A couple of weeks ago, I posited what I thought might make the basis for a sustainable degree program. That is, one that prepares students for a world with increasing change. In it I talked about the domains that I thought would provide a solid basis, but I did  not talk about something else important.  It’s also about the learning to learn and work skills that accompany the foundational knowledge. It’s about ‘meta’ skills.

Meta-skills, like learning to learn and learning to work well (21C skills), can’t be developed on their own. They  need to be layered on top of other things. We teach them  across  other domains, so they’re abstracted and can be reapplied to new problems and situations.   Thus, these challenges must reappear across the curriculum.

What skills?  I think things like the ability to research questions, design and run experiments, communicate results, collaborate, ask and answer questions in ways that work, and more.  This includes using technology for these tasks, as well as working with others.  Thus, creating spreadsheets, diagrams, and presentations is as much a part as is participating in and leading projects, commenting constructively, and coaching and mentoring.

So, using an application-based pedagogy,  there are a series of activities that require application, but they vary in type: research, design, problem-solve, interact, and in output. Then we evaluate those cross-discipline skills as well as the domain knowledge and skills.  How was your research process on this interface design project?  How well did you communicate your learnings from that experiment on recursion in learning programs?

Curriculum  and the pedagogy can be refined, and in fact are interleaved. Then we use technology to serve both as a tool for learners to construct (make) outcomes, and to track their progress.  We need to go meta in both!

And this isn’t true just for formal education, and can and should play a role in organizational learning as well.  We shouldn’t take our learners’ learning skills for granted, and we can and should track and develop them as well.  This isn’t currently supported, but perhaps can be in existing tools, or we may need a new platform.  But we should, I suggest.  Your thoughts?

Things are moving faster, and careers are supposed to be changing more frequently. What does that mean for university degrees (or other employment preparation)?  Yes, university degrees aren’t necessarily  just employment preparation, but I’m thinking about a degree program that provides a useful preparation for the coming world. And I think it’s got several key components.

For one, anything we do, in working together and in meeting client needs,  must be aligned with how our brains work. Industrial design, interface design, learning design, marketing; increasingly everything  about  our products and services must be producing  experiences.  And, if the prognostications are to be believed, experiences that  transform  us.  Increasingly, organizations will need to work in such ways, and those that understand this will be core. Similarly, increasingly products and services will likewise need to adapt. At core, everything we do revolves around thinking,  and our brains aren’t changing. Understanding cognition is a sustainable value.

A second such areas is understanding information technology. Increasingly, the capability of computational systems to serve as configurable information processing machines is fundamental to society. It’s the perfect cognitive complement, doing well what our brains don’t, and vice-versa.  And while the technology continues to evolve, some core understandings don’t. Computational thinking is focused on breaking down problems into tractable steps, and that’s part of it. And understanding how AI works (e.g. machine learning, symbolic logic, neural nets, etc), and even quantum computing, are tools to solve problems. People need to understand computational technology to complement our cognitive capability, and it’s stable enough to again be a sustainable edge.

Then, the question is, what complements these to provide a solid foundation. I have two answers: one is design (e.g. design thinking), and the other is business. And I had trouble reconciling this until recently.  So, in some sense, design is an  outcome of our cognitive processes, and an application. (To design is human!)  But being systematic about it is a useful integration of the other two. For those who haven’t had experience in business, however, an overall understanding of business is key.  This suggests that a full bachelor’s program would include design  and business, while a master’s program could focus more specifically on the design (assuming some business experience).

Could these be minors on some other area people might want? It might be good to supplement this with specific interests whether bio, art, or what have you.  You do want to support people’s passions. But I’ll suggest that these elements  should be part of all folks preparation for life going forward.  So, what do you think?

Is there any difference? At core, my new book  is  about learning science. And, as I’ve lamented before, the lack of understanding of cognitive science is a barrier to better L&D. However, it takes a certain mindset to put this into practice in practical ways.

Should you overall be optimistic or cynical?  Applying cognitive science (see what I did there?), I would err on the side of optimism.  Research suggests a positive attitude is overall better.  Thus, I guess I’m arguing for the former ;). The alternative, a cynic still looking for good, is less optimal.

However, optimism tempered with  a healthy skepticism! There are those who’d take advantage of naivete, as has reliably been exposed.  A vigilant evaluation of what’s presented is healthy for dialog and moving the industry forward!

You need to be prepared for the variety of ways people can mislead you (and even themselves).  Without a decent understanding of scientific validity, you might be swayed by statistical sleight-of-hand.  Worst case, you listen to those who carry the standard of rigor in evaluation.  I don’t necessarily mean the scientists, because they don’t always present it in comprehensible ways (writing in their native academese).  Instead, there are those who serve as translators of research to practice.

People like Will Thalheimer, Patti Shank, Julie Dirksen, Guy Wallace, Mirjam Neelen, and more (including yours truly), have boiled down learning science into practical approaches. Whether it’s overviews, processes, or even acronyms, their guidance is soundly based.  We may not always agree, but you’re far better off betting on them than on those with a vested interest.

On your own, of course, you should be conducting several validity checks.  Who’s telling you, and what’s in it for them?  Is their message supported by external validation? Are there alternate views? Does it pass the ‘sniff’ test (that is, does it make a plausible causal story)?  Of course, “if it sounds too good to be true, it probably is”.

In addition to empirical grounds, one should also evaluate the theoretical basis.  Did the work emerge from empirical data, or was it made from someone’s musings, and untested?  Is there a reason to accept the underlying frameworks?

Overall, I suggest that practitioners in learning first need to be grounded in understanding  how we learn. Then, I reckon we need to be rigorous in evaluating new approaches.  There will be wheat amongst the chaff, but the relative ratios are the issue. Make sure you’re finding nuggets, not tailings (I like my metaphors mixed).