In my last post I wrote about the problems facing scientific discovery and how our system of research funding and support is stifling opportunities for young innovators. I’d like to expand on that by focusing on the larger system that this research is couched in, particularly the way in which education is tied to innovation.
Let’s start first with the term innovation. My description, as opposed to definition, looks like this:
Innovation sits at the intersection of discovery and application; it means doing something different to create value. If we take this as our defintion, it means that an innovator is someone who challenges orthodoxy or established ways of doing things and delivers value to others in the process of doing so.
Now let’s look at the term education. Looking at the various definitions, I actually like the one from Wikipedia which is:
Education or teaching in the broadest sense is any act or experience that has a formative effect on the mind, character or physical ability of an individual. In its technical sense education is the process by which society deliberately transmits its accumulated knowledge, skills and values from one generation to another.
Taken together, innovation and education look to fit well together. Both of these terms refer in some capacity to change and impact. It is not enough to be different for the sake of difference, it is change that produces value (innovation) and change that transforms the fundamental state of what was there before (education). This change could come from something genuinely new (discovery) or taking something we’ve learned before and apply it in a novel context. What is often forgotten is that novel context can be the mind of a young person learning something for the first time. It is easy to forget that math, history, art, geography, biology and all of these things are new to everyone at some point in their life and the degree of novelty is inversely proportional to experience. The more experience you have, the less things seem novel.
Experience is the accumulated influence of patterns of activity and information. We pull in data, transmute that into information, which combines to create knowledge and, with time and accumulation, leads to wisdom. Or so the thinking goes. Russell Ackoff, who I’ve mentioned here before in a couple of posts, adds understanding to this mix (via Bellinger, Castro & Mills). David Weinberger, writing in HBR, and author of the Cluetrain Manifesto and Everything is Miscellaneous, challenges this and questions whether this neat DIKW relationship taxonomy is really is as neat and clean as it seems. Weinberger’s challenge is not to Ackoff’s DIKW hierarchy per se, but rather the way in which the parts are put together to make the whole. Knowledge, for example, is the most problematic of the terms in this model:
The real problem isn’t the DIKW’s hijacking of the word “knowledge” but its implication that knowledge derives from filtering information. It doesn’t. We can learn some facts by combing through databases. We can see some true correlations by running sophisticated algorithms over massive amounts of information. All that’s good.
But knowledge is not a result merely of filtering or algorithms. It results from a far more complex process that is social, goal-driven, contextual, and culturally-bound. We get to knowledge — especially “actionable” knowledge — by having desires and curiosity, through plotting and play, by being wrong more often than right, by talking with others and forming social bonds, by applying methods and then backing away from them, by calculation and serendipity, by rationality and intuition, by institutional processes and social roles. Most important in this regard, where the decisions are tough and knowledge is hard to come by, knowledge is not determined by information, for it is the knowing process that first decides which information is relevant, and how it is to be used.
The real problem with the DIKW pyramid is that it’s a pyramid. The image that knowledge (much less wisdom) results from applying finer-grained filters at each level, paints the wrong picture. That view is natural to the Information Age which has been all about filtering noise, reducing the flow to what is clean, clear and manageable. Knowledge is more creative, messier, harder won, and far more discontinuous.
Knowledge generation is therefore social, challenging, process-oriented, prototyped and revised, and non-linear.
Now think of our universities and training institutions and know knowledge is generated and transmitted (using the term mentioned above) and what that looks like:
– Students are graded individually, and absolutely. No value is placed on social interaction here, because then it is hard to assess what the individual learner “learned” if they did so working with others where the discrete contribution of each person can’t be parsed out. Think of how ridiculous the idea of letter grades are, which are only slightly more idiotic than number grades for any course that involves contextual subject matter (which is social sciences, humanities, business, most of medicine, some of engineering, lots of biology, quite a lot of architecture, design, ….)? A student gets a 79 and their grade is a B+, while a student with a grade one per cent higher gets an A-; a qualitatively different realm of feedback.
– “Outcomes” trump process. By outcomes, these mean the #students who attended class, #lectures given, review of the syllabus and that’s about it. We evaluate courses using only the most banal indicators (did the course match the syllabus? did the professor show up? did the professor speak coherently?). The result is outcomes: what are the grades? Was there a normal distribution? (I am explicitly asked what percentage of my student grades are A’s when I submit my grading form, revealing both a horrid understanding of the university’s understanding of both learning and statistics. But this is not something unique, indeed it is pretty much standard across universities).
– Courses are frequently lecture-based (one teacher, many students), which is also at odds with the social nature of learning (see Brown & Duguid’s book the Social Life of Information for more on the absurdity of this in practice). If we learn more from teaching than “being taught”, why are we not training students to be teacher-learners and giving them more opportunities to try things out and teach others what they learn?
– Prototyping is discouraged. It saddens me that every year I get students who sit in my class with the sole purpose of getting an A. Doing anything risky, by its very nature, threatens the possibility of an A. Grades are meant to assess learning, but what they are doing is measuring performance to a standard derived without context to the learner. Thus, a student can get an A without learning much, and could get a D and learn more than they had in any course. Yet, it is the A that judges whether a student is deemed a success or not, whether they get scholarships and so on. A syllabus is designed to resist prototyping with its predictable week-to-week learning plan so there is little reason our students should come prepared for anything that deviates from this plan. So much for non-linear thinking.
Taken together, does this look like an environment that fosters innovation, or even education for that matter?
How then, do we create environments of learning, discovery and innovation when our system is designed precisely to discourage this?
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