Time to re-load? Computational Thinking and Computer Science in Schools

Snapshot—April 5th, 2012 In Chicago today the Obama re-election campaign is set to be the most technically sophisticated ever seen with voters being wooed via Twitter and Facebook, and digital technology along with those who understand how to build and use it set to play a key role in influencing people’s decision making. Across the Atlantic in the UK we face an abundance of choices about how to exploit and use technology, and this poses an enormous challenge for both the current and future education of our children. The realisation that we need people who can produce as well as consume technology has brought a new energy and excitement about computer science and computational thinking, which is being heralded by some as the new literacy of the 21st century. The technology revolution has changed the way many of us work and interact, it has generated new industries and new
businesses, and it is natural that we now look to schools, teachers and the education system to help us to understand how we might best prepare our children to live, work and make best use of what computer technology offers.

But how best can we do this?

A mess? 2012 has seen the Secretary of State for Education state that “ICT in schools is a mess” and he has called for a new approach with the hope that technology can be used creatively to develop curricular content: the ‘wiki’ curriculum. What is happening with ICT and computer science education in schools has also been the subject of a 2011 Naace report entitled “The Importance of Technology”, an Ofsted report on ICT in schools, and the importance of providing young people with the skills required by the new workplace is captured by Nesta’s Next Gen report. Clearly there is growing concern and government commitment to change, so what change should we make and why?

Is Computer Science the answer? Computer science is an important element of the debate. The Royal Society’s 2012 ‘Shut down or restart?’ report suggested that a sound understanding of computer science concepts enables people to get the best from the systems they use, and to solve problems when things go wrong. However, computer science is evolving rapidly and its interdisciplinarity means that its evolution touches on many domains and every day life. There are significant challenges for those interested in how best to include it in the curriculum.

Are we sure we know what we want to change? There is already some excellent teaching of ICT and computer science in some schools within the current curriculum and programme of study, so not everything is wrong. Care needs to be taken that the changes we make do lead to a better learning experience at school: an experience that inspires and educates. But, are we clear about what is wrong with computer science and ICT in schools now? Can we be precise about the rationale for what learners at different stages need to be taught? What do we want learners to be able to achieve as a result of studying computer science? Where do ICT and computer science fit in the structure of the school curriculum: media, design, science, cross-curricular?

How can learners tap into the power of computational thinking? The skills of computational thinking can be taught with or without computers, by exploring how processes work, looking for problems in everyday systems, examining patterns in data, and questioning evidence. With a computer, learners can put their computational thinking into action. Could a focus on computational thinking better equip learners to use their understanding effectively and to learn how to apply a range of computing tools? Writing the code that makes a computer behave in a particular way is a creative pursuit: reflecting on what you have constructed is a key part of learning. We may therefore valuably ask: How can we develop good computational thinking for children?

Are we looking in all the right places? Are there less obvious areas of research that might help us answer some of these questions? For example, many people encounter the experience of Flow and are all too familiar with the experience of losing themselves in a task. Might the idea of Flow itself help us understand the learning process in computational thinking and computer science? Researchers in the psychology of programming have spent decades exploring how people learn to code, surely their expertise needs to be drawn into the debate?

There are no short cuts to answering these questions. The process of addressing them requires an interdisciplinary and participatory approach that involves groups from across the sectors that is inclusive in nature and powerful in design. This will require an approach that is new to society, schools, teachers and learners: a process that must be both flexible in its thinking and realistic in its understanding of the role of schools.

Tomorrow we will be having a debate about some of these issues at the London Knowledge Lab and I’ll report back on how that goes.

Read our briefing paper (from which the above text is taken)

Follow the event through illuminate – go to http://link.lkl.ac.uk/e-meeting for access to the live stream.

Thinking like a computer or computing like a thinker?

In my last post I finished with a comment about pop-11, a langauge designed to help novices learn how to program and to grapple with Artificial Intelligence. I also included an image of the Eliza programme an early example of natural language processing designed in the sixties by Joseph Weizenbaum at MIT. The computer user typed in questions and the ELIZA programme appeared to make human like responses. It worked on a script and when the user started to ask questions that were outside the script, ELIZA struggled. Probably the most famous example of the ELIZA programme was that of a psychotherapist, but my version of the ELIZA programme was one of the first pieces of code I ever produced in pop-11. I found pop-11 a good way into programming, it enabled me to get something up and running quickly, it helped me to start thinking in a particular way and it introduced me to the practical design and application of algorithms. My ELIZA program took on the personna of a politician, and it was surprisingly realistic and succeeding in writing a program that worked and produced a dialogue gave me a huge amount of pleasure. You know the sort of thing:

John: You don’t seem to be having a very good week

Teresa: Why do you think I am not having a very good week?

John: Well it looks as if your attempts to deport Abu Qatada have been stalled

Teresa: My attempts to deport Abu Qatada have not been stalled

John: But you acted too early and you have left him a days window to appeal

Teresa: I have not acted too early and I have not left him a days window to appeal

John: Do you not think that the three month deadline ended after you arrested him?

Teresa: I do not think that the three month deadline ended after I arrested him?

John: Are you sure that you have counted the days correctly?

Teresa: I am sure that I have counted the days correctly?

John: Is it not possible that you and your team have made a mistake?

Teresa: No, it is not possible that me and my team have made a mistake?

John: So have the European Court confirmed that you and your team have the correct deadline

Teresa: I have confirmed that me and my team have the correct deadline

John: But have the European Court confirmed this

Teresa: I don’t know why you are asking me if the European Court have confirmed this

John: Because the European Court will now decide if there is time for Abu Qatada  to appeal

Teresa: I don’t know why you are asking me if the European Court have confirmed this. I have confirmed that me and my team have the correct deadline

John: But we need to know if the deadline you used was correct

Teresa: I don’t know why you are asking me if the European Court have confirmed this. I have confirmed that me and my team have the correct deadline

John: We seem to be going around in circles here

Teresa: I have not gone around in a circle I travel in a Jaguar: a circle is a simple shape of Euclidean geometry consisting of those points in a plane that are equidistant from a given point, the centre

John: !

To code or not to code, if that is the question, what is the answer?

I was struck by the item on the R4 Today program this morning at 8.45 ‘ish when Rory Cellan-Jones told us about his day course at Decoded, through which he, as a complete novice, built an app. You can see his app and his story on the Today website, and as Rory points out “coding is cool – the government is listening to those calling for it to be taught in schools, and executives are signing up for courses.” This has prompted me to reflect on my own programming experiences and to make this the subject of a few posts over the next few days and running up to our next “What the Research Says” event on computing in schools.

I am a member of the BBC micro generation who first came across computing through using this delightfully frustrating device. However, I was not a member of the young audience at whom this machine was aimed, but the wife of a teacher who became intrigued by what her husband was up to in his office. Having secretly mastered the manoeuvre of disk swapping that got you started with the BBC micro my appetite was whet and I enrolled for a course at the local technical college. When I went to sign-up I said I wanted to learn about computers and I was asked what I meant by that. I had no clue why they were asking me this question, because the answer seemed obvious to me – I wanted to learn how the computer worked of course! However, I was offered a range of courses that would take me into the realms of managing a spreadsheet or learning to word process as well as learning how to write a program in basic – no brainer of a choice for me then. I duly arrived at my first evening class ready to build something, no idea what, but something. I loved it, even though my outputs were modest:  a greeting on the screen (you know the one), a date reminder, but I was hooked. I wanted more and much to the bafflement of my husband and to my children aged 5 and 3 I announced that I was going ‘back to school’ and was going to apply to read Computer Science and Artificial Intelligence at University. I was a distinctly mature student and was a little afraid that I would be the ‘silly old woman at the back of the class’. My fears were unfounded – most of us felt silly when it came to programming, because for most of us it was very, very hard!

After my BBC basic baptism, I entered the heady world of pop-11 a langauge designed to help novices learn how to program and to grapple with AI.

 

To be continued….

Are we too disciplined to make ourselves understood?

One of the wonderful things about being an undergraduate in the School of Cognitive and Computing Sciences (COGS) at the University of Sussex was the interdisciplinary experience that was embedded within our studies. When I studied Computer Science and Artificial Intelligence I did so within a community of philosophers, psychologists, linguists and computer scientists and it was great. This has given me a particular perspective on all the work I have subsequently been involved with – all of which has sat on the ‘fault lines’ between disciplines. As I am currently pondering how best we might improve the way that we academics communicate what is valuable about the research that we have and are doing about technologies for learning I realise that the communication problem is more deeply rooted than the ‘external’ communication challenges of making our research relevant and impactful, we often don’t communicate effectively within academia across disciplines. So, if we can do better at engaging with each other outside of our disciplinary comfort zones, then maybe we can also set our minds to finding a way to help those outside academia understand the complex landscape of researchers whose work might really help to enable a step change in the quality of the technology and its use for learning.

If we take a quick look at the sorts of research that might be relevant to people working in industry to develop technologies to support learning, from smart phone apps to learning platforms and interactive whiteboards, and to practitioners and learners both within and without educational institutions, there are many different research communities who might feel they have something interesting to offer. For example, there are computer scientists who develop the algorithms that drive the software that makes a technology capable of particular behaviours and the engineers who build the hardware that enables features like touch sensitive interfaces, drag and drop icons and multimedia output. There are the interdisciplinary researchers in Human Computer interaction who understand how to design interfaces to support the optimal types of interaction whether on an interactive table top, a virtual reality or an ipad. There are research communities that use artificial intelligence methods and techniques to design computer models that can enable software to adapt to the particular learner or learners who are using it so that a game or a simulation can interact in a manner that is tailored to its users needs, whether teachers or learners. Then there are the psychologists who understand how people learn and the sociologists who understand about communities and social interactions, and the social scientists who understand about educational systems and human relationships. Here I am only scratching the surface and there will be as many research communities and disciplines I have omitted as those that I have named. These communities largely publish their work in separate journals and at separate conferences. There are attempts to develop interdisciplinary communities in order to try to encourage cross fertilisation of ideas and appreciation of the contribution that different expertise can make to shared problems, although the well documented demise of COGS illustrates the power of the bureaucratic penchant for administrative neatness. These interdisciplinary communities are not the norm and at times of austerity there is a tendency for funders to concentrate on their core disciplines, which has a knock on effect upon the extent to which researchers are able to work across the disciplinary boundaries.