Originally published on November 18, 2015
I came across this article this week, entitled “AI has a better shot at Tokyo University than your kid” (Engadget, 2015).
It comes from Engadget, an online technology journal – so the focus is very much skewed towards what amazing potential is bubbling to the surface in new tech, rather than any objective review of educational practice already underway. That said, it reminded me of our current focus in education on standards, testing, and a need for today’s graduates to enter the ‘real world’ armed with skills in innovative thinking that we simply are not providing in many schools at the moment.
The crux of the story is a piece of software, built by The National Institute of Informatics in Tokyo, that scored above average on the entrance exam for University of Tokyo. Notable was the strong performance of the software in the history section of the test, requiring “natural learning processing skills to make inferences.” (ibid.)
This raises the question, what use are these exams and tests if a computer program can pass with a strong mark (511 out of 950, national average is 416)? Are they assessing the wrong skills? Or the wrong knowledge? Or are machines simply on course to overtake humans in skills provision, changing the structure of employment in the future?
Let’s just put our tongues in our cheeks and assume for a moment that the last possibility is the likely one (I don’t doubt that software advancements will change our lives, I’m just not on the technological armageddon bandwagon just yet), are we simply going to sit back and let a minority of programmers make the majority of the human race redundant? In this unlikely scenario, the education system will be responsible for adapting students to flourish in a world where sitting exams will no longer be necessary (because computers can do that anyway); for thinking of innovative ways in which the human race can occupy its new-found time; for thinking of innovative ways in which to improve and progress this new technology.
In short, if the machines are on the rise, education needs to adapt to provide a different skill set, and assess the possession of such in a different way. If, however, we are not yet at the point where the robots take over (OK, I tried to leave my personal opinion out of this, but I just have to be honest now…), that leaves us with option 1 and option 2: we are assessing the wrong knowledge/skills; we are assessing in the wrong way.
Tony Wagner is,
Appalled at the idea, now widely held, that the best measure of teachers’ effectiveness is students’ performance on standardised, multiple-choice tests. (Wagner, 2012)
Ken Robinson sets out his stall in several books, most recently Creative Schools, by unequivocally stating that the standards culture is bad (Robinson, 2015) for learners, teachers, and the education system moving forwards.
So, how to be innovative in an age where computers are passing tests to enter prestigious universities? Innovation is not something that only applies to technology, or science. Nor is it necessarily something that transforms or disrupts. Tony Wagner claims that innovation can fall into two types, across one or more of five strands.
Disruptive Innovation & Incremental Innovation
One is not more valid than the other. Whilst disruptive innovation grabs headlines and public interest – the iPod, iPhone, the railway, television – it is the incremental innovation that quietly mounts up to a critical mass that tips over into disruptive – the MP3 format, the touchscreen, the internal combustion engine, the vacuum tube. Indeed, it can be argued that a disruptive innovation is merely one incremental innovation that connects many other incremental innovations. The examples I give all come from the realm of technology but,
Innovation occurs in every aspect of human endeavour. (Wagner, 2015)
The strands of human endeavour I identify are:
Political (could be considered a subsection of a larger socio-political strand)
In education, we are of course interested in educational innovation from a teaching point of view, but we must be conscious of the need to teach, facilitate, and inspire all students to innovate in the other strands also. Such innovation relies on “experimentalism” – a term used by Tim Brown (Brown, 2008, 3) – which is essentially a process of trial and error; a can-do approach to a seemingly unsolvable problem. Indeed, Brown lists five skills required by creative thinkers:
Are these the skills we should be teaching? Are they even teachable? Should we be instead using the five strands as vehicles for nurturing these skills? Where does the traditional curriculum fit in – or more importantly: should it fit in?
Back in a software lab in Tokyo, however, is a computer program that, according to the Wall Street Journal, today holds an 80% chance of being admitted to 474 universities in Japan. Whilst I don’t yet have the answers to the questions above, one thing is clear: we are currently looking for the wrong skills in our students.