Diffusion, Systems, and Complexity

In the past, I have observed first hand a number of approaches to innovation diffusion in education (and other fields). These run the range from what would be classified as management innovations (i.e. evaluation protocols and state standards), to technology innovations (i.e. 1:1 iPad model). In reading the articles from this week and last week, I was struck repeatedly by three recurring thoughts (a) diffusion of innovation is best achieved within organizations, in a peer-to-peer fashion (Abrahamson, 2013), (b) the observability of the benefits of innovations in education is difficult to measure, and (c) the education process already represents such a complex system that predicting the effects of any innovation is extremely challenging.

In terms of diffusion, education frequently experiences top-down mandates in terms of standards and changes to management. Almost invariably, these are received with skepticism for a number of reasons. First, there is a history of “innovations” that come and go. Like the weather in New England, the mantra among my colleagues has often been, “If you don’t like this year’s mandate, just wait. It will change.” In this atmosphere, innovations are not adopted, even if it might have some benefit, because there is a sense that investing in the latest innovation will be wasted when the next innovation takes its place. Compounding this feeling is a sense that new “innovations” are cyclical, returning every few years under different names. In some cases, the adaptability of the innovation may promote at least partial adoption. In one district, the administrators were generally supportive of the concept that there was not a one-size-fits-all innovation. Many mandates were presented with the disclaimer that staff would be working to adapt the innovation to our district’s style, so that disruption would be minimal and benefit would be maximized. The most successful innovations were always those that were adopted by a handful of teachers (i.e. the “innovators”), who could then communicate their experiences to other teachers, answer questions, and offer guidance in a peer-to-peer manner.

The requirement that the benefits of innovation be observable (Minishi-Majanja & Kipling’at, 2005) is another significant challenge. How do we measure the effect of educational technology? The simplest measures, such as pre- and post-testing scores are, by necessity, limited in the scope of their assessment. This is complicated by the fact that any innovation, especially a technology-based innovation, may produce an effect simply based on its novelty. To measure the long-term benefit of technology on learning requires a significant investment of time and effort, which can be a barrier to many educators, who don’t want to have these innovations significantly impact their teaching. This is both a cost in terms of social and real capital (Surry & Farquhar, 1997).

The complexity of the systems involved in the education process is a major stumbling block for educational technology innovation. Between individual student needs, different learning domains, the many different views on pedagogy, and the complexity of the technology in general, there are so many variables (Ni & Branch, 2008). As we have already seen with such “innovations” as Common Core and standardized testing, trying to make one strategy work, even for a large number of cases, is nearly impossible and ultimately not in the best interest of the students or the instructors.

Another thing that struck me, reading from Tidd (2010) was the distinction between technological, organizational, and commercial innovation.  From my perspective, I look at my emerging trends in personalized, student-centered learning as being a combination of all three innovation types. This is where I am most excited to explore the possibilities of personalization and adaptation. One way in which innovation is diffused is via reinvention (Abrahamson, 2013). As education becomes more student-centered, I think that reinvention itself becomes an innovation. Can we create a system that is truly customized for the student, the instructor, and the content? If we can do that, and if we can use learning analytics to observe the systems, then I think that we will not only change the way we teach and learn, but we will also find entirely new ways of teaching and learning that we never considered before.



Abrahamson, E. (2013). Innovation diffusion. In E. H. Kessler (Ed.), Encyclopedia of management theory (Vol. 2, pp. 373-377). Thousand Oaks, CA: SAGE Publications Ltd.

Minishi-Manaja, M. K., & Kiplang’at, J. (2005). The diffusion of innovations theory as a theoretical framework in library and information science research. South African Journal of Libraries & Information Science, 71(3), 211-224.

Ni, X., & Branch, R M. (2008). Complexity theory. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll (Eds.), Handbook of Research on Educational Communications and Technology (3rd ed.). New York: Taylor & Francis.

Surry, D. W., & Farquhar, J. D. (1997). Diffusion theory and Instructional technology. Journal of Instructional Science and Technology, 2(1), 24-36.

Tidd, J. (2010). Gaining momentum: Managing the diffusion of innovation. Singapore: World Scientific Publishing Company.

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