T2VC in Buffalo, NY next week! Bright Buffalo Niagara forum on Oct. 1 at Statler City aims to make progress on innovation and entrepreneurism.
Henry Doss: The Innovation Curriculum–STEM, STEAM or SEA?
By Henry Doss September 26, 2013
How do we prepare young people to be scientists, engineers, mathematicians and technologists, who can also be creative?
Educating young people is the key to future innovation, so it’s probably a good idea to ask ourselves if we are on the right track. The question about what to teach, how to teach and why to teach is perennial, and thousands of years old. But the ongoing debate we are having about STEM (science, technology, engineering and math) and STEAM (science, technology, engineering and math with a dab of the arts thrown in to pacify the Liberal Arts folks) seems a particularly strange one.
We seem intent on developing an education strategy that answers the wrong strategic question. Because technology and scientific advancement are so dominant in our modern lives, we have defaulted to the obvious question: “How do we prepare young people to be scientists, engineers, mathematicians, and technologists?” The answer we give to that question is a STEM curriculum. But this answer generates a collective howl of dissent from educators about “the arts” and creativity and critical thinking. So, we expand the question to: “How do we prepare young people to be scientists, engineers, mathematicians and technologists, who can also be creative?” The answer we offer to that question is a STEAM curriculum. The scientifically inclined are happy and the Liberal Arts inclined are pacified.
But will either curriculum really foster innovative thinking, risk-tolerant leadership, bold thinking and an ability to trust and be trusted? Will either approach develop possibility and potential and promise in young people? Will either curriculum create a Renaissance of innovation in our society? Likely not, because the answers – STEM and STEAM – are incomplete answers to an incomplete question. Or, said a little differently, both STEM and STEAM initiatives emanate not from thinking about education, but from attempting brute force social engineering.
In the Renaissance (you know, the “real one,” a few hundred years back) there was a surge of disruptive technologies, cultural upheaval, profound religious and social conflict, and a dizzying rise in the dominance of science-based thinking. The result was arguably the most innovative period in all of human history. The core curriculum for study during this period was some mix of the guadrivium (arithmetic, geometry, music and astronomy) and trivium (grammar, logic and rhetoric), a very heavy dose of theory-based study of language, math and what we rather offhandedly refer to today as “critical thinking.”
One way to think about the innovative features of the Renaissance – and the curricular focus that was dominant then – is to think about three obvious features of the ecosystem: Science, Economics and Arts . . . or SEA. It was the diversified interplay of an expanding and vibrant capital system, an enthusiastic social exploration of the “new sciences” and a widespread passion for the arts that drove “innovation.” Rather than an overt, outcome-oriented curriculum aimed at producing “workers,” the Renaissance curriculum developed – for lack of a better term – sensibility. It was based on the notion of developing the intellect for substantial expression and it helped to fuel “big thinking” – the food of innovation.
Ghiberti’s Gates of Paradise, commissioned in 1425 for the North Doors of the Florence Baptistery, is a great example of innovation and the interplay between diverse cultural elements. The creation of this masterpiece required capital (lots of it, which came from a vibrant cloth trade, courtesy of the Cloth Importers Guild); science and technology applied to metallurgy and various construction arts; and, of course, a profound artistic sensibility. Just to name a few of the more overt benefits, over the 25 years Ghiberti labored on these doors, the project: created many jobs in his studio; drove innovation in the science and technology of bronze casting (which would find use across multiple other, more practical, applications); and left an artistic legacy that has endured for almost 600 years.
None of this happened because of policy wonks analyzing labor statistics and concluding that there was a need for an emphasis on metallurgy. None of this happened because the pedagogically inclined sought to teach “bronze door skills.” Nor did any of this happen because of an ideologically driven capital system. It happened because of idealism and aspiration, which serendipitously led to a world of practical applications.
The difference between then and now may be simple. When we craft educational strategies to address purely economic outcomes – jobs, manufacturing, growth and so on – we draw constraints around innovation. It’s not that the areas of study or the curriculum itself are wrong, so much as we are studying for the wrong reason. If our curriculum is aimed at preparing young people to do a job, how likely is it to prepare them to create jobs?
If what we want in the world is innovation, we would do well to relax a bit on the data-driven thinking that dominants pedagogy, and let the world be a little more risk-oriented, random, and joyful. If what we want is a world that is creative, future-oriented and progressive, maybe we should be a little less heavy handed with the outcome-driven thinking that drives policy. And if what we really want is a generation of happy people, maybe we should rediscover the notion that idealism is the foundation of pragmatism, and let idealism be the platform on which education is built. And perhaps we should think about SEA – Science, Economics and Arts — as a model for our 21st century innovation curriculum.
Victor talked about innovation and production in Porto Alegre.
T2’s weekly newsletter just came out! Find out how blue yarn can help your company to be more productive! http://us6.campaign-archive2.com/?u=d2e007daf0f740d16385ca370&id=87b1068a02
Notes on the practice of innovation and technology commercialization
“Mankind are so much the same, in all times and places, that history informs us of nothing new or strange in this particular. Its chief use is only to discover the constant and universal principles of human nature.”
David Hume (1711–76). An Enquiry Concerning Human Understanding.
A handful of years ago a colleague of mine from the World Bank and I were having coffee in Washington DC with visitor from South Africa. She was listing the skills and tools that she believed South Africa needed to improve technology transfer and commercialization, especially from universities. At that time I had never worked anywhere in Africa, but I was stunned by the realization that some 90% of the items in our visitor’s list of wants and needs were identical to those of the countries in Eastern and Central Europe where I had more experience. Since then this commonality of needs has been verified by working in many other countries from Colombia to Kazakhstan.
In this blog I argue that there is unnecessary and frequent reinvention in creating technology commercialization systems, especially in developing countries, resulting in unnecessarily high transaction costs and less than optimum efficiency. Reusable knowledge tools, analogous to more general reusable knowledge or learning objects, can reduce reinvention of known processes, lower transaction costs, and increase technology commercialization efficiency. This is important because more and more developing countries are attempting to build ecosystems around technological innovation. To be clear, when I use the terms ‘learning object’ I mean a digital resource that can be reused to facilitate learning. In the application discussed here it’s helpful to think of a learning object for what it does (an agent) rather than what it is (its properties).
Wait a moment. Am I going against what I was preaching in an earlier blog about context and cutting and pasting solutions without paying attention to context? To be honest, maybe – a little. In Solving the Right Problem: part 1, March 24, 2013. http://innovationrainforest.com/2013/03/24/solving-the-right-problem-part-1/ I stated “Solving the right problem is all about context. A problem comes embedded in its own context; apparently similar problems in different contexts may have very different solutions. Likewise, solutions have their own contexts.” In the first part of that blog I looked look at one way of identifying a problem which may also bring out its context and suggest possible solutions. In Part 2 of the blog, Fallibility and the Making of Good Decisions, problem solving and decision making in Rainforest ecosystems was discussed. http://innovationrainforest.com/2013/04/30/fallibility-and-the-making-of-good-decisions-solving-the-right-problem-part-2/ Let’s see what, if anything, has changed.
I always have fun playing with my granddaughter’s Lego™ blocks, and the Lego™ block analogy is used frequently whenever knowledge is being collected and assembled from disparate resources. Mary Adams and Michael Oleksak in their 2010 book Intangible Capital: Putting knowledge to work in the 21st century organization speak about using Lego™ blocks to build models of a knowledge factory such as Google search or a medical device company (see the video “You can grow like Google” http://www.youtube.com/watch?v=brBwWqiSg8g)
Wouldn’t it be great if we could build technology commercialization programs, and more broadly supportive ecosystems, by plugging Lego™ blocks of learning into each other? There is an appealing simplicity. In the second part of this blog we will use some examples to see how far we might go, discuss the limitations of the Lego™ block analogy, and suggest that a reluctance to apply reusable knowledge tools to problems arises from a misunderstanding of the role of context.
We will also introduce the concept of ‘contextual qualifiers’ which are those pieces of knowledge that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment. Conditional qualifiers are statements, which refer to knowledge Lego™ blocks (documents, videos, etc.) which ‘qualify’ the knowledge presented as being dependent on certain conditions.
As I was preparing this blog a Harvard Business Review article was published: Consulting on the Cusp of Innovation by Clayton Christensen, Dina Wang, and Derek van Bever (HBR October 2013, pp. 106 – 114) which discusses how incumbent consulting firms are being eroded by technology and other forces. The authors note that “only a limited number of consulting jobs can be productized but that will change as consultants develop new intellectual property. New IP leads to new toolkits and frameworks, which in turn lead to further automation and technology products.” In this new business model, consultants may not always re-invent solutions; a move away from work where value depends primarily on “consultants’ judgment rather than repeatable processes.” The authors call this “value-adding process business” in which “processes are usually repeatable and controllable.”
I’m guilty of raising several issues and leaving them hanging. Next time these will be pulled together and some conclusions drawn about the feasibility of reusable knowledge tools in technology commercialization.
Victor Hwang to give keynote address at University Economic Development Association (UEDA) Annual Summit, Oct 27-30, in Pittsburgh.Posted: September 18, 2013
By Henry Doss, Chief Strategy Officer of T2 Venture Creation, from Forbes
Our doubts are traitors,
And make us lose the good we oft might win
By fearing to attempt.
– Shakespeare — Measure for Measure
The world of innovation leadership is a tricky one. At our best levels of leadership, we can create an organizational ecosystem that is robust, energetic, nimble and fun. At our worst, we can create an ugly mess of uncertainty, miscommunication, misalignment and misdirection.
The difference between those two arbitrary extremes is measurement.
The idea of measurement may seem obvious as a point of view, but in the innovation world there is a complication waiting to trip the unwary. That complication is the need to focus on measuring features of the innovation ecosystem, rather than outputs of the innovation ecosystem. And the science of measuring features – things such as normative trust, win-win value systems, diversity of point of view, and so on — is not as fully developed as our ability to measure output. So, we are naturally drawn to look at outputs, rather than features, because outputs can more easily be known.
But this approach simply avoids the hard work of measuring system attributes.
Edward Deming, who may yet emerge as one of the pioneers of innovation, captured this challenge succinctly: “We should work on our process, not the outcome of our process,” he said. Extending this to the evolving world of innovation ecosystems, the lesson is clear: Leaders should focus on the normative features of the innovation ecosystem, not the output of the ecosystem.
A perfect example of this challenge can be found in most organizational incentive systems. As a general rule, incentive systems are funded by a fixed pool of dollars, awarded to individual performers or teams ranked on relative performance. Top performers (or top teams) get rewarded; bottom performers (or teams) do not. On the surface this seems rational and logical. But, as Deming also noted: “The biggest enemy to the system is common sense.”
If the organization is measuring and valuing only the output of an incentive system – production – and not measuring and valuing the normative featuresof the incentive system, then the management view is not so much wrong as it is woefully incomplete. Of course organizational leadership is rightfully concerned with production; but focusing solely on measuring production, at the expense of other organizational variables, is a sure-fire way to foster and perpetuate systems that are resistant to innovation . . . and this would most certainly include incentive structures!
In the innovation ecosystem, incentives need to be understood – and measured – relative to how they support innovative mindsets and culture. In evaluating incentives, an obvious set of questions would be: 1) To what extent does the incentive system create trust? 2) To what extent does the incentive system create collaboration? 3) To what extent does the incentive system create a “win-win” culture.
What might emerge from asking these kinds of questions is a world of “the unknown.” And this place – the “unknown” – is precisely where genuine innovation leadership will come into play. It is only natural to be attracted to measures –outputs – that are already in use, that seem to be proven, and which are easy to get to. But the challenge is to lead organizations tonew measures, which will in turn allow fornew leadership – of the ecosystem.
The difficulty here is obvious. Assuming that a leadership team understands the inherent value in, for example, “trust,” there is still the difficulty of both knowing what trust is and being able to talk about it in an empirical manner. Absent clear measures, leaders may find themselves in the awkward – and untenable – position of declaring a value set, but having no meaningful way of measuring or reporting on that value set. Strong leaders can and will advocate value systems, and they will do so based solely on their confidence and good judgment. But, over time, if judgment cannot be supported by evidence, institutional confidence will slip.
There is an old saying, often attributed to Deming: “Never let measurement substitute for judgment.” Of course not. But the obverse of that may be even more instructive: “Never let the absence of measurement be an excuse to rely solely on judgment.” Innovation leaders will commit to ecosystem change based on judgment; they will initiate work on ecosystem norms based on judgment; and they will persevere when measurement proves to be difficult. But sooner or later, leadership judgment must be buttressed by strong, valid empirical measures, or what starts out as a strong commitment to innovation principles will become only vague platitudes.
One of Deming’s 14 Points was: “Get rid of unclear slogans.” The organizational world is chock full of empty slogans, and they tend to pop up when leaders are unclear about precisely what it is they want to achieve. It is the very absence of measures and metrics that causes organizations to fall back on the vaguely inspirational. Inspiring leadership will serve just fine as a beginning point, but over the long haul, leadership must have empirical rallying points to maintain relevance and validity.
So, inspire, yes! Rely on your leadership judgment, yes! Be a vocal advocate for innovation values, yes! But recognize that part of fulfilling your innovation promise is to get down to the basic work of measuring what you are trying to create, in a way that everyone in your organization can understand and align themselves to. Rendering the subjective objective is the final test of innovation leadership.