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I don’t think that I’m the first person to say that careers should be treated as experiments.  This sounds like something that I stole from Wired Magazine or something - but I can’t find the source for the idea on Google.  Maybe you can help.

Wherever the idea that we should treat our careers as experiments originated - the notion is certainly worth discussing in our higher ed context.

Here are 7 reasons why we should treat our (higher ed) careers as experiments:

1 - The Outcome Is Unknown:

We don’t know how our higher ed career is going to play out.  This uncertainty is particularly acute for us alternative-acacademics (alt-acs), non-faculty educators, and edtech professionals.  There are no established career paths - and much of the work is too new to learn from those who might have come before.

Situating the uncertainty of our higher ed careers within the language of experiments may help ease our anxiety about unknowable futures.  The full outcome of any new experiment can never be totally known.  If you knew how the experiment was going to turn out then there would be no reason to run the experiment.  The lack of pre-determined outcomes is a feature, not a bug, of experiments - and maybe we should think of our careers in the same way.

2 - Hypothesis Testing:

We are not certain how our higher ed careers are going to go - what sort of work we we will be doing and how effective we will be at this work - but that does not mean that we shouldn’t make predictions.  Articulating the path that we think our career path will take is helpful in that we will learn a great deal when we inevitably deviate from our predictions.

What if we changed the annual performance review process to one of designing an experiment?  If instead of writing “smart goals” that we wrote out a series of hypotheses about how the next year (or 5 years) will go.  Where will we have the most impact?  Where will we contribute the most to the mission and goals of our institutions?  We would then be able to look a year (or 5 years) later to see how well our hypotheses matched the results.

3 - The Importance of a Theoretical Framework:

The progression of our careers should be situated within a framework of values.  We should judge the success of our careers based on how well our work supports the values that we hold most closely.  My values are all around the importance of a liberal arts education.  I see technology as one tool (the tool that I understand best) to help improve learning.  I believe in the relational model of education - one in which a well-supported educator and a motivated student are able to co-create knowledge.   The idea that technology can assist, but never replace, educators is a big idea - and one that I think that I can situate and evaluate my career.

Experiments - or careers -that are not situated in a theoretical framework (loosely defined) are difficult to evaluate.  Without a theoretical framework the work is disconnected from the efforts of others.  Lacking a theoretical framework it is impossible to contextualize individual results as part of a larger story.

4 - Learning Is the Goal:

The reason that we do experiments is to learn something new.  Learning is the whole point of the experiment.

What if we thought that the point of our careers is to learn?  This may seem like a stance of ridiculous privilege.  Aren’t careers really jobs - and isn’t the point of jobs to create value?  And learning is great and all - but must of us work because we need to pay the mortgage, put braces on our kids, save for higher education, etc. etc.

Sure, that is all true.  But thinking of the point of our careers as opportunities for learning may help us be better at our jobs.  We may be more open to criticism, to changing our ways of working, and to learning from past mistakes.  We may seek out opportunities to learn - and therefore get better at our work.

We also may be a bit more relaxed about our uncertain careers.  Careers that will inevitably have setbacks and reversals.  What higher ed career is linear nowadays?  Especially the careers of non-faculty and contingent educators.

5 - Building on Prior Knowledge:

An experiment is never done in isolation.  An experiment always builds upon the thousands of experiments that came before.  We should think of our careers in the same way.

Unfortunately, it is difficult for those of us in non-traditional higher ed careers to learn from each other and from past experience.  We lack good mechanisms to share with each other what is working, and what is not, as we navigate our careers.  Much of the work that we are doing does not have much of a precedent - we are sort of making it up as we go along.

5 - A Willingness to Fail:

The process of knowledge creation is never smooth.  Big advances don’t happen without big failures.  We think of experiments that don’t work as a normal part of the scientific method.

If everything always goes well in our careers then we are probably doing something wrong.  A totally smooth career is most likely a sign of inadequate ambitions.  If we want to make a real difference in our work then we will have to accept that most of the time things will not work out as we planned.

This sort of resilience is easy to talk about and hard to practice.  Thinking about our careers as experiments might help.

6 - Making Decisions Based on Data:

The evaluation of an experiment depends on the assessment of the data.  Conclusions should follow the evidence.

What sort of data do we collect about our careers?  What are the metrics that we use to demonstrate our impact?

Answering these sorts of impact questions may be easier for traditional academics.  You can count your journal articles, book chapters, and books.  You can look at the impact factor (IF) of the journals that you publish in.

Quantifying impact is more difficult for non-traditional academics.  What counts, and what does not?  Most of the projects that you work on are with a team.  It is seldom that any of us work alone.

Still it - it is important to try to bring some metrics, some data, to understanding and growing our careers.  It may not always be possible, but we should be looking to make data-driven career decisions.

7 - Sharing Your Results:

How public should we be with our career journey?  How much weight should be give to sharing with others what we learn along the way with our careers?

If we think of our careers as experiments, we may be more willing to prioritize outreach and sharing.  We will work to situate our individual careers within a community of practice.  We will see our work as part of a larger discipline.  Our individual career goals will be generated and realized within a larger context.  Career sharing is less about our individual paths, and more about how careers (especially non-traditional academic careers) are changing.

How do you think we can treat our careers as experiments?


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