Why do so many well-conceived education reform designs fail in implementation? For the same reason that old-school top-down software development fails in today’s rapidly evolving Internet-based marketplaces.
In both cases there is an implicit false assumption that the designers can accurately predict what users will need in perpetuity and develop a static one-size-fits-all product. In response to that fallacy, both software development and education reform have developed agile models of adapting to unpredictable environments. Independently, these have failed to scale to their potential in the real-world trenches of the U.S. educational system. Interdependently, could they achieve the results that have so far eluded each?
Traditional education reform, like traditional engineering development, invests heavily in up-front design. In engineering, this makes sense when dealing with deliverables that are hard to change, like silicon, or when mistakes are not an option, as with space flight or medical technology. However, when the deliverable is malleable, as with consumer software, once the market starts to change the implementer is trapped between the choice of piling modification upon modification until the initial design is completely obscured, or plowing ahead unswervingly only to deliver a product that is obsolete on delivery. The software developer is destined to be outperformed by more nimble developers who can adapt effectively to changing market needs, new information, and an evolving industry.
Similarly, education reform interventions are rigidly constrained. To prove a treatment’s effectiveness, research needs to demonstrate that one particular variable in a messy human dynamic environment is responsible for a change in student outcomes. This means that an educator and his/her students must behave precisely as designed in order for the research to be valid. Tremendous resources are spent in these kinds of trials to ensure “fidelity of implementation.” In this situation, the educator is trapped between the choice of corrupting trial data by changing the implementation to meet the changing needs of students and the environment, or plowing ahead only to limit the good he/she can do for students to the lowest, common, measurable denominator.
In the software world, we address this dilemma through an iterative development model. That is, we assume that when we are thinking about what users might need or how they will use our product, we will get some things wrong. So we code up some simple end-to-end functionality, throw it out for people to use, and then improve it iteratively based on feedback from our users. This feedback may be explicit, in the form of questions and requests, or implicit, based on our observations of how the software is used. It may well be automated, in the way Google instruments the applications we use and modifies them based on how we engage.
In the education world, there is also a shift away from rigid implementations to more scalable adaptive approaches. Alan Bain writes in “The Self-Organizing School” about how the metaphor of emergence mediates the tensions between top-down control and bottom-up chaos. Rather than designing and dictating the everyday workflow of educators and students, the self-organizing school identifies a small set of simple rules. These rules, in combination with multiple feedback loops, drive and iterate the work of teachers, students, administrators and others involved in teaching and learning. As with the emergent behaviors of ant hills and flocks of birds, the simple rules drive elegant, complex system-level behaviors that adapt to changing circumstances.
This model of education reform depends on real-time, effective feedback loops of information at a scale that is possible only with the support of technology. But the technology platforms to support a self-organizing school haven’t been developed — as with most educational use of technology they are likely to be pulled together on an ad-hoc basis with minimal support, making them clunky to use and difficult to modify. As a result, rather than enabling and supporting adaptation, they are just as likely to carve existing processes into digital concrete and become a force resisting change.
How do you get to a technology platform that supports scalable education reform? Perhaps the best option is to grow it. Plant it in the fertile soil of existing open source education software and open education resources. Seed it with some simple elements: digital content creation or assessment distribution or maybe collaboration spaces or online courses. Feed it with a few data flows: perhaps computer-graded quiz results to students, teachers and parents; homework assignments and recorded lectures in one direction, completed projects in the other; automated attendance data to teachers and administrators. Immerse it in an environment built on feedback loops that are nourished by the data that is generated on the platform. Adapt and evolve it in response to decisions and needs that are uncovered by those feedback loops.
In symbiosis, the platform and the practices it supports mature and reach a sort of dynamic equilibrium of continual, steady, incremental growth. As it matures iteratively, the technology platform becomes ready for transplantation to other environments.
Traditional education reform fails to scale because top-down designs don’t survive the reality of the day-to-day classroom. Emergent designs adapt to real circumstances but depend on extensive data collection driving feedback loops at every level. Not only is this not well supported by existing technology implementations, but the functional requirements of those implementations are not yet well understood. Through a process of co-evolution, those requirements can be surfaced and technology platforms developed that can then enable education reform to scale.