I’m working on the learning science workshop I’m going to present at DevLearn  next month, and in thinking about how to represent the implications of designing to account for how we work better when the learning context is concrete and sufficient contexts are used, I came up with this, which I wanted to share.

The empirical data is that we learn better when our learning practice is contextualized.  And if we want transfer, we should have practice in a spread of contexts that will facilitate abstraction and application to all appropriate settings, not just the ones seen in the learning experience.  If the space between our learning applications is too narrow, so too will our transfer be. So our activities need to be spread about in a variety of contexts (and we should be having sufficient practice).

Then, for each activity, we should have a concrete outcome we’re looking for. Ideally, the learner is given a concrete deliverable as an outcome that they must produce (that mimics the type of outcome we’re expecting them to be able to create as an outcome of the learning, whether decision, work product, or..).  Ideally we’re in a social situation and they’re working as a team (or not) and the work can be circulated for peer review.  Regardless, then there should be expert oversight on feedback.

With a focus on sufficient and meaningful practice, we’re more likely to design learning that will actually have an impact.  The goal  is to have practice that is aligned with how our learning works (my current theme: aligning with how we think, work, and learn). Make sense?

So I was reading something that talked about designed versus emergent experiences.  Certainly we have familiarity with  designed experiences: courses/training, film, theater, amusement parks. Yet emergent experiences seem like they’d have some unique outcomes and consequently could be more valuable and memorable.  So  I wondered how  an emergent experience might play out to reliably generate a good experience, regardless.

The issue is that designed experiences, e.g. a Disney ride, are predictable.  You can repeat them and notice new things, yet the experience is largely the same.  And there can be brilliant minds behind them, and great outcomes including learning.  But could and should we shoot higher?

What emergent experiences do we know?  Emergent means having to interact with something unpredictable and perhaps even reactive. It could be interacting with systems, or it could be interpersonal interaction.  So, what we see in clouds, and experiences we have with games,  and certainly interpersonal experiences can be emergent.  Can they repeatedly have desired outcomes as well as unpredictable ones?

I think the answer is yes if you allow for the role of some ‘interference’.  That is, someone playing a role in controlling the outcomes.  This is what happens in Dungeons and Dragons games where there is a Dungeon Master, or in Alternate Reality Game where there’s a Puppet Master, or  in social learning where an instructor is structuring group assignments.

I’m interested in the latter, and the blend between.  I propose that our desired learning experiences should go beyond fixed designs, as our limitations as designers and SMEs will constrain what outcomes we achieve.  They may be good, but what can happen when people interact with each other, and rich systems, allows for more self discovery and ownership.  An alternative to social interaction would be practice set in a simulation that’s richer and with some randomness that mimics the variations seen in the real world that go beyond our specific designs.

By creating this richness through interpersonal interaction via dialogue and different viewpoints, or through simulations, we create experiences that go beyond our limitations in specific design.  It certainly may go beyond our resources: branching scenarios and asynchronous independent learning are understandably more pragmatic, but when we can, and when the learning outcomes we need are richer than we can suitably address in a direct fashion, say when we need flexible adaptation to circumstances, we should consider designing emergent experiences.  And I’m inclined to think that social learning is the cheaper way to go than a complex system-generated experience.

I’m just thinking out loud here, a tangent sparked by a juxtaposition, part of my ongoing efforts to make sense of the world and apply that to creating more resilient and successful organizations. Based upon the above, I think emergent experiences can create more adaptable and flexible learning, and I think that’s increasingly needed. I welcome your thoughts, reflections, pointers, disagreements, and more.

I was thinking about how to make meaningful practice, and I had a thought that was tied to some previous work that I may not have shared here.  So allow me to do that now.

Ideally, our practice has us performing in ways that are like the ways we perform in the real world.  While it is possible to make alternatives available that represent different decisions, sometimes there are nuances that require us to respond in richer ways. I’m talking about things like writing up an RFP, or a response letter, or creating a presentation, or responding to a live query. And while these are desirable things, they’re hard to evaluate.

The problem is that our technology to evaluate freeform text is difficult, let alone anything more complex.  While there are tools like latent semantic analysis that can be developed to read text, it’s complex to develop and  it won’t work on spoken responses , let alone spreadsheets or slide decks (common forms of business communication).  Ideally, people would evaluate them, but that’s not a very scalable solution if you’re talking about mentors, and even peer review can be challenging for asynchronous learning.

An alternative is to have the learner evaluate themselves.  We did this in a course on speaking, where learners ultimately dialed into an answering machine, listened to a question, and then spoke their responses.  What they then could do was listen to a model response as well as their response.  Further, we could provide a guide, an evaluation rubric, to guide  the learner in evaluating their response  in respect to the model response (e.g. “did you remember to include a statement and examples”?).

This would work with more complex items, too.  “Here’s a model spreadsheet (or slide deck, or document); how does it compare to yours?”  This is very similar to the types of social processing you’d get in a group, where you see how someone else responded to the assignment, and then evaluate.

This isn’t something you’d likely do straight off; you’d probably scaffold the learning with simple tasks first.  For instance, in the example I’m talking about we first had them recognize well- and poorly-structured responses, then create them from components, and finally create them in text before having them call into the answering machine. Even then, they first responded to questions they knew they were going to get before tasks where they didn’t know the questions.  But this approach serves as an enriching practice on the way to live performance.

There is another benefit besides allowing the learner to practice in richer ways and still get feedback. In the process of evaluating the model response and using an evaluation rubric, the learner internalizes the criteria and the process of evaluation, becoming a self-evaluator and consequently a self-improving learner.  That is, they use a rubric to evaluate their response and the model response. As they go forward, that rubric can serve to continue to guide as they move out into a performance situation.

There are times where this may be problematic, but increasingly we can and should mix media and use technology to help us close the gap between the learning practice and the performance context. We can prompt, record learner answers, and then play back theirs and the model response with an evaluation guide.  Or we can give them a document template and criteria, take their response, and ask them to evaluate theirs and another, again with a rubric.  This is richer practice and helps shift the learning burden to the learner, helping them  become self-learners.   I reckon it’s a good thing. I’ll suggest that you  consider this as another tool in your repertoire of ways to create meaningful practice. What do you think?