The following is an attempt to reflect upon an EDUCAUSE Review article by Bryan Alexander entitled “Apprehending the Future: Emerging Technologies, from Science Fiction to Campus Reality”. I’m doing this because I believe the topic, at least at first glance, has connections with the new role I’m meant to fulfill at my current institution.
Provides an overview of five different methods that can be used to apprehend what the future might hold for higher education in terms of technology and its application. The methods are:
- The environmental scan.
- The delphi method.
- Prediction markets.
The articles givesEach of the methods get the following treatement:
- A brief description;
- Pointers to relevant examples; and
- A summary of the advantages and disadvantages.
The last main section recognises that all of these methods are at best, partial solutions and raises a number of challenges they face, including:
In reflecting on the problems with these methods the article suggests a number of reasons for consuming resources to undertake them. The reason I like most and which is classed as the best is that the intellectual exercise prepares the individuals and the institution.
Provides a good overview of the methods listed. What I found most interesting were the pointers to the relevant examples of each method that exist within the university/educational technology fields.
The following are some nit picks. Whether you think them relevant may be a factor of your perspective or opinion. None of them limit the value of the article.
Seems to miss some disadvantages
For example, I believe that the reliance on experts in the Delphi model is a limitation especially when dealing with potential paradigm shifts – probably connected to the unknown-unknowns mentioned in the latter parts of the article. Experts are experts because they have a large number of deeply complex mental patterns associated with a certain world view that have been built up over time. A paradigm shift encapsulates radical change in that world view which makes expert knowledge somewhat less than appropriate.
The example I’ve seen first hand is that of print-based distance education experts faced with the rise of the Internet. Or more broadly, the hypermedia community when faced the idea of the World-Wide Web.
This page on the Delphi Method seems to suggest that this is a weakness, but perhaps not for the reasons I give.
Prediction markets are not “wisdom of the crowds”
The article associates prediction markets with the wisdom of the crowds. I’m influenced here by my following of Dave Snowden who argues, quite effectively in my opinion, that prediction markets are not examples of the wisdom of crowds. Though the Wikipedia page on the wisdom of crowds thinks they are.
Showing my bias/Snowden influence – a Snowden post on scenario planning
Snowden and his group have developed the Future Backwards as an alternative to scenario planning. So it might belong here as another method.
I need to take the time to visit and examine each of the examples given of the methods. By combining the results of those with my own thinking and experience should help something interesting arise. As the article points out, the intellectual exercise of reflecting on the findings will help expand perceptions and better prepare for thinking about the future.
The next two sections make related points. Essentially, I’m trying to develop an argument that apprehending the future is only half the argument.
The role of context
The focus of the methods discussed is on knowing what the future brings. It focuses outward, not inward on the local context. I think both is needed.
The point I’m trying to make is that it’s not just about the nature of the next wave of technology but it’s how that technology is combined with the problems faced within particular contexts that can generate interesting approaches. Sometimes those approaches can be totally unthought of by the original developers of the technology. The article offers a William Gibson quote
“the street finds its own uses for things”
that captures some of this. The street or each unique context may generate a new and interesting application that the experts don’t see as they are divorced from the complexities of the context. They’ve abstracted away all those lower problems and consequently miss some stuff.
A famous Allan Kay quote seems to have the essence of what I’m trying to get at
the best way to predict the future is to invent it
You don’t know what you’ve got until you build it
Related to the above point is the assumption that if you know which future technologies are coming then you can predict the impact it will have on your local context. This assumes that the local context is, in the sense of Snowden’s Cynefin framework, is simple or complicated. In such systems cause and effect exist. You, or an appropriately skilled expert, can predict what will happen when you introduce a new technology.
Personally, I believe that the context in which learning and teaching takes place within a university is complex. It is the type of system where cause and effect cannot be predicted. You never really know what is going to happen until you try it. Also, if you try the same thing at different times, or in slightly different contexts, you are likely/certain to get different outcomes.
The article does make the point that
One challenge to any futures method is the sheer complexity of the future.
I’m suggesting that the context is another source of complexity that needs to be considered.
A Snowden suggestion is safe fail probes. Matching such probes with the options and possibilities identified by the approaches described within this article could prove useful.
Perhaps a focus on response is better?
One last thought, perhaps it’s more important to build into the system the ability to respond quickly to near-term changes, rather than predict long-term changes.