What Might be the Future of Educational Reform

Ken Allan recently referenced James Kauffman in this post, who correctly notes that many calls for change in education fail to define any specifics of change. In this post I want to look at trends that are relevant to much of educational reform (and to work practices in general), trends that might help specify change needs.

As the world changes, so do learning needs.  Raelin et al (2010) points out that a 20th Century scientific approach to practice, characterized by standardization, no longer suffices.    I find this sentiment to be an echo of Hagel, Browns and Davison (HBD) who encourages us toward a “pull” model of learning.  This means not depend on trying to predetermine knowledge and push it in advance to where it will be needed, but instead to focus on tapping into knowledge flows and pulling knowledge to where it is needed right now.  The Agile Manifesto, explained in Sahana’s view here, is a recent idea that highlights a new found importance for flexibility beyond practice standards.  I find this flexibility beyond standards to be an important pattern in all approaches.

For most of history, education and work practices were predominately guided by a tradition that began in ancient Greece.  Each generation added to that tradition, but learning’s foundation remained in this ongoing tradition.  It’s still with us today, as it rightfully should be.  We can’t escape our heritage, at least not completely.  But, there were limitations to depending on tradition these limitations were to be exposed by the enlightenment thinkers.

The enlightenment sought to place the authority of science above tradition as a new method to make judgements.  Empirical science was used to build practice standards that were considered more authoritative than tradition and positivism sought to extend these standards into every aspect of our lives and our practices.  Successful standardization in 20th Century Fordist practices in some ways can be seen as the apex of enlightenment thinking about standards and practice.  However, in many other ways the triumph of scientific standards proved allusive.  In the face of a growing recognition of the complexity of life, especially the opened nature of social life, postmodern, post-structuralist, post-Fordist and many other critiques took root.

While standardization proved very productive in closed and limited process situations, much of the most importance processes in our lives were open, multidimensional and complex.  While science would help us to better understand these processes, these processes would not conform to universal standards and simple applications of scientific experimental findings.  It became recognized that something more flexible than standards are required.

If you look at current suggestions for change in education, Raelin’s practice-based learning, HBD’s Power of Pull or software developer’s calls for agility; they have a common theme.  The need to improve practice in ways that are open and flexible, are beyond what can be achieved by standards, and allow people to make use of the mind’s capacity for pattern recognition when responding to everyday complexity.  This seems like as good a trend as any when specifying educational reform.  That does not mean the closed processes and standards are finished.  The evidence-based practice movement can be seen as a recognition that practice standards and protocols can still be improved.  But we also need a layer of learning that sits on top of practice standards.

What is the driving force of this new level? It’s not tradition.  It’s not science, at lest in a positivist sense.  It seems to be digitally enabled collaboration, the enablement of creativity, the ability to adapt to contexts, and maybe much more.  I think the jury is still out, while the research come in.

How Do You Innovate

Jon Kolko wrote How Do You Transform Good Research Into Great Innovations? on the fast company design blog.  I would summarize his view as design synthesis which involves:

  1. Visualize your data
  2. Search for Patterns
  3. Develop and experiment with different models (his definition of models = a visual representation of an idea, an artifact)

A good process.  This point is important:

Because these are thinking tools, tools for synthesis, there’s only one wrong way to do this: not doing it at all. Looking at the data and talking about the data doesn’t count. If it isn’t modeled, written, drawn, and otherwise solidified in an artifact, it never happened. (Emphasis added)