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Experimentation specifics

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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?

Application-based Instruction

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A number of years ago, I wrote about activity-based learning.  And I firmly stand behind the model there. It’s not a major campaign, but quietly permeates the things that I do. However, I realize that there’re two misnomers in the label, and it’s time to rectify that. It’s about instruction, and it’s about application. I need to make those distinctions clear.

The original point was to find a way to make it hard for content (read: info dump and knowledge test) to be the basis of curricula. So, instead of a series of content, or knowledge, to be a curriculum,  a curriculum is a series of activities.

There are, of course, specific constraints around the activities, and I realize it’s about knowledge application. There are lots of activities that aren’t going to lead to meaningful learning. The key is retrieval, but I think what’s important is retrieval to do something. Hence application, applying knowledge to make decisions. It can be behavioral decisions or the decisions inherent in creating meaningful work product, but it’s about cognitive skills in context.

The focus on decisions is because I believe what will make the difference to organizations is making right, or better, decisions. Not, for instance, just knowing things. And part of the application core is about doing things.

I argue that learning is acton and reflection. And, consequently, instruction is designed action and guided reflection. Similarly, application is designed actions to require decisions. And this is about instruction. It’s not learning, though that’s the goal. But instruction is a probabilistic game; as I’ve paraphrased Dorothy Parker, you can lead learners to learning, but you can’t make them think.

‘Application-based instruction’ it is. It’s like problem-based learning, or case-based, but it’s instruction not learning, and it’s an umbrella for those and other such initiatives. The real question is whether this labeling makes important distinctions. I think it does, but I’m biased. What do you say?

Citations

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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?

Editing, process, topics, and other reflections

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My lass let me know there was a typo in my recent post on Transformation.  I’m thrilled that she’s reading them (!), but she triggered many thoughts about my writing approach. I thought I’d share how I deal with blogging, articles, and writing in general, as a ‘show your work‘ effort. And, in a sense, solicit your thoughts on approach, editing, and topics (amongst other things).

Process

It starts with my commitment to two blog posts a week. And I’m pretty sure I average that, since while I occasionally only get one, I also occasionally get three (say, during a week at a conference with mindmaps).  That means, however, that sometimes I’m brimming with ideas and have them queued up a week or two in advance, and sometimes I’m writing them at the last minute (*cough* this one *cough*).  When I know I’ll be on the road on a particular week, I definitely try to have them in the hopper in advance.

Regardless, I tend to write each in one fell swoop. Something sparks a thought, and I rush to get it down. Sometimes I’ll have an idea elsewhere, and jot myself a one line reminder, and need to generate the full prose. But my writing’s often like that: once I’m going, I have to let that full idea gestate. Even when writing a full book (as I’ve done a time or two ;), I outline it in a go, and then write sections in a burst.

Now, I write in several channels: my blog, my committed articles, and of course books. And, not surprisingly, I write them differently.  The blog comes out ‘as is’. I do reread it after it’s first done, typically, but as my lass discovered, it can have flaws. I reread my Trends article after posting, for instance, and noticed a couple of flaws. (I’ve fixed them, of course, similarly when folks comment in one way or another about something I’ve left confusing or wrong.)

My articles are different. I write them typically in one go, but I always hang on to them for at least a day, and reread with fresh eyes. I think that’s obligatory for such efforts. In one case, I have an editor who reads them with a careful eye, and always sends back a revised version. I don’t get to see the revisions (which is frustrating), but the articles are always improved. Editing is valuable!

For books, as I mentioned, I outline it, then write sections. And, depending on the book, the experience changes. With Engaging Learning, it had been percolating for so long it kind of flew out of my fingers onto the page.  For Designing mLearning, it was different; I outlined, and wrote, and as I got further in I found myself rearranging the structure and going back to add things.  The Revolutionize L&D book was closer to the Designing mLearning book, with two changes. I didn’t reorganize as much, but I kept going back and adding stuff. It was hard to finish!

With my books, I’ve always had an editor. The ones from the publisher varied in quality (good experiences generally), but I also have m’lady serve as my first (and best) editor. And I’ve learned to truly value an editor. The benefit of a second eye without the assumptions and blinders the writer brings is great!

Topics

The ideas come differently as well. My blog tends to get whatever I’m thinking about (like this). My articles tend to be a deeper dive into whatever I think (or we agree, with my editor) is important. I keep a list of potential topics for each, and take whatever feels ‘right’ for the month.

Books, of course, are a bigger story. For one, you need a publisher’s agreement (unless you self-publish). My first book was based upon my research for years on games and engagement. The mLearning books were publisher requests, and yet I had to believe I could do a proper job. Revolutionize emerged from my work with people and orgs and looking at the industry as a whole, and was something I think needed to be said. My latest, on myths, was also requested, but also something I felt comfortable doing (and needed to be done).

(Interestingly, on the requested books, I first checked to see if someone else might write it instead, but when the obvious candidates declined, I was happy to step up. I got their voices in anyway. ;)

The hard part, sometimes, is coming up with topics. The commitment to two posts a week is a great catalyst for thinking, but sometimes I feel bereft. I welcome suggestions for topics for any of the above as well. Someone asked what my next book would be, and I asked them what they thought it should be.  However, I’m not ready to write a memoir yet; I’m not done!  Thoughts solicited on any or all of the above.

Trends for 2019?

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It’s already started!  Like Christmas (which morally shouldn’t be even be thought about before Thanksgiving), requests for next year’s trends should be on hold until at least December.  Still, a request came in for my thoughts. Rather than send them off and await their emergence; I toss them out here, with a caveat: “It’s tough to make predictions, particularly about the future.”

1. What, on your opinion, are the main Digital Learning (DL) trends for 2019?

I think the main trend will be an increasing exploration of alternatives to ‘courses’.  This will include performance support, and social networks. Similarly, models for formal learning will shift from the ‘event’ model to a more sustained and distributed framework that segues from spaced learning through coaching.

I sincerely hope that we’ll be paying more attention to aligning learning with cognition, and pursue ‘shiny objects‘ only after we establish a solid foundation. Instead of looking for the magic bullet, we’ll recognize that our brain architecture means we need a drip-irrigation model, not a flood.

This may be wishful thinking, but I believe we’re beginning to see some positive signs. We’re seeing more interest in learning science, growing awareness of myths, and more. Hopefully there’s an accompanying shift from being fascinated by technology to being interested in what technology can do for better learning outcomes!

2. What are the main threats and obstacles, then?

The main threats and obstacles are several. For one, our own lack of understanding of the foundations of our industry hampers us. When we don’t really understand learning, we can be swayed by well-designed distractors.  That’s the second factor: there are those who are happy selling us the latest fad.

Coupled with this is a lack of business awareness in our own practices. We measure the wrong things, e.g. efficiency – such as cost/seat/hour. And we’re reluctant to talk to the stakeholders in the business. We should be worried about impact: are we reducing costs, increasing profitability or customer satisfaction?

Overall, we’re hampered by a true lack of professionalism. We learn the tools, and crank stuff out, but we’re not concerned enough about whether it’s demonstrably the right stuff.

3. Do you believe in the AI and DL robotization? When does this bright future come?

I believe in increasing use of AI to support functions that shouldn’t involve humans. It’s silly to have people doing rote things we can teach computers to do. That includes responding to knowledge requests, and filtering, and a few other tasks.  However, I think we need to recognize that not all the things needed in learning, such as evaluating complex work products, should be done by machines. I think we should look for when we can automate, and when we want people in the loop.

So I’m more interested in IA (not AI): Intelligence Augmentation.  That is, what is the right distribution of tasks between machine and people?  There are things that computers do well, but they’re remarkably brittle; as of yet they don’t handle edge cases, or make good inferences in the grey areas very well. That’s when you want people. I think our design discipline needs to be smart about when to use each, and how they complement each other.

The future of IA is already underway, as is AI. We’re seeing, and will see more, uses of AI to filter, to answer questions, and to take over rote tasks.  These behaviors are not yet ready to be termed ‘bright’, however. Some success stories are emerging, but I suspect we don’t hear much yet about the money being wasted.  The time of consistency in effective synergies is still a few years off.

4. Your advice to the market for 2019.

Work smarter!  Get smart about learning science, about business, and about what technology can (and can’t) do. I’d like to see: staff pushing more for real impact via metrics, leaders asking for business cases not order taking, vendors pushing solutions not resource savings, and buyers asking for real evidence.  I’d like to see smarter purchasing, and the snake oil sales folks’ business withering away.  We can do better!

And I realize that my proposed trends are more wishful thinking than predictions. One of my favorite quotes is by Alan Kay: “The best way to predict the future is to invent it” so I keep pushing this agenda. My goal is simple: to make this a field I’m truly proud to be working in. The folks in L&D, I think, are some of the nicest folks; they’re here because they want to help others (you don’t go to L&D to become rich ;). I think there’s a promising future, but it doesn’t start with AI or ML or DL, it starts with getting down to the realities of how we learn, and how we can support it. When we do that, I think our future will be one which will help our organizations and our people thrive. Our future can be bright, and it’s up to us to make it so.

Transformation!

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butterfly cocoonI’m a fan of the notion of ‘learning experience design’ (not so sure about platforms; I need to investigate them more ;).  The idea of integrating effective education and engaging experiences is something I’ve been on about for a very long time. And I want to push it a little further. I want to talk about transformation.

What am I talking about? So, I’ve previously referred to Pine & Gilmore’s concept of the Transformation Economy. That is, going beyond experiences (e.g. themed restaurants) to ones that change us. And I argue that’s what we do; we create (or should) experiences that give us new skills, new abilities to do.  But I want to push it further.

Here I’m talking about deliberately using the idea of transformation as a learning design goal. Not just change, but leveraging the emotions as well as cognition to have the learner not just feel empowered, but transformed!  This may sound like a lofty goal, fine for a TED Talk (just read the book; recommended), but is it practical for elearning?  Well, that’s an interesting question.

Let me spin it another way: I do not think we should be shooting for an information dump and knowledge test. For two reasons: one is that it’s not inspiring. More importantly, however, it also isn’t effective. You end up with what cognitive scientists call ‘inert knowledge’. You’ll learn it and pass a test on it, but when it’s relevant in practice it won’t even get activated!  Because you’ve never used it in ways like you practice.

I think if we are actively thinking about transformation as a goal, we might do a better job of thinking about the necessary practice and the emotional engagement.  We can focus on thinking “what will lead to the transformation we want”, and “how do we make people want it and celebrate when they’ve made the breakthrough?”  And I think this is a useful perspective.

Even for things like compliance, I’d suggest that we should be having visceral reactions like “Ok, I get it <bad behavior> is pretty heinous”, and “safety is important, and I commit to following these rules”.  For more important things, you’d like them to feel “yes, I see, this will change how I do this!”

Yes, it’s ambitious. But why set ourselves limited goals? When I was teaching interface design, I maintained that if I accommodated the engineers lack of background in Psych, I’d get them only so far. If I pushed them, they’d end up farther than if I was conciliatory. Similarly, here, I think we’ll do a better job if we think ‘ambitious’, and end up not as far as we’d like. I’ll suggest that’s better than satisfactorily achieving mediocrity. Most importantly, I truly think we’ll do a better job of design if we strive for transformation.

And, if there’s nothing transformative about what we’re covering, should we really be using our resources?  Let me put it another way: why shouldn’t we do this? Seriously, I’m asking.  So, what’s your answer?

 

Content Confusion

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I read, again and again, about the importance of ‘content’ in learning. And I don’t disagree, but…I think there’s still a problem about it. And where I get concerned is about what is meant by the term.  Just what do mean by ‘content’?  And why should we care about the distinction?

My worry is twofold. For one, I get concerned that talking about content foregrounds ‘information’. And that’s a problem. I’ve been concerned for a while about how it’s too easy to allow knowledge to dominate learning objectives. Know, understand, etc are generally not meaningful objectives. Objectives should be ‘able to use to ___’.  Talking about content, as I’ve talked about before, leads us down a slippery slope to curriculum being defined as content.

My second concern is related. It’s about content being meant to include concepts, examples, and practice. Yet, if we don’t separate out interactivity separate from consumption, we can make nonsensical learning interactions instead of meaningful applications of concepts to contexts. Recognition is not powerful learning.

Look, I get it.  From a technical point of view, e.g. a content management system perspective, it’s all content. It’s addressable files. They may just report access, or they can report success/failure, or many other things. However, again, this view can make it easy to do bad things. And, as the book Nudge I just read suggests, we want to make it easy to do the right things, and make you have to work to do things inappropriately.

So I may be being a pedant about this, but I have a reason. It won’t be when we’re all on the same page about good learning design, practice foregrounded and concepts and examples as learning resources, not the goal. But I don’t think we’re there yet.  And language matters in shaping thinking. It may not be the Whorfian Hypothesis, but it does influence how we think and what we do.

For principled and practical reasons, I think we want to distinguish between content (concepts and examples) and interactives (practice).  At least as designers. Others can focus differently, but we have our own language for other things (I’d argue our use of the term ‘objectives’ is different than business folks, for example), and I argue we should do so here as well.  What say you?

Developing learning to learn skills

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I’m an advocate of meta-learning, that is: learning to learn. Not just because it’s personally empowering, but because it can and should be organizationally empowering. The problem is, little is talked about how to develop it. And I have to say that what I do see, seems inadequate. So I thought I’d rant, for a post, on what is involved in developing learning to learn skills.

First, of course, you have to identify what they are!  What are learning to learn skills?  Harold Jarche’s PKM is a good start, talking about seek > sense > share. Obviously, there’s more to it than just that, so it’s about seeking actively but also setting up systems to continually feed you new, potentially tangential thoughts. And how to evaluate what you get. Then, it’s about being able to process the inputs in ways that help you understand, or do, something new. What does it mean, in practice?  Finally, of course, it’s about sharing, in two ways. For one, contributing to others’ questions and work. Then it’s also sharing your own thoughts and work.

That’s (largely) working alone, but there are also specifics about how you work and play well with others. Do you know how to best manage the process of solving a problem together?  How can you ask questions, and answer them, in ways that people will recognize and participate?   People need models and frameworks that guide performance.

Of course, just knowing this isn’t enough.  There are some necessary additional steps. The first is evangelizing and sharing the best principles for working together. So, people have to know about the principles, and be encouraged to use them.  And even be rewarded, whether just with praise or actual promotion of their successes. There should also be models, examples. So L&D should be practicing what they preach, and working and learning ‘out loud’.  Show, and narrate, your own work!  And, this is still not enough.

Most importantly, you have to develop the skills. Actively. So, content about them, and examples are good. But learning is, at core, about mentored practice.  And it can’t be in the abstract, it’s about doing it with real tasks. You can set up such opportunities in your formal learning (and should), but you should also be coaching around real work.

At least, you should be facilitating proper approaches in public forums, like social media.  You can quietly coach individuals about good practices if they’re off target.  You can point out, as a meta-discussion, when people are learning effectively.  Annotate the thinking behind what learners can and should be doing.

The worst thing is to leave it to chance, or assume your learners are effective self-learners. The evidence is that they’re not. Sadly, our education system doesn’t do a good job of this. Nor do our organizations. But we could. This is about more effective innovation, really. Learning manifests as new ways of doing things. Innovation is about better ways of doing things. If we evaluate our learnings and apply the ones that are improvements, we’re innovating. Both for specific needs and as a ongoing background process.  And if indeed innovation is the only sustainable differentiator, this is the best investment you can make for the organization.

And, if you’re truly contributing to the central success factor in the organization, you’re becoming essential to the organization. As you should be. So seize the opportunity, and make meta-learning a priority. Develop learning to learn skills consciously, and conscientiously.  It’s an innovative, and valuable, thing to do :).

 

Making Multiple Choice work

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For sins in my past, I’ve been thinking about assessments a bit lately. And one of the biggest problems comes from trying to find solutions that are meaningful yet easy to implement. You can ask learners to develop meaningful artifacts, but getting them assessed at scale is problematic. Mostly, auto-marked stuff is used to do trivial knowledge checks. Can we do better.

To be fair, there are more and more approaches (largely machine-learning powered), that can do a good job of assessing complex artifacts, e.g. writing. If you can create good examples, they can do a decent job of learning to evaluate how well a learner has approximated it. However, those tools aren’t ubiquitous. What is are the typical variations on multiple choice: drag and drop, image clicks, etc. The question is, can we use these to do good things?

I want to say yes. But you have to be thinking in a different way than typical. You can’t be thinking about testing knowledge recognition. That’s not as useful a task as knowledge retrieval. You don’t want learners to just have to discriminate a term, you want them to use the knowledge to do something. How do we do that?

In Engaging Learning, amongst other things I talked about ‘mini-scenarios’. These include a story setting and a required decision, but they’re singular, e.g. they don’t get tied to subsequent decisions. And this is just a better form of multiple choice!

So, for example, instead of asking whether an examination requires an initial screening, you might put the learner in the role of someone performing an examination, and have alternative choices of action like beginning the examination, conducting an initial screening, or reviewing case history. The point is that the learner is making choices like the ones they’ll be making in real practice!

Note that the alternatives aren’t random; but instead represent ways in which learners reliably go wrong. You want to trap those mistakes in the learning situation, and address them before they matter! Thus, you’re not recognizing whether it’s right or not, you’re using that information to discriminate between actions that you’d take.  It may be a slight revision, but it’s important.

Further, you have the consequences of the choice play out: “your examination results were skewed because…and this caused X”.  Then you can give the principled feedback (based upon the model).

There are, also, the known obvious things to do. That is, don’t have any ‘none of the above’ or ‘all of the above’. Don’t make the alternatives obviously wrong. And, as Donald Clark summarizes, have two alternatives, not three. But the important thing, to me, is to have different choices based upon using the information to make decisions, not just recognizing the information amongst distractors. And capturing misconceptions.

These can be linked into ‘linear’ scenarios (where the consequences make everything right so you can continue in a narratively coherent progression) or branching, where decisions take you to different new decisions dependent on your choice.  Linear and branching scenarios are powerful learning. They’re just not always necessary or feasible.

And I certainly would agree that we’d like to do better: link decisions and complex work products together into series of narratively contextualized settings, combining the important types of decisions that naturally occur (ala Schank’s Goal Based Scenarios and Story-Centered Curriculum and other similar approaches).  And we’re getting tools that make this possible. But that requires some new thinking. This is an interim step that, if you get your mind around it, sets you up to start wanting more.

Note that the thinking here also covers a variety of interaction possibilities, again drag’n’drop, image links, etc. It’s a shift in thinking, but a valuable one. I encourage you to get your mind around it. Better practice, after all, is better learning.

And the myths go on

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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.