I spent my dinner hour talking open education, domains-of-one’s-own, and WordPress in an interview hosted by Kyle Maurer of WP Round Table.
I am sitting in my hotel room trying to pack my bags and simultaneously unpack the conference. It’s an interregnum between being immersed in the society of the conference and the travel I must begin in a few hours to return to home. I’ve returned from many, many conferences. I know how to do this. But this time I think it will be harder. Home, my home campus, and my friends and colleagues will likely be much the same as when I left. But I’m not. I’m different.
When I get home, folks will assume the OpenEd16 conference I attended was about free textbooks and OER’s which they think they know about. It was and they do. They’ll think it was about technology – especially all that crazy tech they think they don’t understand (yet) but that I’m always going on about. It was and I’ll continue to go on about it.
But that’s not at all what this conference was really about. It was about insight (thank you Gardner Campbell!). It was about students. But it wasn’t about Students(tm), those commoditized abstract entities for whom we are supposed to provide Success(tm) so that they might assume their rightful place as cogs and consumers with Good Jobs(tm) in the neo-liberal globalized economy. It was about real students. Real people. Human students. Students in Sisyphean struggles to be human, support families, and to learn – often while being hungry and burdened with more debt. (thank you Sara Goldrick-Raab).
This conference wasn’t about technology or licenses or books. It was about us. Humans. There is much I still need to “process” so that I might integrate all I experienced. I say experienced because learn doesn’t seem adequate. A short, incomplete list of this conference for me would include
- discovering that I have a voice myself and that there are people who actually listen to me! At a very personal level I’m not willing yet to expose publicly, this is profound for me.
- connecting at a very basic human level with Kate Bowles of Australia and feeling at a visceral level how we are one people on one planet and borders don’t matter.
- realizing my struggles on campus are not isolated. Many others have the same struggles. It’s not me. It’s being pioneer.
- how we – us humans – really progress and grow. We model for each other. Consciousness matters and we progress is possible if we reflect. I thank Martin Weller for modelling and reflecting on how those of us who are privileged, like myself, need to behave.
- The opportunity to meet, exchange ideas, have fun, and just be humans with so many people that I’ve met only once before maybe or have only known a year or so. People who now seem to be such good friends that I can hardly remember not knowing them or having their ideas in my world: Robin DeRosa, Scott Robison, Alan Levine, Gardner Campbell, Tom Woodward, Adam Croom, and, of course, Laura Gogia and others.
- just the overwhelming number of people who I knew only via the Web but now have had the privilege and luxury of knowing in person such as Ken Bauer, Tim Owens, Lauren Brumfeld, Martin Weller, Kate Bowles, Audrey Watters, Lee Skallerup-Bessette, Jon Becker, and again many others.
- the vast numbers of Canadians including Irwin Defries and the whole crew from British Columbia who show what can happen when folks at different institutions really collaborate (politely, of course!)
- the number of new people I met and now share bonds with.
This conference was really about inclusion, insight, humanity, empathy, learning, and love. It was not as much a celebration of the commons as a loud voice proclaiming our commonality as humans and our connected diverse strengths. As Tom Woodward put it (or he quoted, I don’t know), it was about who owns how you move through the world’ power structures & understanding your position within & how to navigate through them
It is sad that the values or goals that the mainstream leadership of higher education claims to be pushing us to achieve exist largely only as abstract concepts – the picture of learning that Gardner referenced. While this wonderful assemblage of people, most of whom are considered too fringe to be taken seriously at their home campuses, are actually creating the real things: inclusion, insight, creativity, humanity. Those who were not part of this misunderstand. It was not technology and free books. It was precisely what they claim they want.
I have long thought that the measure of great rhetoric is that the listener cannot ever be the same person again. I cannot be the same again. I am changed. Thank you my friends and colleagues for doing this. In particular, I want to thank Gardner Campbell and Sara Goldrick-Raab for their book-ending keynotes and their rhetoric.
But alas, I must now face the trek to home and back to work where they will likely once again look at me like I am from Mars and politely humour me. It will be hard.
OpenEd16 isn’t your normal higher ed conference. This year it had all the normal features of a higher ed conference: keynotes, the stimulating concurrent presentations, food, and evening socializing by academics that felt just a little more freedom by being out of town. But it also had a something new. A jam session.
Yes, that’s right. In addition to organizing the usual conference, David Wiley (@opencontent) rented a drum kit and who knows what other instruments and somehow convinced the Hilton Hotel to allow us to take over the lobby bar from 8 to 10 last night. Anybody from the conference was free to step up to the microphones, grab and instrument, and make music with their peers. Peers they had never practiced with. Peers they were playing with for the first time. Peers who were all at different stages of experience in playing. Peers who had varying levels of talent and skill (I’m assuming that, since being at the zero level on that scale I can’t really judge). Does this sound like an open pedagogy class to anyone yet?
When I heard of the plan, it sounded crazy. But it wasn’t. It was brilliant. It was fun. It was energizing. Some people got up and danced. Many watched and listened intently. Many others were actively engaged in conversations around the room with the music of their peers as background. I think everybody there had fun. And I know I at least had a moment of insight, that exquisite moment when the blood surges in the brain near the right temple that Gardner Campbell told us about in the opening keynote yesterday.
Somebody called it the OpenEd band (although membership was rather fluid). I have to agree. The band actually demonstrated why open education (open pedagagy) works. I’m now music expert, but even I know that objectively they weren’t “great”. They certainly weren’t as polished or slick as the original bands that sold platinum albums of those recordings. But that peer-reviewed, objective standard of “great” didn’t matter. Nobody wanted to sit around and hear the albums of The Monkees, The Rolling Stones, Dylan, Bonnie Raitt, Lynyrd Skynrd, and all the other bands whose songs got played. What mattered was who was playing and that they were playing – creating– live. Live music beats polished recorded music. Everywhere.
Why? Why would we rather listen to flawed music, complete with mistakes, than all gather to listen together to the perfect, polished recording? Because it’s live. And live means alive.
I think the same is true with students and learning. Alive matters. Alive gets us the real learning, not the “picture of the learning”. But for learning to be alive, somebody has to be actively creating something. We have to be part of a live experience. To me, the core of open learning is being in that space where things and ideas are created. The best space for that is for both instructors and students to create, share, and publish their own work. Simply reading or viewing the flawless, peer-reviewed, polished, perfected work of some publisher is like listening to an album in public. It becomes background noise. If directed, we can attend a small part of it, maybe. But mostly, we it has no affect on us. On the other hand, reading, viewing, and listening to each others’ creations in the same time and space as they’re being created engages us. It even inspires us to create ourselves. The flaws don’t matter. The creating does.
Open works because it’s live. And live means we’re alive.
David Wiley, you modeled the proper role of a professor tonight perfectly. You set up the space. You provided the assignment. You mixed the sounds to pull in everybody. Folks engaged the risky experiment because they trusted you. And then you let the students open it up and create. Open. Live. Alive.
This is my presentation for Open Ed 2016 in Richmond, VA. It’s kind of a progress report on the LCC Open Learning Lab project. It’s very much a work-in-progress (the Lab project, not the presentation). Assuming the universe cooperates, I’ll follow-up on this posting of the slides with a few long-form posts explaining what I said and going into some more detail.
If perchance your browser or Internet connection takes too long to load the above presentation, you can download the file here.
Economists disagree. It’s so common that there are jokes about. For example,
If all of the economists in the world were laid end-to-end they would scarcely reach a conclusion.
Economics is the only field in which two people can get a Nobel Prize for saying the opposite thing.
Why? I can’t explain all of economists’ disagreements here (I don’t have enough pixels!), but I can explain some of the disagreement over questions of raising the minimum wage. There are numerous calls for Congress to raise the minimum wage, yet Congress has remained bitterly divided on the issue. Their disagree to a large extend reflects the disagreement among economists. To non-economists, the disagreement seems to either indicate that there really isn’t any science in economics and it’s all opinion, or that some economists must be lying or deliberating obfuscating. In truth, though, there’s another reason for the apparent disagreement: the difference between micro and macro level economic analyses.
First, let’s establish some historical perspective on the debate. I want to clarify the difference between “normative” and “positive”. Positive arguments are statements or conclusions about what the predicted effects of a proposal will be without taking a stand on whether those effects are desirable or tolerable. (note that the word “positive” here denotes “factual or likely”, not “good”) Normative arguments are when someone argues whether a proposal should be done. Normative arguments typically are based upon a combination of predicted outcomes and a value judgement as to whether those outcomes are desirable or tolerable compared to the alternative. For a long time in economics, economists were actually largely in agreement on the positive science, or the predicted effects, of a rise in the minimum wage. It was generally agreed that raising the minimum wage would give larger incomes to those who continued to work at minimum wage (i.e. low skilled) jobs but that the rise would decrease the numbers of those jobs and thus raise unemployment rates among those seeking low-skilled jobs. Historically the disagreement over minimum wage hikes was over the normative aspects: was the rise in unemployment and loss of jobs worth the increased incomes to others.
The agreement over the predicted positive effects wasn’t always unanimous. There have always been some dissenters. But in the early 1990’s Card and Krueger studied a “natural experiment” by comparing fast food restaurants on two sides of a state line when one state raised the minimum wage and the other didn’t. Their results started a fierce debate that still rages over the predicted effects of rises in minimum wages. On one side there are now many economists who side with Card & Krueger in saying that raising the minimum wage, even if raised very significantly such as to $15 per hour from less than $8, will not decrease employment and will have a very large increase incomes. On the other side, maintaining the older stance are those such as Don Boudreaux who doggedly argue that any rise in minimum wage must increase unemployment significantly. Like most topics in economics the practicality of measuring and analyzing the empirical data is somewhat equivocal. Although there have been numerous studies since Card and Krueger that have buttressed their results, the empirical data along always leaves enough room for some argument. So what does the theory say?
Don Boudreaux and others of the “increases in minimum wage MUST increase unemployment” camp, would have use believe that theory is unequivocal. The essence of their argument is that low skilled labor is a commodity sold in a market. It has a demand (firms want to buy it) and a supply (low skill workers want to sell it and get paid). The wage that gets paid is the price of this labor commodity. The most basic supply-and-demand analysis tells us that if the government forces the price up somewhat artificially by setting a price floor (i.e. a minimum wage) below which transactions cannot occur, then there will a smaller quantity of hours of labor demanded. In other words, firms will hire and pay fewer workers. There’s often an appeal to the concept that if the price (cost) of an input or resource goes up, then the firm’s profits will go down and the firm will be less inclined to produce that good or service and therefore will buy less (hire fewer workers).
How can good theory-toting economists dispute this? Isn’t it supply-and-demand, the most basic micro economic concept as taught in the first few chapters of any principles of econ text? It’s easy actually. The key is that this supply-and-demand theory as argued against a rise in minimum wage has three major flaws. Two flaws are the result of the theory as applied being too simple (there’s more chapters in the micro text!) and the other flaw reflects the difference between micro and macro in economics.
The first flaw in the simple supply-and-demand model application to minimum wage type jobs is that there’s really very little evidence that labor markets behave like commodity markets or that they conform to the assumptions necessary to use a supply-and-demand model. Most jobs, including minimum wage jobs, are more like long-lasting relationships. They aren’t commodity, transaction based like a market for selling widgets or apples or even theatre tickets. There are dramatic transaction costs involved. Put another way, it’s expensive to hire people (and to fire them and then replace them). Minimum wage jobs aren’t homogenous (they aren’t all the same) the way the theory requires. Further, the wage paid affects the productivity of the worker, which in turn affects the value of that worker’s output to the firm. When the wage is boosted, workers work harder, stay longer on the job, quit less often, and gradually acquire more productivity and skills. Firms often find that when forced to pay the higher wage, the firm’s total costs, including hiring costs, etc, stays level or even declines. This is the essence of Arindajit Dube’s studies.
The second flaw is in focusing on the cost of the worker’s wages as if it were the sole consideration in the firm’s decision of how much to produce. The standard theory of the firm and production, which is covered in-depth just a few chapters later in the same economics textbooks after the supply-and-demand model makes it clear: a firm will produce whatever quantity makes it the most profit. The primary constraints on the output are the demand for the end product, the pricing of the end product, and the core technology used. In other words, if the firm can still sell the output to consumers, it will produce it and the technology (means of production) will require it to hire the necessary labor. A rise in the price of a particular input does not necessarily mean a drop in the quantity produced.
The other two flaws in the arguments against minimum wage increases require shifting to a macro perspective. Micro economics is often described as studying individuals and individual products/markets. That’s only partially true. Actually micro is a methodology. It’s more properly called “comparative statics using partial equilibrium analysis”. Micro theories and models explicitly focus on only one particular shifting variable (the wage in this case) and it assumes that all other variables or influences are held constant or unchanged. (Economists call this the ceteris paribus assumption). In contrast, macro theories are often described as focusing on large aggregate phenomena such gross national product or the inflation rate or the national unemployment rate. But again, there’s actually a methodological difference. Macro theories require a general systems approach accounting for multiple effects and ripples of many variables that are interrelated. Let’s look at minimum wage increases as an example of these differences in methodology between macro and micro.
In micro, there’s really only the price (i.e. the wage itself), the quantity of jobs offered, and the quantity of workers available, all of it in the low skill arena. That’s it. So the micro analysis sees that when the minimum wage is boosted, the firm pays more per worker and each employed worker gets more. End of story. The only micro question is how it all affects the quantities of jobs.
Macro, however, recognizes that nothing happens in isolation in the economy. There’s a circular flow. Workers are also simultaneously customers. So when the minimum wage goes up, yes, the workers get paid more and firm pays out more money. But what do those workers do with the additional money income? They buy things. Who do they buy them from? Firms that sell and produce products. So the firms not only pay out more money to workers, the firms also get to collect more money by selling more to the increased consumer demand. But, you say, Acme’s newly enriched minimum wage workers don’t buy that much stuff from Acme. Doesn’t matter. The workers spend it somewhere. And that firm uses the additional money and additional demand to buy more inputs and pay more profits. And those firms and workers then experience income increases and so on and so on as the money circulates throughout the economy. Eventually even Acme sees an increase in sales and revenue collected which in turn helps pay for the wage boosts. Macro looks at the whole system.
In recent years, many cities and some states have taken it on themselves to raise the minimum wage, often to a so-called “living wage”. The empirical results have pretty clearly supported the macro analysis. Rises in minimum wages tend to not depress employment and actually tend to stimulate the local economy. This is the macro analysis.
Sometimes economists just disagree and sometimes they let their ideological and political biases color their professional arguments. Some of that happens in the debates on minimum wage increases. However, much of apparent disagreement arises from the choice of whether to view the issue through a micro lens or a macro lens.
To read more about the economic analysis of minimum wage increases see these earlier posts:
- Links on Minimum Wage
- Inflation Erodes Real Value of Minimum Wage
Today I’m speaking to the Birmingham NEXT community organization
today. I’ll be reprising a slightly updated version of my talk on “Mythbusting the Fears of Social Security Insolvency”. It’s a talk I’ve given to many classes and community groups but I love it and I think it’s critical for people to hear. So I’ll repeat the main point: Social Security is NOT going “bankrupt”. It will be able to pay benefits and young people are NOT being “screwed” by the program.
Today’s slides are here. To download the file, click on the little gear icon. The presentation is Creative Commons licensed BY-SA-NC.
Some other links that viewers might find interesting where I’ve discussed Social Security and how the “trust fund” really works are:
Note: This is the second of
two three posts that summarize the presentation Sue-Anne Sweeney of Madonna University and I made at the Higher Learning Commission 2016 annual conference in Chicago in April 2016. The first post in this series is The Leadership Traps That Stop Transformative Change in Higher Ed. The slides for the whole presentation are available at the original post Iterating Toward Disruption: The Paradox of Becoming Agile (HLC 2016). The thoughts are our own based upon our research and our years over eight decades of change management experience as both leaders and consultants in both higher ed and many other organizations. They do not necessarily reflect those of our respective institutions.
In this Part II, I we explain two of the paradoxes by which we can escape the six leadership traps we identified in Part I. These six leadership traps, while well-intentioned, actually prove self-defeating in our quest to achieve transformative change in higher education. We labelled those traps as
- Unquestioned Brilliance
- Vision Delusion
- Technical Solutionism
- “They just….”
- Telling, Not Listening
Solving the Puzzle With Paradox
These traps are largely of our own doing as leaders. We choose how to react. The traps are made of our own values and perceptions. The traps intertwine with each other. We choose to emphasize urgency and discount studied learning which in turn leads us to emphasize Grand Vision to the neglect of detailed context. We choose to make quick analyses and choices which lead to discounting contradictory information via confirmation bias, which in turn leads us to conclude “they just…don’t get it” or, worse, “they just…don’t want to change”.
But the reality is that people do embrace change. In fact, our very institutions of higher education are monuments to just how much people not only embrace life-altering change, they seek it out. Life-altering change is what we’re about. Everyday, students enroll and attend classes, often at great hardship or difficulty for themselves, simply because they want their lives to change. Our research faculty dedicate their lives and work extensive hours in search of information that will change lives and change how we all think. Our teaching faculty dedicate their lives to helping others learn so they can change lives.
Yet as institutions we often seem stuck. Trapped. Change as an institution is difficult and frustrating, often due to the leadership traps we’ve identified. The intertwining of the traps combine with the nature of higher education (if the confidence of unquestioned brilliance is a trap, then higher education is certainly a target-rich environment!) to make change seem intractable. It’s a giant puzzle – a kind of n-dimensional Rubik’s cube. The key to solving the institutional change puzzle lies in Paradox.
So let’s revisit what a paradox is. We turn to Merriam-Webster:
something (such as a situation) that is made up of two opposite things and that seems impossible but is actually true or possible
How can a paradox help us? The seeming contradiction embedded in a paradox opens our thinking. The paradox acts as a siren to our reasoning mind.We want to “solve” the paradox, so we begin to question assumptions and terms and we open ourselves to new perspectives. It’s fun especially for intellectuals like us in higher education. Therein lie the solutions to our puzzles – and our puzzle of change leadership. Edward Teller, the famous theoretical physicist observed that
two paradoxes are better than one; they may even suggest a solution.
Well, if two paradoxes are better than one, we go even better. We are going to offer five paradoxes that can help unlock the transformative change puzzle and help us escape the traps. Each paradox takes the form of a simple directive or rule (I actually prefer guideline) that at first glance seems to be internally contradictory.
Paradox #1: To Move Faster, Start Slower
We feel the urgency. We know we need large-scale change and we need it as soon as possible. We want to move fast – the urgency trap. The key to moving fast though is to start slow. Traditionally, especially in the U.S., we stress and reward accomplishment, and tend to place less importance on the planning needed to effectively prepare for the implementation. I recall many years ago back in the early 1980’s when I first began to learn and absorb Continuous Quality Improvement concepts and the teachings of W. Edwards Deming and William Ouchi. At that time Japanese industry, particularly electronics, precision machinery, and automotive, far exceeded their American counterparts in quality, both real and perceived. I remember an illustration of how the typical American firm at the time approached change. It looked like this. Americans were accomplishment-focused and in a hurry. They were proud of their “bias for action”. Traditionally, especially in the U.S., we stress and reward accomplishment, and tend to place less importance on the planning needed to effectively prepare for the implementation.
The contrast was this diagram which illustrated the approach of high-quality, continuous improvement focused organizations. Many Japanese firms led the world in quality in the early (and in most any other metric of success) in the early 1980’s because they spent more time up front planning and studying. It was once they really knew and understood the whole system, as a system, that they could take decisive action. Organizations with successful change management reverse the traditional model and spend more time and energy in planning in order to be able to implement rapidly and efficiently.
It is indeed ironic that in higher education leadership there seems a real fear of “analysis paralysis” and a real desire to quickly move through the plan phase to the doing. We seem to measure leaders by their appearance of doing rather than their understanding. It is ironic because higher education is all about study and learning. Whether it’s research or teaching, that’s why we exist. Yet when it comes to our own affairs we skimp on the learning part. It’s the Unquestioned Brilliance trap in action.
So we need to start slow. But how does slow become faster? There are three reasons why starting slow can actually lead to a faster overall implementation of change.
- First we need to thoroughly understand the phenomenon and how it affects everyone involved with it. Once we know and really understand, we can identify the right actions to take. Study, planning, and learning is vastly cheaper and less disruptive than implementing actions. It’s better to be less efficient at planning & studying and be more efficient at doing. Planning helps set better priorities. Precision helps when defining the destination.
- Second, a more in-depth planning and study phase enables us to not only identify the change we want to have happen, but also to identify the best method of making that change happen. There may be many routes to our destination. Planning enables us to consider the most efficient and efficacious.
- Finally, starting slow allows us to practice and refine our skills, new processes, and new tools. What we do in practice, even when slow, determines how we react when we speed up. Again I turn to a lesson learned from my adventures in auto racing. When I first started racing I attended a race-drivers’ school. I was so excited. Real race cars. A real race track (sports car road course). I was geeked to prove how fast I could go. But the order of the first day was to lap the track in these 150 mph cars at 40-50 mph. We were scrutinized in incredible detail for the lines and approaches we made to each turn on each lap – when it was so slow. But that practice at slow speed built the habits and reactions that enabled us to go really fast a few days later. What we do slowly, we will attempt to do fast. So we need to practice the right way first.
Paradox #2: To Achieve Big Change, Rapidly Iterate Small Changes
Higher education is in love with the Grand Initiative. That is perhaps why higher education leaders have been so taken by Christiansen’s “disruptive innovation” hype, despite the Christiansen’s own revisions and clarifications or the many criticisms of it. It sounds so good – so attractive to how we think in higher ed. A single “innovation”, a single initiative that conquers all. What greater legacy could a college president or university provost ask for? It seems so plausible too. After all we see what appear to be dramatic change in many industries led by some significant change in some organization, right? So, despite the evidence that the theory really doesn’t work that way and the evidence that it really isn’t applicable to higher education, we embrace the rhetoric. It sounds so exciting. But rhetoric has consequences and may get misled by our own rhetoric. (Confession: I, Jim, have used the term “disruption” in titles of presentations at conferences a few times – mostly as a blatant attempt to use the latest buzzword to attract attendees so I could evangelize what I knew better. Sue is innocent of this charge).
We want BIG change, but we can get there by biting off small little pieces, making small changes, and then rapidly repeating. Change one aspect of a system quickly, then repeat on other parts. Change one unit and then repeat on the other units. Learn from experience and practice.
The big bang concept very rarely works. What does work is iteration. he easiest way to achieve dramatic change, so-called “innovative disruption” or transformative change, is to focus on smaller changes and to rapidly iterate or cycle through them. Change, by definition is doing things differently. We need practice. As we practice, the changed behavior becomes second-nature. It enters procedural memory. Iteration is how some of most successful organizations have accomplished dramatic change. The core of Deming-inspired Continuous Quality Improvement processes is a cycling of small, incremental improvements made an a relentless schedule with the result that a small organization such as Honda or Toyota eventually conquer their world industry.
In a more modern context, the software package WordPress has largely conquered the World Wide Web in less than a decade. Over 25% of all websites now are powered by WordPress. Yet WordPress doesn’t produce new dramatic versions. Instead, the entire open source WordPress community – it’s not even a single company, but rather a community of ‘000’s of volunteers – creates powerful software by adhering to a philosophy of releasing updated versions every 4 months. Sometimes the changes are big and sometimes small. But the key is to relentlessly keep making modest changes. Eventually the changes accumulate like a snowball rolling down hill conquering. Web browsers work the same way. Remember the old days when there were major updates with dramatic changes at long intervals? Nowadays, Firefox and Chrome get updated regularly at frequent intervals with small changes each time. Gradually the experience changes dramatically, but without the drama of the big bang.
How does iteration work its magic? One way is because small changes also allow people to manage the amount of distress and extra energy involved in doing things differently. As the changes are iterated, they develop some “change stamina” and skills, such as collaboration across silos and routinely engaging in debrief and lessons learned to improve the process for the next go-round. Practice makes perfect.
A second way iteration works magic is because it lowers risk. Regardless of whether you measure risk as “the potential damage done by an error” or “the probability of making an error”, risk slows change efforts. A higher perceived risk means people proceed cautiously and slowly. When we have the grand initiative approach, risk is higher. We can’t risk any mistakes. So we use “proven solution” even though it doesn’t really produce the transformative change we want or need, but it’s safe and predictable. The big bang or Grand Initiative approach is like having the organization take the train together. All parts of the organization are going to go through the same journey at the same time and are expected to arrive at the same central station even if their real destination is a few blocks or a mile away from that station. To move the entire organization safely down the same path at the same time requires us to build a railroad to get to our destination. That takes time and investment. We could use somebody else’s railroad, but then we won’t get to our destination, we’ll arrive at theirs. If we rush the track building process, the result can ugly. The whole organization derails and fails.
Transformative change necessarily involves innovation & creativity. And creativity & innovation require the ability to make mistakes and then correct and learn from them. If you’ve got all the eggs in that one basket, you’re not going to run with it. Iteration lowers risk. We can move faster. We can try different approaches or tools. If one doesn’t work we can easily regroup and the damage, if any, is limited. Instead of sending the entire organization down the same track at the same time and same speed (which by the way is too slow for some and too fast for others in our institution), we could use fleets of cars or trucks that communicate. They can follow similar paths and learn from each other. They can deviate slightly to better fit their circumstance. And, if one fails, the others survive and thrive.
The great inventor and innovator Charles Kettering knew that iteration, repeated attempts, was key to innovation and creativity. He knew that to minimize risk we need to learn from each attempt so that the next attempt had a greater chance of success. He said:
We often say that the biggest job we have is to teach a newly hired employee how to fail intelligently.
Unfortunately “failure” is too often a 4-letter work in higher education. Again it is ironic. Our researchers know that learning from failure in the labs is how science advances, but we don’t apply the concept to our own institutions.
A word on “pilots” is appropriate here. Doing a “pilot” of some initiative is not iterating. Pilot projects can be useful. They can be great laboratories to learn and explore. But too often they are treated as “let’s do a small version of the concept first and when we show some evidence that it works, we’ll turn around and ‘roll it out’ to the whole organization”. Pilots have many issues. One is that the real purpose often isn’t to learn or explore – we’re convinced with unquestioned brilliance that it’s the right or best solution, so what’s to learn? Instead the real purpose is often to simply demonstrate that it will work. We’re trying to demonstrate that our idea is as brilliant as we think. Never mind that we’re likely to fall prey to confirmation bias in our judgement that it works. The pilot cannot be allowed to fail. So additional resources, energy, and attention are given the pilot – resources that cannot be scaled or applied to the larger organization. Further, doing a pilot really is just a slight postponement in the big bang. Iteration doesn’t work that way.
Let’s consider an example that many institutions are pursuing today. In the name of increasing student completion rates, the assumption is that students have too many options and choices in the curriculum. The favoured idea is that if we reduce the choices and provide a clearer pathway to a degree that has no options for getting “off-track”, then students will arrive at the destination (degree) in larger numbers. I don’t want to discuss the relative merits of the concept here – that’s a topic for other future posts. What we want to consider is how an institution goes about changing all of its degree plans of study to conform with the pathway idea. A common approach is to do a pilot first. Pick one or maybe two degree programs and redesign them. Often the pilot programs are either smaller, already more coherent, or just staffed by true believers. The pilot is deemed a success because the degree pathways have been redesigned. Note that success has already been redefined. Success is now actually doing the idea, not achieving the transformative goal the idea is supposed to accomplish. The pilot succeeded because we have proof of a redesigned plan of study, not because we have proof of higher completion rates. With the success of the pilot, the institution’s leadership is eager to “roll out” the innovation to all programs and all degrees. Phase II begins with a massive simultaneous effort to redesign all degree pathways. It takes time. Conflicts arise. Questions and issues not seen in the pilot arise. It seems each program has its own problem fitting the template. That’s not iteration. That’s big bang Grand Initiative.
Iteration approaches the challenge differently. Iteration appears more like a series of “pilots”. It might start with a pilot too, but the purpose of the first pilot is different. It’s to learn and to see what’s involved in doing this. A second pilot can be rapidly dispatched with some modification based on lessons of the first pilot. Then a third and fourth “pilot” might be started in parallel. Rather than doing all programs at the same time, iteration calls for repeatedly taking each program individually but learning the whole way. Early efforts are used to create tools to accelerate and simplify the next program. Rather than emphasizing all programs doing the same thing simultaneously, each program is urged to make it fit to their needs, pass along lessons to the next program, and complete the change rapidly.
That should bring us to the third paradox, but this post is already getting quite long. So in the interest of iteration, we’ll stop here and pick up the story with paradoxes 3-5 in another post soon. I hope to get the next post up this weekend.