I’m obviously a fan of innovation, and experimentation is a big component of innovation. However, I fear I haven’t really talked about the specifics.  The details matter, because there are smart, and silly, ways to experiment. I thought I’d take a stab at laying out the specifics of experimentation.

First, you have to know what question you’re trying to answer. Should we use a comic or a video for this example?  Should we use the content management system or our portal tool to host our learning and performance support resources?  What’s the best mechanism for spacing out learning?

An important accompanying question is “how will we know what the answer is?”  What data will discriminate?  You need to be looking for a way to tell, we know, we can’t know, or we need to revise and do again.

Another way to think about this is: “what will we do differently if we find this?” and “what will we do differently if it turns out differently?” The point is to know not just what you’ll know, but  what it means.

You want to avoid random experimentation. There  are the ‘lets try it out’ pilots that are exploratory, but you still want to know what question your answering. Is it “what does it take to do VR” or “let’s try using our social media platform to ‘show our work'”.

Then you need to design the experiment. What’s the scope? How will you run it? How will you collect data? Who are your subjects?  How will you control for problems?

One of the claims has regularly been “don’t collect any data you don’t know what you’ll do with”.  These days, you can run exploratory data analysis, but still, accumulating unused data may be a mistake.

The after-experiment steps are also important. Major questions include: “what did we learn”, “do we trust the results”, and “what will we do as a result”. Then you can followup with the actions you determined up front that would be predicated on the outcomes you discover.

Experimentation is a necessary component of growth. You have to have a mindset that you learn from the experiment, regardless of outcome. You should have a budget for experimentation  and expect a degree of failure. It’s ok to lose, if you don’t lose the lesson!  And share your learnings so others don’t have to make the same experiment.  So experiment, just like I did here; is this helpful?  If not, what would it need to be useful?

Following on my thoughts on writing yesterday, this was a topic that didn’t fit (the post got too long ;).  So here we go..  Colleagues have written that citations are important. If you’re making a claim, you should be able to back it up. On the other hand, if you’re citing what you think is ‘received wisdom’, do you need to bother?  Pondering…

Now, citations can interfere with the flow, I believe. If not the reading, they can interfere with the flow of my writing! (And, I’ve been accused of ‘name dropping‘, where instead I believe it’s important to both acknowledge prior work and show that you know what’s been done.) Still, it’s important to know what to cite, and when.

I admit that I don’t always cite the claims I make. Because, I take it as a given.  I may say something like “we know” or otherwise presume that what I’m saying is accepted premise. One problem, of course, is that I don’t know what others know (and don’t). And, of course, that this isn’t an official article source, this is my blog ;). Still, when I’m talking about something new to me (like thoughts from books), I will cite the locus.

Articles are different. When I write those, I try to provide sources. In both cases I generally don’t go to the extent of journal article links, because I’m not expect that folks have easy access to them, and so prefer to cite more commonly available resources, like books that have ‘digested’ the research.

And when I write ‘take down’ articles, I don’t cite the offender. It’s to make the point, not shame anyone. If you’re really curious, I’m sure you can track it down.

And, realize I don’t have easy access to journals either. Not affiliated with an institution, I don’t have access to the original articles behind a pay wall. I tend to depend on people who summarize including books and articles that summarize. Still, I’ve a grounding for over a decade in the original materials and am able to make inferences. And of course occasionally I’ll be wrong. Sometimes, I’ll even admit it ;).

The issue really is when do you need to make a citation. And I reckon it’s when you’re stating something that folks might disagree with. And I can’t always anticipate it. So I’ll try to consistently point to the basis for any claims I think might be arguable, or state that it’s my (NSH :) opinion.  And you can always ask!  Fair enough?

Yet another silly post I stumbled upon.  And last week at a conf someone said they liked my take-downs. If you disagree, let me know, but otherwise here’s yet another bunch of marketing hype.  Hopefully no one uses this for any real decisions!

This one talks about ‘generation Z’, and implications for L&D. Ok, so we’re off on the wrong foot from the get-go.  These are listed as 1995-2014. (Er, um, as Jessica Kriegel pointed out last week, isn’t the whole point of the millennial label that they’re ‘2000’? )  However, there’s no evidence to point to reliable generational differences. What differences there are can be attributed to age, and it’s still a form of age discrimination, how about treating people by how they individually behave?

So there’s a list of differentiators, sourced from elsewhere. You go to the elsewhere, and it’s preferences, and anecdotal. Neither one are good bases for making broad claims. There are several cites in the list, as well. From marketing sites. So the author clearly doesn’t understand good data.  What are they talking about? Here’s a subset:

• Digital multitaskers: well, we know that’s inefficient, but haven’t we seen that taken up by device, not age group? It’s certainly true for millennials as well, and seems to be true for everyone who’s gotten on to mobile devices.
• Secretly social: (wth?) they share, but with control. As do most astute folks beyond high school.
• Diverse: er, yes, so’s the whole US. And, more and more, the world. How is this definitional? And do you think they really don’t still have biases?
• Quick Information Processors/Communicators:  dealing with chunks, quickly but not necessarily accurately. Isn’t that, er, just kind of human?

The recommendations list is similarly silly:

• Update job descriptions: make sure they’re up-to-date.  Really?  This isn’t just good practice?
• Expunge bias: ditto
• Go where the talent is: use appropriate social media. C’mon, already; any other statements of the obvious?
• Benefits: emphasize the WIIFM. Can you imagine?

The overarching theme here is ‘do good things’.  Why isn’t this appropriate for  every job search?  And the same thing continues when recommendations for your courses:

• Digital and Visual Content: Use media? Really?  Who’d have thought of it?
• Reassess your Library and Curricula: you don’t need diversity, but you do need soft skills. Here I think there is bad advice, instead of the generally ‘best principles argued for the wrong reasons’.  Just because you hear more messages of tolerance (yay!), doesn’t mean you know how to be inclusive, and are aware of unconscious bias. (That’s why it’s  unconscious!)

And the same overall pattern of good advice pretending to be specific to a generation holds true for the final list.   (I’m paraphrasing the advice here):

• Embrace diversity
• Provide social connection tools
• Give them the ability to contribute
• Include them
• Don’t try to ‘own’ their time

Tell me if you think any of these should be not true for other folks than these new folks?  I think this approach is a bad idea, overall. You’re providing decent advice (er,  mostly), but doing so through a myth-perpetuating framing. That’s still myth-perpetuating!

Ok, so this was from a company that’s trying to flog their services. It still seems like it’s written by a person more focused on marketing than matter. And I think we need to unpack these, and push back. Generation Z is just as discriminatory as millennials,  gender, and other differences that are attempts to avoid dealing with people as individuals.  If we don’t kick up our heels, we won’t get better efforts. And we should.

Many years ago, I met a guy who worked for Apple. They were allowed to have their own job titles, and his was “Intellectrician”. I thought that was a very nice turn of phrase. And, as I just ordered new business cards, I put  Intellectricity as the tagline instead of “Learning & Performance Strategy” or other permutations with Technology and such in the title.  Why?

The goal, of course, is to have a phrase that folks will read it and go “what’s that about?”, as some of my colleagues helped me remember. If you can spark a conversation, you have a chance to do a little evangelism/education. (And maybe some business interest?)  Also, I think it actually captures what I believe and like to do pretty well.

You’ve likely heard or read me harp many times on how companies aren’t well aligned with how we think, work, and learn. The cognitive violations are many, from how we design our learning, to design our workplaces, policies, tool use, and culture. If we redesigned what we’re doing, creating strategies to get better practices in place, we’d be unleashing the organizational intellect!  Hence, ‘intellectricity’.

And this is pretty much what I’m on about, in several ways:

• knowing what formal learning  really  looks like, and designing our design processes accordingly
• recognizing what facilitates informal learning in the short term (the ‘solve this’ type of problem-solving’)
• facilitating long-term informal learning by practices and tools suites
• fostering a culture where innovation thrives

This is a partial list that goes fractal really quickly with practices and principles around each area. The point is that these elements are key to organizational ‘thrival’.  Overall, they’re about optimizing the intellectual activity of the organization, learning quickly to be agile.

We’ll see if this tactic works to generate conversations and then new thinking. As Jay Cross used to say “conversations are the stem cells of learning”.  Practicing what we preach.  So here’s to Intellectricity: more conversations and more learning.

This may seem like an odd pairing, so bear with me.  I believe that we want to find ways to support organizations moving in the direction of innovation and learning cultures. Of course, I’ve been on a pretty continuous campaign for this, but I’m wondering what other levers we have. And, oddly, I think competencies may be one. Let me make the case for competencies and innovation.

So I’ve gotten involved in standards and competency work. Don’t ask me why, as I have no better answer than a) they asked, and b) the big ‘sucker’ tattoo on my forehead.  Of course, as I’ve said before, the folks that do this stuff (besides me, obviously) are really contributing to the benefit of our org. Maybe I felt I had to walk the talk?

In the course of the one that was just launched, we identified a number of competencies across the suite of L&D activities. This included (in addition the more traditional activities) looking at how to foster innovation. This means understanding culture and the change processes to get there, as well as knowing how to run meetings that get the best outputs. It’s about being prepared for both types of innovation, fast (solve ‘this’ problem) and slow (the steady percolation of ideas).

Thus, the necessary skills are identified as a component of a full suite of L&D capabilities. And the hope, of course, is that people will begin to recognize that there are parts of L&D they’re not addressing, and move to take on this opportunity. I hope that it’s becoming obvious that the ability to facilitate innovation is an organizational imperative, and that there’s a strong argument for L&D to be key. This is on principle, and pragmatically, it’s a no-brainer for L&D to find a way to become central to org success, not peripheral.

However, leaving that to chance would be, well, just silly. What can we do?  Well, two things, I think: one is to help raise awareness, the other is to provide support. A suite of skills aligned to this area is a ‘good thing’ if it known and used. Working on the know has been an ongoing thing (*cough*), but how can we support it?

Again, two things, I think. One are examples where people have put in place programs where they’ve oriented themselves in this direction and documented benefits. The other is to provide scaffolding; support materials that help folks implement these competencies. And I believe that’s coming.

“Systematic creativity is  not an oxymoron” (I may need to make a quip post about that). And this is an example. Think of brainstorming, for example. It can be useful, or  not. When done right, the outcomes are much better. And similarly in lots of ways, the nuances matter. If we define, through competencies, what suites of knowledge matter, we bring awareness to the possible outcomes. And the opportunity to improve them.

It may be an indirect path, to be sure, but it’s a steady, and real one. In fact, to say “we want to innovate, but how” and have a suite of specific sets of knowledge on tap to point people to, is pretty much next to the fastest path.  Showing people the benefits and the path to obtain them is key. It’s even self-referential: let’s innovate on making innovation systematically embedded in organizations! ;)  So, keep on experimenting!

In my post last week on engagement, I presented the alignment model from my  Engaging Learning  book on designing learning experiences. And as I thought about the post, I pondered several related things about labels, models, and drives. I thought I’d wrestle with them ‘out loud’ here, and troll (in the old sense) to see what you think.

Some folks have branded a model and lived on that for their career. And, in a number of cases, that’s not bad: they’re useful models and their applicability hasn’t diminished. And while, for instance, I think that alignment model is as useful as most models I’ve seen, I didn’t see any reason to tie my legacy to it, because the principles I like to comprehend and then apply to create solutions aren’t limited to just engagement. Though I wonder if people would find it easier to put the model in practice if it had a label.  The Quinn Engagement model or somesuch?

I’ve also created models around mobile, and about performance ecosystems, and more. I can’t say that they’re all original (e.g. the 4Cs of mobile), though I think they have utility. And some have labels (again, the 4Cs, Least Assistance Principle…) Then the misconceptions book is very useful, but the coverage there isn’t really mine, either. It’s just a useful compendium. I expect to keep creating models. But it’d led to another thought…

I’ve seen people driven to build companies. They just keep doing it, even if they’ve built one and sold it, they’re always on it; they’re serial entrepreneurs. I, for instance, have no desire to do that. There are elements to that that aren’t me.    Other folks are driven to do research: they have a knack for designing experiments that tease out the questions that drive them to find answers. And I’ve been good at that, but it’s not what makes my heart beat faster. I do  like action research, which is about doing with theory, and reflecting back. (I also like helping others become able to do this.)

What I’m about is understanding and applying cognitive science (in the broad sense) to help people do important things in ways that are enabled by new technologies.  Models that explain disparate domains are a hobby. I like finding ways to apply them to solve new problems in ways that are insightful but also pragmatic.   If I create models along the way (and I do), that’s a bonus. Maybe I should try to create a model about applying models or somesuch. But really, I like what I do.

The question I had though, is whether anyone’s categorized ‘drives’.  Some folks are clearly driven by money, some by physical challenges. Is there a characterization?  Not that there needs to be, but the above chain of thought led me to be curious. Is there a typology of drives? And, of course, I’m skeptical if there is one (or more), owing to the problems with, for instance, personality types and learning styles :D. Still, welcome any pointers.