The following is an article that I started to write several years ago and just now rediscovered while working on something else. I guess I intended to write more about ‘big picture’ innovation in education but only got as far as some guiding questions that that section would address. Nevertheless, I think it’s an interesting article as is and may have some relevance in the rapid change environment that schools find themselves today as they adopt technologies such as mobiles/tablets, etc. I’m going to leave it as is with the questions at the end rather than writing the final section. Comments, especially ones that address the questions at the end, are very welcome.
I think most would agree that in times of increasingly rapid change, innovation is important for education. That then raises the question; how do we foster innovation in educational systems? There are a lot of general ideas about how innovation works and what needs to be done to promote it. Many of these ideas have been applied to education, primarily approaches that encourage small-scale innovation in the classroom, i.e. making the teacher the primary innovator. These approaches have given mixed results. They’ve produced a lot of interesting ideas but have seldom led to lasting change. Here, I want to consider what broader long-term views of innovation processes, what I’ll call “big picture” theories of innovation, can contribute to discourse on innovation in education.
‘Big Picture’ Theories of Innovation
My thinking about this was prompted by a recent article on NPR about touch technology that brought Bill Buxton’s theory of the “long nose of innovation” to my attention. Buxton’s point with his theory is that most innovations take a long time to gel into something concrete that can lead to new products, methods, etc.. He demonstrates this with the following graph (source: BusinessWeek, Jan, 2008):
Buxton refers to the long gradual slope in the graph as the “long nose” (as opposed to Chris Anderson’s “long tail”).
This is not a profound discovery. In fact, a series of studies conducted around the late 50s to the early 70s demonstrated the same point. The studies include the US Dept. of Defense’s HINDSIGHT study, the TRACES (Technology in Retrospect and Critical Events in Science) study, and the Battelle Research Institute’s study on the “Interactions of Science and Technology in the Innovative Process”, all of which have been well documented by Ben R. Martin and his colleagues (see for ex. The political economy of science, technology, and innovation, 2000; Research foresight: Priority-setting in science, 1989; Foresight in science: Picking the winners, 1984). So, it could be said that Buxton’s long nose theory demonstrates its veracity by having a rather long nose of its own.
The HINDSIGHT, TRACES, and Battelle studies were meant to address an intense debate that preceeded them. The debate was on the question of what kind of research produces the most valuable returns; “pure research”, i.e. unhindered curiousity-driven academic pursuit for the simple sake of gaining new knowledge, or “applied research”, i.e. research that aims to address specific market demands. Martin & Irvine (1984) associate the former with a “science-push” model, where science nurtures market demand by creating possibilities for innovation, and the latter with a “market-pull” model, where market demands dictate to science what sort of research is necessary. The series of studies essentially pitted “pure research” advocates against “applied research” advocates. The first in the series, the HINDSIGHT study, looked at several innovations and traced their roots within a given timespan and concluded that the “market-pull” model gave more significant returns. The “pure research” advocates were somewhat less than pleased with these results, which led to the TRACES and Battelle studies. These two studies traced the roots of several innovations over a longer timespan with the aim of discovering the true roots of the innovations in question. Both studies were able to map direct connections from the innovations being studied to products of pure research. Rather than take sides in the debate, Martin and Irvine reflected on the whole series of studies and came to the wise conclusion that both the “science-push” and “market-pull” models, and especially the interplay between the two, are important for innovation.
Buxton’s recommendation, on the basis of his theory, is that firms try to find ways to “shorten the long nose”. He suggests that to achieve this, “[firms] might focus on developing a more balanced approach to innovation—one where at least as much investment and prestige is accorded to those who focus on the process of refinement and augmentation [of the results of research] as to those who came up with the initial creation.” Thus, Buxton basically repeats Martin and Irvine (perhaps we should say that Buxton’s nose is not only long but also somewhat hooked?). But, there is a difference. Martin and Irvine never said anything about, or hinted at, the prospect of shortening the time that it takes for a particular innovation to produce marketable or useful goods or processes. They were more concerned with engaging the stakeholders representing either side of the innovation equation to find ways to foster the “pure” researcher’s tinkering as well as the “applied” researcher’s practical experimentation. This, to me, seems a more sensible recommendation than Buxton’s, which suggests a sort of desperate rush to come up with something new merely for the sake of producing newness with little regard for its actual usefulness. It brings to mind some of the more asinine “made-for-TV” products, like coffee for dogs and slippers with headlights, or some of Microsoft’s innovation faux pas, such as the annoying paperclip helper and the doomed desktop redesign known as “Microsoft Bob” (oh wait, that’s Buxton’s company isn’t it?).
These ‘big picture’ theories of innovation demonstrate three things that are essential for innovation to lead to novel and useful new products and processes. First is the role of “stakeholders”. The involvement of stakeholders implies that at some point in the innovation process there has to be some intrinsic connection to the field being innovated for; i.e. someone has to be able to contextualize the outcome of someone’s tinkering to suggest some sort of applicability. Secondly, creative stakeholders have to have opportunities and encouragement in their environments to tinker. Thirdly, more practically oriented stakeholders have to have access to the outcomes of those tinkerings, along with opportunities and incentives to turn them into something useful. On this basis we could say that it is likely that part of what contributes to the rapid increase in innovation today, in a general sense, is that information technology has made the outcomes of tinkerer’s endeavors much more accessible than they were several decades ago. As a result, people looking to solve practical problems have a vast amount of resources at their disposal that they can use to create novel products and processes. Likewise, from the tinkerers’ perspective, they have far more efficient outlets for their creative endeavors than before. Thus, we have an intensifying *feedback loop* between tinkerers and practical problem-solvers and it is this feedback loop that produces innovative products and processes.
The important message here is not that the “long nose of innovation” has to be shortened, but rather that the innovation feedback loop between tinkerers and problem-solvers needs to be intensified and made accessible. That might lead to a shortening of the time that it takes for an innovation to produce a product or process but merely focusing on the timespan involved overlooks the real significant dynamics at play in the innovation process.
‘Big Picture’ Innovation in Education
Applied to education, ‘big picture’ theories of innovation raise the question; is the innovation feedback loop supported in educational environments, and if so, how? As I mentioned previously, in many educational systems teachers are encouraged to innovate in their classrooms, but is this enough to generate the type of feedback loop that produces major innovations?
