I’ve documented some quips in the past, but apparently not this one yet. Prompted by a nice article by Connie Malamed on creativity, I’m reminded of a saying, and the underlying thinking. Here’s both the quip and some more on systematic creativity. First, the quip:

Systematic creativity is not an oxymoron!

In her article, Connie talks about what creativity is, why it’s important, and then about steps you can take to increase it. I want to dig a wee bit further into the cognitive and formal aspects of this to backstop her points. (Also, of course, to make the point that a cognitive perspective provides important insight.)

As background, I’ve been focused on creating learning experiences. This naturally includes cognition as the basis for learning, experiences, and design. So I’ve taken eclectic investigations on all three. For instruction, I continue to track for insights from behavioral, social, cognitive, post-cognitive, even machine learning. On the engagement side, I continue to explore games, drama, fiction, UI/UX, roleplay, ‘flow’, and more. Similarly, for design my explorations include architecture, software engineering, graphic, product, information, and more.

One of the interesting areas comes from computer science, searching through problem-spaces for solutions. If we think of the solution set as a space, some solutions are better than others. It may not be a smooth continuum, but instead we might have local maxima that are ‘ok’, but there’s another elsewhere that’s better. If we are too lax in our search, we might only find the local maxima. However, there are ways to increase the chances of exploring a broader space, making a more global search. (Of course, this can be multidimensional.)

Practically, this includes several possibilities. For one, having a diverse team increases the likelihood that we’ll be exploring more broadly. (On the flip side, having folks who all think alike mean all but one are redundant. ;). For another, brainstorming properly keeps the group from prematurely converging. We can use lateral random prompts to push us to other areas. And so on. I wrote a series of four posts about design that included a suite of heuristics to increase the likelihood of finding a good solution. Connie’s suggestions do likewise.

I also suppose this is a mental model that we can use to help think about designing. Mental models are bases for predictions and decisions. In this case, having the mental model can assist in thinking through practices that are liable to generate better design practices. How do we keep from staying localized? How do we explore the solution space in a manner that goes broad, but not exhaustively (in general, we’re designing under time and cost constraints).

Creativity is the flip side of innovation. It takes the former to successfully execute on the latter. It’s a probabilistic game, but we can increase our odds by certain practices that emerge from research, theory, and practice. We also want to include emotion in the picture as well, in our design practices as well as in our solutions. When we do, we’re more likely to explore the space effectively, and increase our chances for the best solution. That’s a worthwhile endeavor, I’ll suggest. What are your systematic creativity approaches?

I recently mentioned that one of the problems with research is that things are more interconnected than we think. This is particularly true with cognitive research. While we can make distinctions that simplify things in useful ways (e.g. the human information processing system model*), the underlying picture is of a more interactive system.  Which underpins why it makes sense to talk about Learning Experience Design (LXD) and not just instructional design. We need to accommodate complexity in learning design. (* Which I talk about in Chapter 2 of my learning science book, and in my workshops on the same topic through the Allen Academy.)

We’re recognizing that the our cognition is more than just in our head. Marcia Conner, in her book Learn More Now mentioned how neuropeptides passed information around the body. Similarly, Annie Murphy Paul’s The Extended Mind talks about moving cognition (and learning) into the world. In my Make It Meaningful workshops (online or F2F at DevLearn 19 Oct), I focus on how to address the emotional component of learning. In short, learning is about more than just information dump and knowledge test.

Scientifically, we’re finding there are lots of complex interactions between the current context, our prior experience, and our cognitive architecture. We’re much more ‘situated’ in the moment than the rational beings we want to believe. Behavioral economics and Daniel Kahneman’s research have made this abundantly clear. We try to avoid the hard mental work using shortcuts that work sometimes, but not others. (Understanding when is an important component of this).

We get good traction from learning science and instructional design approaches, for sure. There are good prescriptions (that we often ignore, for reasons above) about what to do and how. So, we should follow them. However, we need more. Which is why I tout LXD Strategy! We need to account for complexity in learning design approaches.

For one, our design processes need to be iterative. We’ll make our best first guess, but it won’t be right, and we’ll need to tune. The incorporation of agile approaches, whether SAM or LLAMA or even just iterative ADDIE, reflects this. We need to evaluate and refine our designs to match the fact that our audience is more complex than we thought.

Our design also needs to think about the emotional experience as well as the cognitive experience. We want our design processes to systematically incorporate humor, safety, motivation, and more. Have we tuned the challenge enough, and how will we know? Have we appropriately incorporated story? Are our graphics aligned or adding to cognitive load? There are lots of elements that factor in.

Our design process has to accommodate SMEs who literally can’t access what they do. Also learner interests, not just knowledge. We need to know what interim deliverables, processes for evaluation, times when we shouldn’t be working solo, and tools we need. Most importantly, we have to do this in a practical way, under real-world resource constraints.

Which is why we need to address this strategically. Too many design processes are carry-over from industrial approaches: one person, one tool, and a waterfall process. We need to do better. There’s complexity in learning design, both on the part of our learners, and ourselves as designers. Leveraging what we know about cognitive science can provide us with structures and approaches that accommodate these factors. That’s only true, however, if we are aware and actively address it. I’m happy to help, but can only do so if you reach out. (You know how to find me. ;) Here’s to effective and engaging learning!

I’ve argued before about the need for evaluation in our work. This occurs summatively, where we’re looking beyond smile sheets to actually determine the impact of our efforts. However, it also should work formatively, where we’re seeing if we’re getting closer. Yet there are some ways in which we go off track. So I want to talk about iterating and evaluating our learning initiatives.

Let’s start by talking about our design processes. The 800 lb gorilla of ADDIE has shifted from a water flow model to a more iterative approach. Yet it still brings baggage. Of late, more agile and iterative approaches have emerged, not least Michael Allen’s SAM and Megan Torrance’s LLAMA. Agile approaches, where we’re exploring, make more sense when designing for people, with their inherent complexity.

Agile approaches work on the basis of creating, basically, Minimum Viable Products, and then iterating. We evaluate each iteration. That is, we check to see what need to be improved, and what is good enough. However, when are we done?

In my workshops, when talking about iteration, I like to ask the audience this question. Frequently, the answer is “when we run out of time and money”. That’s an understandable answer, but I maintain it’s the wrong answer.

If we iterate until we run out of time and money, we don’t know that we’ve actually met our goals. As I explained about social media metrics, but applies here too, you should be iterating until you achieve the metrics you’ve set. That means you know what you’re trying to do!

Which requires, of course, that you set metrics about what your solution should achieve. That could include usability and engagement (which come before and after, respectively), but most critically ‘impact’. Is this learning initiative solving the problem we’re designing it to achieve? Which also means you need to have a discussion of why you’re building it, and how you know it’s working.

Of course, if you’re running out of time and money faster than you’re getting close to your goal, you have to decide whether to relax your standards, or apply for more resources, or abandon your work, or…but at least you’re doing so consciously. Yet this is still better than heuristically determining that three iterations is arbitrarily appropriate, for example.

I do recognize that this isn’t our current situation, and changing it isn’t easy. We’re still asked to make slide decks look good, or create a course on X, etc. Ultimately, however, our professionalism will ask us to do better. Be ready. Eventually, your CFO should care about the return on your expenditures, and it’ll be nice to have a real answer. So, iterating and evaluating should be your long term approach. Right?