pass rates, retention figures, ATARs, GPAs. The reason that I talk about data analytics as a guide to human decision making is that the human factor reminds us to focus on the students who are part of the figures. It’s no secret that I’m opposed to curve grading because it uses a clear statement of numbers (70% of students will pass) to hide the fact that a group of real students could fail because they didm’ perform at the same level as their peers in the same class. I know more than enough about the ways that a student’s performance can be negatively affected by upbringing and prior education to know that this is not just weak sauce, but a poisonous and vicious broth to be serving to students under the guide of education.
I can completely understand that some employers want to employ people who are able to assimilate information quickly and put it into practice. However, let’s be honest, an ability to excel at University is not necessarily an indication of that. They might coincide, certainly, but it’s no guarantee. When I applied for Officer Training in the Army, they presented me with a speed and accuracy test, as part of the battery of psychological tests, to see if I could do decision making accurately at speed while under no more stress than sitting in a little room being tested. Later on, I was tested during field training, over and over again, to see what would happen. The reason? The Army knows that the skills they need in certain areas need specific testing.
Do you want detailed knowledge? Well, the numbers conspire again to undermine you because a focus on numerical grade measures to arrive at a single characteristic value for a student’s performance (GPA) makes students focus on getting high marks rather than learning. The GPA is not the same as the wheels of the car – it has no relationship to the applicable ability of the student to arbitrary tasks nor, if I may wax poetic, does it give you a sense of the soul of the student.
We have some very exciting tools at our disposal and, with careful thought and the right attitude, there is no doubt that analytics will become a valuable way to develop learning environments, improve our academies and find new ways to do things well. But we have to remember that these aggregate measures are not people, that “10% of students” represented real, living human beings who need to be counted, and that we have a long way to go before have an analytical approach that has a fraction of the strength of synecdoche.
Digital Humanities: Reflections on distant reading and why the ability to fly hasn’t stopped us walking.Posted: August 17, 2014
One of the themes explored in the Digital Humanities is often “what exactly do we mean by Digital Humanities” because everyone asks and there are any number of self-described skeptics who seem to have an inability to add any new categories to their knowledge hierarchies. We’re studying the intersection of traditional computing and humanities so we’re asking the old question of “where does the desert end” which is only answered locally and specifically, rather than globally and generally. But a major fear for Humanists that came up during the week I was in Maryland was the threat of a colonising external force that would fundamentally alter what Humanists did until it was unrecognisable. I’m going to talk briefly about my view of digital humanities as a parallel augmentation, rather than a displacing colonisation.
Many areas of Humanities use the notion of close reading, where the text is carefully read and interpreted as part of a sustained effort. While this is exemplary for extracting themes and really getting into the work it doesn’t scale up well. We keep producing things to read and there is a limit as to how many things you can close read. This is where distant reading can come in, because it scans works thematically and syntactically, and provides an aggregate or abstraction to the reader. This is scalable and fast, because we can computerise it, but it risks inaccuracy, shallowness and is guaranteed to have the bias of the analysis software.
Let me step back and talk about travel for a moment. We started (well, by we, I mean bipedal humans) moving around on our feet. Then we did things with animals – in a vehicular sense – then the wheel, then lots of wheels, plus animals, plus betting – and that’s how we got the Colosseum. At some point, we stopped trying to put petrol into animals (who kept exploding) and tried it in cars intend. Suddenly we could zoom around, which widened our stride, but had the downside of enabling Italian Futurism at the start of the 20th century which led to all sorts of odd things and the declaration of war as the “great hygiene” until a lot of them died in the Great War – seriously, Marinetti, what were you thinking? (As a side note, the Futurist Cookbook is worth reading because it’s very Heston Blumenthal, just 90 years beforehand.)
Then we developed planes and the journey that took months on foot, weeks on animal and days by car, could take hours. But we never stopped walking, although we could now use our more advanced techniques to walk in new places and ultimately go further.
I feel exactly the same about close and distant reading. There are now (hooray) too many books on most subjects for any person to read in their life, let alone in a span to allow detailed analysis in a timely fashion. But this doesn’t mean we have to stop close reading. It means that we can look into topics and areas, refine our distant reading and visualisation, and then drill down once we’ve landed somewhere. Better still, distant reading allows us to link areas of close reading that may not be apparently connected – we can fly to a new place to explore that will develop the knowledge we already have.
Personally, I’d love it if the Humanities came and did a bit of colonisation in Computer Science, but I can completely understand why the reverse is culturally confronting. And I can also understand the many trad CS people who would also feel threatened by a counter-colonisation – although I probably don’t agree with their reasoning.
Going to a course like this is always good for my thinking as it requires me to switch gears and lens to get things done. I strongly recommend stepping out of the comfort zone of your own discipline when you can as it gives you extra knowledge and some valuable perspective.
This sessions was dedicated to the very important issues of gender and diversity. The opening talk in this session was “A Historical Examination of the Social Factors Affecting Female Participation in Computing”, presented by Elizabeth Patitsas (@patitsel). This paper was a literature review of the history of the social factors affecting the old professional association of the word “computer” with female arithmeticians to today’s very male computing culture. The review spanned 73 papers, 5 books, 2 PhD theses and a Computing Educators Oral History project. The mix of sources was pretty diverse. The two big caveats were that it only looked at North America (which means that the sources tend to focus on Research Intensive universities and white people) and that this is a big picture talk, looking at social forces rather than individual experiences. This means that, of course, individuals may have had different experiences.
The story begins in the 19th Century, when computer was a job and this was someone who did computations, for scientists, labs, or for government. Even after first wave feminism, female education wasn’t universally available and the women in education tended to be women of privilege. After the end of the 19th century, women started to enter traditional universities to attempt to study PhDs (although often receiving a Bachelors for this work) but had few job opportunities on graduation, except teaching or being a computer. Whatever work was undertaken was inherently short-term as women were expected to leave the work force on marriage, to focus on motherhood.
During the early 20th Century, quantitative work was seen to be feminine and qualitative work required the rigour of a man – things have changed in perceptions, haven’t they! The women’s work was grunt work: calculating, microscopy. Then there’s men’s work: designing and analysing. The Wars of the 20th Century changed this by removing men and women stepping into the roles of men. Notably, women were stereotyped as being better coders in this role because of their computer background. Coding was clerical, performed by a woman under the direction of a male supervisor. This became male typed over time. As programming became more developed over the 50s and 60s and the perception of it as a dark art started to form a culture of asociality. Random hiring processes started to hurt female participation, because if you are hiring anyone then (quitting the speaker) if you could hire a man, why hire a woman? (Sound of grinding teeth from across the auditorium as we’re all being reminded of stupid thinking, presented very well for our examination by Elizabeth.)
CS itself stared being taught elsewhere but became its own school-discipline in the 60s and 70s, with enrolment and graduation of women matching that of physics very closely. The development of the PC and its adoption in the 80s changed CS enrolments in the 80s and CS1 became a weeder course to keep the ‘under qualified’ from going on to further studies in Computer Science. This then led to fewer non-traditional CS students, especially women, as simple changes like requiring mathematics immediately restricted people without full access to high quality education at school level.
In the 90s, we all went mad and developed hacker culture based around the gamer culture, which we already know has had a strongly negative impact on female participation – let’s face it, you don’t want to be considered part of a club that you don’t like and goes to effort to say it doesn’t welcome you. This led to some serious organisation of women’s groups in CS: Anita Borg Institute, CRA-W and the Grace Hopper Celebration.
Enrolments kept cycling. We say an enrolment boom and bust (including greater percentage of women) that matched the dot-com bubble. At the peak, female enrolment got as high as 30% and female faculty also increased. More women in academia corresponded to more investigation of the representation of women in Computer Science. It took quite a long time to get serious discussions and evidence identifying how systematic the under-representation is.
Over these different decades, women had very different experiences. The first generation had a perception that they had to give up family, be tough cookies and had a pretty horrible experience. The second generation of STEM, in 80s/90s, had female classmates and wanted to be in science AND to have families. However, first generation advisers were often very harsh on their second generation mentees as their experiences were so dissimilar. The second generation in CS doesn’t match neatly that of science and biology due to the cycles and the negative nerd perception is far, far stronger for CS than other disciplines.
Now to the third generation, starting in the 00s, outperforming their male peers in many cases and entering a University with female role models. They also share household duties with their partners, even when both are working and family are involved, which is a pretty radical change in the right direction.
If you’re running a mentoring program for incoming women, their experience may be very. very different from those of the staff that you have to mentor them. Finally, learning from history is essential. We are seeing more students coming in than, for a number of reasons, we may be able to teach. How will we handle increasing enrolments without putting on restrictions that disproportionately hurt our under-represented groups? We have to accept that most of our restrictions actually don’t apply in a uniform sense and that this cannot be allowed to continue. It’s wrong to get your restrictions in enrolment at a greater expense on one group when there’s no good reason to attack one group over another.
One of the things mentioned is that if you ask people to do something because of they are from group X, and make this clear, then they are less likely to get involved. Important note: don’t ask women to do something because they’re women, even if you have the intention to address under-representation.
The second paper, “Cultural Appropriation of Computational Thinking Acquisition Research: Seeding Fields of Diversity”, presented by Martha Serra, who is from Brazil and good luck to them in the World Cup tonight! Brazil adapted scalable game design to local educational needs, with the development of a web-ased system “PoliFacets”, seeding the reflection of IT and Educational researchers.
Brazil is the B in BRICS, with nearly 200 million people and the 5th largest country in the World. Bigger than Australia! (But we try harder.) Brazil is very regionally diverse: rain forest, wetlands, drought, poverty, Megacities, industry, agriculture and, unsurprisingly, it’s very hard to deal with such diversity. 80% of youth population failed to complete basic education. Only 26% of the adult population reach full functional literacy. (My jaw just dropped.)
Scalable Game Design (SGD) is a program from the University of Colorado in Boulder, to motivate all students in Computer Science through game design. The approach uses AgentSheets and AgentsCubes as visual programming environments. (The image shown was of a very visual programming language that seemed reminiscent of Scratch, not surprising as it is accepted that Scratch picked up some characteristics from AgentSheets.)
The SGD program started as an after-school program in 2010 with a public middle school, using a Geography teacher as the program leader. In the following year, with the same school, a 12-week program ran with a Biology teacher in charge. Some of the students who had done it before had, unfortunately, forgotten things by the next year. The next year, a workshop for teachers was introduced and the PoliFacets site. The next year introduced more schools, with the first school now considered autonomous, and the teacher workshops were continued. Overall, a very positive development of sustainable change.
Learners need stimulation but teachers need training if we’re going to introduce technology – very similar to what we learned in our experience with digital technologies.
The PolFacets systems is a live documentation web-based system used to assist with the process. Live demo not available as the Brazilian corner of internet seems to be full of football. It’s always interesting to look at a system that was developed in a different era – it makes you aware how much refactoring goes into the IDEs of modern systems to stop them looking like refugees from a previous decade. (Perhaps the less said about the “Mexican Frogger” game the better…)
The final talk (for both this session and the day) was “Apps for Social Justice: Motivating Computer Science Learning with Design and Real-World Problem Solving”, presented by Sarah Van Wart. Starting with motivation, tech has diversity issues, with differential access and exposure to CS across race and gender lines. Tech industry has similar problems with recruiting and retaining more diverse candidates but there are also some really large structural issues that shadow the whole issue.
Structurally, white families have 18-20 times the wealth of Latino and African-American people, while jail population is skewed the opposite way. The schools start with the composition of the community and are supposed to solve these distribution issues, but instead they continue to reflect the composition that they inherited. US schools are highly tracked and White and Asian students tend to track into Advanced Placement, where Black and Latino students track into different (and possibly remedial) programs.
Some people are categorically under-represented and this means that certain perspectives are being categorically excluded – this is to our detriment.
The first aspect of the theoretical prestige is Conceptions of Equity. Looking at Jaime Escalante, and his work with students to do better at the AP calculus exam. His idea of equity was access, access to a high-value test that could facilitate college access and thus more highly paid careers. The next aspect of this was Funds of Knowledge, Gonzalez et al, where focusing on a white context reduces aspects of other communities and diminishes one community’s privilege. The third part, Relational Equity (Jo Boaler), reduced streaming and tracking, focusing on group work, where each student was responsible for each student’s success. Finally,Rico Gutstein takes a socio-political approach with Social Justice Pedagogy to provide authentic learning frameworks and using statistics to show up the problems.
The next parts of the theoretical perspective was Computer Science Education, and Learning Sciences (socio-cultrual perspective on learning, who you are and what it means to be ‘smart’)
In terms of learning science, Nasir and Hand, 2006, discussed Practice-linked Identities, with access to the domain (students know what CS people do), integral roles (there are many ways to contribute to a CS project) and self-expression and feeling competent (students can bring themselves to their CS practice).
The authors produced a short course for a small group of students to develop a small application. The outcome was BAYP (Bay Area Youth Programme), an App Inventor application that queried a remote database to answer user queries on local after-school program services.
How do we understand this in terms of an equity intervention? Let’s go back to Nasir and Hand.
- Access to the domain: Design and data used together is part of what CS people do, bridging students’ concepts and providing an intuitive way of connecting design to the world. When we have data, we can get categories, then schemas and so on. (This matters to CS people, if you’re not one. 🙂 )
- Integral Roles: Students got to see the importance of design, sketching things out, planning, coding, and seeing a segue from non-technical approaches to technical ones. However, one other very important aspect is that the oft-derided “liberal arts” skills may actually be useful or may be a good basis to put coding upon, as long as you understand what programming is and how you can get access to it.
- Making a unique contribution: The students felt that what they were doing was valuable and let them see what they could do.
Take-aways? CS can appeal to so many peopleif we think about how to do it. There are many pathways to help people. We have to think about what we can be doing to help people. Designing for their own community is going to be empowering for people.
Sarah finished on some great questions. How will they handle scaling it up? Apprenticeship is really hard to scale up but we can think about it. Does this make students want to take CS? Will this lead to AP? Can it be inter-leaved with a project course? Could this be integrated into a humanities or social science context? Lots to think about but it’s obvious that there’s been a lot of good work that has gone into this.
What a great session! Really thought-provoking and, while it was a reminder for many of us how far we have left to go, there were probably people present who had heard things like this for the first time.
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 email@example.com 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!
Mark Guzdial has put out some excellent posts recently on Barbara Ericson’s ongoing work on analysing AP CS exam attempts and results across the US. Unsurprisingly, to those of us who see the classrooms on a day-to-day basis, women are grossly underrepresented. In this interview, Barbara is quoted:
Barbara Ericson, director of computing outreach at Georgia Tech, has made a startling claim. She said not one female student in three states – Mississippi, Montana and Wyoming — took the Advanced Placement exam in computer science last year.
Ericson appeared on Weekend Express to discuss the gender gap and explains why more women aren’t interested in computer science.
Now, I’m not going to rehash all of these posts but I did want to pick on one blogger who took the AP data and then, as far as I’m concerned, not only got it wrong by making some fundamental interpretational errors but managed to do so in a way that so heavily reeked of privilege that I’m going to call it out.
I hesitate to link to the article on the Huffington Post but it’s only fair that you should read it to see what you think, even though it will generate traffic. The article is called “Memo to Chicken Little: Female Scientists Do Roam Among Us, and Gasp! Some Even Wear Lipstick”. So before we’ve even started, we’ve got one good stereotype going in the title.
Look, I’m not planning to drag apart the whole article but I will pick on one point that the author makes because it really irritates me. Here’s the paragraph:
As a woman who likes science as a bystander but chose not to pursue it professionally, I’ve got a couple of problems with all this handwringing. Mostly, well-intentioned as it is, it implies that women need “help” choosing a field of study. High school girls are exposed to exactly the same science and math courses they need to graduate as boys are, but in the eyes of the handwringers, girls are either too shallow or simple to choose for themselves, or need to be socially engineered into the correct balance of male vs. female, regardless of their choices. I appreciate your concern, but frankly, it’s pretty demeaning.
Frankly, I’ve never seen a more disingenuous interpretation of attempts to undo and reverse the systematic anti-female bias that is built into our culture. I’ve never seen anyone who is trying to address this problem directly or indirectly label girls as shallow or too simple to choose – this is a very unpleasant strawman, constructed to make those of us who are trying to address a bias look like we’re the ones with the attitude problem. We don’t need to socially engineer girls into the correct balance, we need to engineer society to restore the balance and articles like this, which make it appear that women are deliberately choosing to avoid STEM, are unwelcome, unnecessary and unfair to the many young women who are being told that the way that our society works is the way that it should work.
Need I remind people of stereotype threat? The PNAS study that shows that women are as automatically likely to harshly judge women and lessen their rewards as their male colleagues? Looking at the AP attendance and performance doesn’t show equality, it shows the outcome of a systematically biased system.
To say that “High school girls are exposed to exactly the same science and math courses they need to graduate as boys are” is a difficult statement. Yes, women rack up roughly the same number of course credits but on the critical measurement of whether they choose to go on and pursue a profession? No, something breaks here. The AP test is a great measure because it is an Advanced Placement exam and your intention is to use this to go further. Is there clear evidence of far fewer women, as a percentage, going on from high school to college in STEM despite scoring the same kinds of grades? Yes. Is there evidence that some of these problems (anxiety about maths, for example) can start with perceptions of teachers in primary school? Yes. Is there a problem?
And the question is always, if your previous exposure has not been fair, then is it reasonable to pick an arbitrary level of course that would be fair to people who haven’t been discriminated against? For years, racism was justified by culturally-based testing that could not be performed at the same level by people outside the culture – which was then used to restrict their access to the culture.
To me, that statement about exposure summarises everything that is wrong with glib arguments about constructing equal opportunity. If we’re going for a big job and there’s a corporate ‘interview dinner’ for 20 people, then we’ll all be on our best behaviour at dinner. For someone to lose the job because nobody showed them how to use a finger bowl or because their family uses a knife in the ‘other’ way, is to provide an equal exposure in the present that is blatantly unfair because it doesn’t take into account the redress of previous bias to bring people up to the point where it is really equal opportunity.
I think history supports me in the statement that we have been proved wrong every other time we’ve tried to segregate human ability and talent based on fixed physical abilities that were assigned at birth. Isn’t it about time we started investing all of our effort into producing truly equal opportunity for everyone?
CS Students get a pretty bad rap on that whole “stereotype” thing. Given that I’m an evidence-based researcher, let’s do some tests to find out if we can, in fact, spot the CS student. Here’s a quick game for you. Hidden in this image are 3 Computer Science students.
Which ones are they? (You can click on the image to enlarge it.)
I’ll make it easy for you to reference them – we’ll number the rows from the top (A) to the bottom (H) and the images from left to right as 1 to … well, whatever, because the rows aren’t the same length. So the picture with the cactus is A2, ok? Got it? Go!
Who did you pick? Got the details? Now scroll down.
Of course, if you know me at all, you probably know the answer to this already.
They’re ALL Computer Science students – well, they’re found in an image search for “I am a Computer Science student” and, while this is not guaranteed, it means that most of these students are in CS. Now, knowing that, go back and look at the ones you thought were music majors, physicists, business students, economics people. Yes, one or two of them probably look more likely than most but – wait for it – they don’t all look the same. Yeah, you know that, and I know that, but we just have to keep plugging away to make sure that everyone ELSE gets that. Heck, the pictures above are showing less pairs of glasses per person than you would expect from the average and there’s not even one light sabre! WON’T SOMEONE THINK OF THE STEREOTYPES???
This is only page 2 of the Image Search and I picked it because I liked the idea of some inanimate objects being labelled as CS students as well. Oh, that’s right, I said that you’d win something. You know never to trust me with statements unless I’m explicit in my use of terminology now. Sounds like a win to me!
(Of course, the guy with red hair is giving the strong impression that he now knows that you were looking at him on the Internet. I don’t know if you wanted that but that’s just how it is.)