Learnlets
Clark Quinn's Learnings about Learning
(The Official Quinnovation blog)

14 June 2017

Tech and School Problems

Clark @ 8:05 am

After yesterday’s rant about problems in local schools, I was presented with a recent New York Times article. In it, they talked about how the tech industry was getting involved in schools. And while the initiatives seem largely well-intentioned, they’re off target.   There’s a lack of awareness of what meaningful learning is, and what meaningful outcomes could and should be.  And so it’s time to shed a little clarity.

Tech in schools is nothing new, from the early days of Apple and Microsoft vying to provide school computers and getting a leg up on learners’ future tech choices.  Now, however, the big providers have even more relative leverage. School funds continue to be cut, and the size of the tech companies has grown relative to society. So there’s a lot of potential leverage.

One of the claims in the article is that the tech companies are able to do what they want, and this is a concern. They can dangle dollars and technology as bait and get approval to do some interesting and challenging things.

However, some of the approaches have issues beyond the political:

One approach is to teach computer science to every student.  The question is: is this worth it?  Understanding what computers do well (and easily), and perhaps more importantly what they don’t, is necessary, no argument. The argument for computer programming is that it teaches you to break down problems and design solutions. But is computer science necessary?  Could it be done with, say, design thinking?  Again, all for helping learners acquire good problem-solving skills.  But I’m not convinced that this is necessarily a good idea (as beneficial as it is to the tech industry ;).

Another initiative is using algorithms, rules like the ones that Facebook uses to choose what ads to show you, to sequence math.  A program, ALEKS, already did this, but this one mixes in gamification. And I think it’s patching a bad solution. For one, it appears to be using the existing curriculum, which is broken (too much rote abilities, too little transferable skills).  And gamification?  Can’t we, please, try to make math intrinsically interesting by making it useful?  Abstract problems don’t help. Drilling key skills is good, but there are nuances in the details.

A second approach has students choosing the problems they work on, and teachers being facilitators.  Of course, I’m a fan of this; I’ve advocated for gradually handing off control of learning to learners, to facilitate their development of self-learning. And in a recently-misrepresented announcement, Finland is moving to topics with interleaved skills rapped around them (e.g. not one curricula, but you might intersect math and chemistry in studying ecosystems. However, this takes teachers with skills across both domains, and the ability to facilitate discussion around projects.  That’s a big ask, and has been a barrier to many worthwhile initiatives.   Compounding this is that the end of a unit is assessed by a 10-point multiple choice question.  I worry about the design of those assessments.

I’m all for school reform. As Mark Warschauer put it, the only things wrong with American education is the curriculum, the pedagogy, and the way we use technology.  I think the pedagogy being funded in the latter description is a good approach, but there are details that need to be worked out to make it a scalable success.  And while problem-solving is a good curricular goal, we need to be thoughtful about how we build it in. Further, motivation is an important component about learning, but intrinsic or extrinsic?

We really could stand to have a deeper debate about learning and how technology can facilitate it. The question is: how do we make that happen?

6 June 2017

Evil design?

Clark @ 8:03 am

This is a rant, but it’s coupled with lessons. 

I’ve been away, and one side effect was a lack of internet bandwidth at the residence.  In the first day I’d used up a fifth of the allocation for the whole time (> 5 days)!  So, I determined to do all I could to cut my internet usage while away from the office.  The consequences of that have been heinous, and on the principle of “it’s ok to lose, but don’t lose the lesson”, I want to share what I learned.  I don’t think it was evil, but it well could’ve been, and in other instances it might be.

So, to start, I’m an Apple fan.  It started when I followed the developments at Xerox with SmallTalk and the Alto as an outgrowth of Alan Kay‘s Dynabook work. Then the Apple Lisa was announced, and I knew this was the path I was interested in. I did my graduate study in a lab that was focused on usability, and my advisor was consulting to Apple, so when the Mac came out I finally justified a computer to write my PhD thesis on. And over the years, while they’ve made mistakes (canceling HyperCard), I’ve enjoyed their focus on making me more productive. So when I say that they’ve driven me to almost homicidal fury, I want you to understand how extreme that is!

I’d turned on iCloud, Apple’s cloud-based storage.  Innocently, I’d ticked the ‘desktop/documents’ syncing (don’t).  Now, with every other such system that I know of, it’s stored locally *and* duplicated on the cloud.  That is, it’s a backup. That was my mental model.  And that model was reinforced: I’d been able to access my files even when offline.  So, worried about the bandwidth of syncing to the cloud, I turned it off.

When I did, there was a warning that said something to the effect of: “you’ll lose your desktop/documents”.  And, I admit, I didn’t interpret that literally (see: model, above).  I figured it would disconnect their syncing. Or I’d lose the cloud version. Because, who would actually steal the files from your hard drive, right?

Well, Apple DID!  Gone. With an option to have them transferred, but….

I turned it back on, but didn’t want to not have internet, so I turned it off again but ticked the box that said to copy the files to my hard drive. COPY BACK MY OWN @##$%^& FILES!  (See fury, above.)  Of course, it started, and then said “finishing”.  For 5 days!  And I could see that my files weren’t coming back in any meaningful rate. But there was work to do!

The support guy I reached had some suggestion that really didn’t work. I did try to drag my entire documents folder from the iCloud drive to my hard drive, but it said it was making the estimate of how long, and hung on that for a day and a half.  Not helpful.

In meantime, I started copying over the files I needed to do work. And continuing to generate the new ones that reflected what I was working on.  Which meant that the folders in the cloud, and the ones on my hard drive that I had copied over, weren’t in sync any longer.  And I have a lot of folders in my documents folder.  Writing, diagrams, client files, lots of important information!

I admit I made some decisions in my panic that weren’t optimal.  However, after returning I called Apple again, and they admitted that I’d have to manually copy stuff back.  This has taken hours of my time, and hours yet to go!

Lessons learned

So, there are several learnings from this.  First, this is bad design. It’s frankly evil to take someone’s hard drive files after making it easy to establish the initial relationship.  Now, I don’t think Apple’s intention was to hurt me this way, they just made a bad decision (I hope; an argument could be made that this was of the “lock them in and then jack them up” variety, but that’s contrary to most of their policies so I discount it).  Others, however, do make these decisions (e.g. providers of internet and cable from whom you can only get a 1 or 2 year price which will then ramp up  and unless you remember to check/change, you’ll end up paying them more than you should until you get around to noticing and doing something about it).  Caveat emptor.

Second, models are important and can be used for or against you. We do create models about how things work and use evidence to convince ourselves of their validity (with a bit of confirmation bias). The learning lesson is to provide good models.  The warning is to check your models when there’s a financial stake that could take advantage of them for someone else’s gain!

And the importance of models for working and performing is clear. Helping people get good models is an important boost to successful performance!  They’re not necessarily easy to find (experts don’t have access to 70% of what they do), but there are ways to develop them, and you’ll be improving your outcomes if you do.

Finally, until Apple changes their policy, if you’re a Mac and iCloud user I strongly recommend you avoid the iCloud option to include Desktop and Documents in the cloud unless you can guarantee that you won’t have a bandwidth blockage.  I like the idea of backing my documents to the cloud, but not when I can’t turn it off without losing files. It’s a bad policy that has unexpected consequences to user expectations, and frankly violates my rights to my data.

We now return you to our regularly scheduled blog topics.

 

23 May 2017

Some new elearning companies ;)

Clark @ 8:03 am

As I continue to track what’s happening, I get the opportunity to review a wide number of products and services. While tracking them all would be a full-time job, occasionally some offer new ideas.  Here’s a collection of those that have piqued my interest of late:

Sisters eLearning: these folks are taking a kinder, gentler approach to their products and marketing their services.  Their signature offering is a suite of templates for your elearning featuring cooperative play.  Their approach in their custom development is quiet and classy. This is reflected in the way they promote themselves at conferences: they all wear mauve polos and sing beautiful a capella.  Instead of giveaways, they quietly provide free home-baked mini-muffins for all.

Yalms: these folks are offering the ‘post-LMS’. It’s not an LMS, and instead offers course management, hosting, and tracking.  It addresses compliance, and checks a whole suite of boxes such as media portals, social, and many non-LMS things including xAPI. Don’t confuse them with an LMS; they’re beyond that!

MicroBrain: this company has developed a system that makes it easy to take your existing courses and chunk them up into little bits. Then it pushes them out on a schedule. It’s a serendipity model, where there’s a chance it just might be the right bit at the right time, which is certainly better than your existing elearning. Most importantly, it’s mobile!

OffDevPeeps: these folks a full suite of technology development services including mobile, AR, VR, micro, macro, long, short, and anything else you want, all done at a competitive cost. If you are focused on the ‘fast’ and ‘cheap’ side of the trilogy, these are the folks to talk to. Coming soon to an inbox near you!

DanceDanceLearn: provides a completely unique offering. They have developed an authoring tool that makes it easy for you to animate dancers moving in precise formations that spell out content. They also have a synchronized swimming version.  Your content can be even more engaging!

There, I hope you’ll find these of interest, and consider checking them out.

Any relation between the companies portrayed and real entities is purely coincidental.  #couldntstopmyself #allinfun

18 May 2017

Disruptive Innovation

Clark @ 8:06 am

I recently came across a document (PDF) about disruptive innovation based upon Clayton Christensen’s models, which I’d heard about but hadn’t really penetrated. This one was presented around higher education innovation (a topic I’ve some familiarity with ;), so it provided a good basis for me to explore the story.  It had some interesting features that are worth portraying, and then some implications for my thoughts on innovation, so I thought I’d share.

The model’s premise is that disruption requires two major things: a technology enabler and a business model innovation.  That is, there has to be a way to deliver this new advance, and it has to be coupled with a way to capitalize on the benefits.  It can’t just be a new technology in an existing business model, as that’s merely the traditional competitive innovation. Similarly, a new business model around existing technology is still within  competitive advancement.

A related requirement is to have a new entity ready to capitalize. This quote captured me: “In those few instances in which the leader in one generation became the leader in the next disruptive one, the company did so by setting up a completely autonomous business unit…”  You can’t do disruption from inside the game.  Even if you’re a player, you have to liberate resources to start anew.

Which is quite different than most innovation. Typical innovation is ‘within the box’.  This comes from having an environment where people can experiment, share, be exposed to new ideas, and allowing it to incubate (ferment/percolate) over time.  And this is a good thing. Disruptive innovation makes new industries, new companies, etc.  And that’s also good (except, perhaps, for the disrupted).  The point being that both innovations are valuable, but different.

It’s not clear to me what happens when an internal innovation comes up with an idea that’s really disruptive. Clearly, if the idea clears the hurdles of complacency and inertia, you’d probably want to spin it off.  But most innovations just need a fair airing and trialing to get traction (though depending on scope, a bit of change management might be useful).

I encourage innovation, and creating the environment where it can happen. It’s valuable even in established businesses, and a fair bit is known about how to create an environment where it can flourish.  So, what can we innovate about innovation?

3 May 2017

To LMS or not to LMS

Clark @ 8:11 am

A colleague recently asked (in general, not me specifically) whether there’s a role for LMS functions. Her query was about the value of having a place to see (recommended) courses, to track your development, etc. And that led me to ponder, and here’s my thinking:

My question is where to draw the line. Should you do social learning in the LMS version of that, or have a separate system? If using the LMS for social around courses (a good thing), how do you handle the handoff to the social tool used for teams and communities?  It would seem to make sense to use the regular tool in the courses as well, to make it part of the habit.

Similarly, should you host non-course resources in the LMS or out in a portal (which is employee-focused, not siloed)? Maybe the courses also make more sense in the portal, tracked with xAPI?  I think I’d like to track self-learning, via accessing videos and documents the same as I would formal learning with courses: I want to be able to correlate them with business to test the outputs of experiments in changes.

Again, how should I be handling signups for things?  I handle signups for all sorts of things via tools like Eventbrite.  Is asking to signup for a training, with a waiting list, different than other events such as a team party?

Now, for representing your learning, is that an LMS role, or an LRS dashboard, or…?  From a broader perspective, is it talent management or performance management or…?

I’m not saying an LMS doesn’t make sense, but it seems like it’s a minor tool at best, not the central organizing function.  I get that it’s not a learning management system, but a course management system, but is that the right metaphor?  Do we want a learning tracking system instead, and is that what an LMS if or could be for?

When we start making a continuum between formal and informal learning, what’s the right suite of tools? I want to find courses and other things through a federated search of *all* resources. And I want to track many things besides course completions, because those courses should have real world-related assignments, so they’re tracked as work, not learning. Or both. And I want to track things that we’re developing through coaching, or continuing development through coaching and stretch assignments. Is that an LMS, or…?

I have no agenda to put the LMS out of business, as long as it makes sense in modern workplace learning. However, we want to use the right tool for the right job, and create an ecosystem that supports us doing the right thing.  I don’t have an obvious answer, I’m just trying on a rethink (yes, thinking out loud ;), and wondering what your thoughts are.  So, what is the right way to think about this? Do you see a uniquely valuable aggregation of services that makes sense? (And I may have to dig in deeper and think about the essential components and map them out, then we can determine what the right suites of functions are to fulfill those needs.)

26 April 2017

Human Learning is Not About to Change Forever

Clark @ 8:09 am

In my inbox was an announcement about a new white paper with the intriguing title Human Learning is About to Change Forever. So naturally I gave up my personal details to download a copy.  There are nine claims in the paper, from the obvious to the ridiculous. So I thought I’d have some fun.

First, let’s get clear.  Our learning runs on our brain, our wetware. And that’s not changing in any fundamental way in the near future. As a famous article once had it: phenotypic plasticity triumphs over genotypic plasticity (in short, our human advantage has gained  via our ability to adapt individually and learn from each other, not through species evolution).   The latter takes a long time!

And as a starting premise, the “about to” bit implies these things are around the corner, so that’s going to be a bit of my critique. But nowhere near all of it.  So here’s a digest of the nine claims and my comments:

  1. Enhanced reality tools will transform the learning environment. Well, these tools will certainly augment the learning environment (pun intended :). There’s evidence that VR leads to better learning outcomes, and I have high hopes for AR, too. Though is that a really fundamental transition? We’ve had VR and virtual worlds for over a decade at least.  And is VR a evolutionary or revolutionary change from simulations? Then they go on to talk about performance support. Is that transforming learning? I’m on record saying contextualized learning (e.g. AR) is the real opportunity to do something interesting, and I’ll buy it, but we’re a long way away. I’m all for AR and VR, but saying that it puts learning in the hands of the students is a design issue, not a technology issue.
  2. People will learn collaboratively, no matter where they are.  Um, yes, and…?  They’re already doing this, and we’ve been social learners for as long as we’ve existed. The possibilities in virtual worlds to collaboratively create in 3D I still think is potentially cool, but even as the technology limitations come down, the cognitive limitations remain. I’m big on social learning, but mediating it through technology strikes me as just a natural step, not transformation.
  3. AI will banish intellectual tedium. Everything is awesome. Now we’re getting a wee bit hypish. The fact that software can parse text and create questions is pretty impressive. And questions about semantic knowledge isn’t going to transform education. Whether the questions are developed by hand, or by machine, questions are not intrinsically interesting. And AI is not yet to the level (nor will it be soon) where it can take content and create compelling activities that will drive learners to apply knowledge and make it meaningful.
  4. We will maximize our mental potential with wearables and neural implants. Ok, now we’re getting confused and a wee bit silly. Wearables are cool, and in cases where they can sense things about you and the world means they can start doing some very interesting AR. But transformative? This still seems like a push.  And neural implants?  I don’t like surgery, and messing with my nervous system when you still don’t really understand it? No thanks. There’s a lot more to it than managing to control firing to control limbs. The issue is semantics: if we’re not getting meaning, it’s not really fundamental. And given that our conscious representations are scattered across our cortex in rich patterns, this just isn’t happening soon (nor do I want that much connection; I don’t trust them not to ‘muck about’).
  5. Learning will be radically personalized.  Don’t you just love the use of superlatives? This is in the realm of plausible, but as I mentioned before, it’s not worth it until we’re doing it on top of good design.  Again, putting together wearables (read: context sensing) and personalization will lead to the ability to do transformative AR, but we’ll need a new design approach, more advanced sensors, and a lot more backend architecture and semantic work than we’re yet ready to apply.
  6. Grades and brand-name schools won’t matter for employment.  Sure, that MIT degree is worthless! Ok, so there’s some movement this way.  That will actually be a nice state of affairs. It’d be good if we started focusing on competencies, and build new brand names around real enablement. I’m not optimistic about the prospects, however. Look at how hard it is to change K12 education (the gap between what’s known and what’s practiced hasn’t significantly diminished in the past decades). Market forces may change it, but the brand names will adapt too, once it becomes an economic necessity.
  7. Supplements will improve our mental performance.  Drink this and you’ll fly! Yeah, or crash. There are ways I want to play with my brain chemistry, and ways I don’t. As an adult!  I really don’t want us playing with children, risking potential long-term damage, until we have a solid basis.  We’ve had chemicals support performance for a while (see military use), but we’re still in the infancy, and here I’m not sure our experiments with neurochemicals can surpass what evolution has given us, at least not without some pretty solid understanding.  This seems like long-term research, not near-term plausibility.
  8. Gene editing will give us better brains.  It’s alive!  Yes, Frankenstein’s monster comes to mind here. I do believe it’s possible that we’ll be able to outdo evolution eventually, but I reckon there’s still not everything known about the human genome or the human brain. This similarly strikes me as a valuable long term research area, but in the short term there are so many interesting gene interactions we don’t yet understand, I’d hate to risk the possible side-effects.
  9. We won’t have to learn: we’ll upload and download knowledge. Yeah, it’ll be great!  See my comments above on neural implants: this isn’t yet ready for primetime.  More importantly, this is supremely dangerous. Do I trust what you say you’re making available for download?  Certainly not the case now with many things, including advertisements. Think about downloading to your computer: not just spam ads, but viruses and malware.  No thank you!  Not that I think it’s close, but I’m not convinced we can ‘upgrade our operating system’ anyway. Given the way that our knowledge is distributed, the notion of changing it with anything less than practice seems implausible.

Overall, this is reads like more a sci-fi fan’s dreams than a realistic assessment of what we should be preparing for.  No, human learning isn’t going to change forever.  The ways we learn, e.g. the tools we learn with are changing, and we’re rediscovering how we really learn.

There are better guides available to what’s coming in the near term that we should prepare for.  Again, we need to focus on good learning design, and leveraging technology in ways that align with how our brains work, not trying to meld the two.  So, there’re my opinions, I welcome yours.

19 April 2017

Top 10 Tools for @C4LPT 2017

Clark @ 8:06 am

Jane Hart is running her annual Top 100 Tools for Learning poll (you can vote too), and here’s my contribution for this year.  These are my personal learning tools, and are ordered according to Harold Jarche’s Seek-Sense-Share models, as ways to find answers, to process them, and to share for feedback:

  1. Google Search is my go-to tool when I come across something I haven’t heard of. I typically will choose the Wikipedia link if there is one, but also will typically open several other links and peruse across them to generate a broader perspective.
  2. I use GoodReader on my iPad to read PDFs and mark up journal submissions.  It’s handy for reading when I travel.
  3. Twitter is one of several ways I keep track of what people are thinking about and looking at. I need to trim my list again, as it’s gotten pretty long, but I keep reminding myself it’s drinking from the firehose, not full consumption!  Of course, I share things there too.
  4. LinkedIn is another tool I use to see what’s happening (and occasionally engage in). I have a group for the Revolution, which largely is me posting things but I do try to stir up conversations.  I also see and occasionally comment on posting by others.
  5. Skype let’s me stay in touch with my ITA colleagues, hence it’s definitely a learning tool. I also use it occasionally to have conversations with folks.
  6. Slack is another tool I use with some groups to stay in touch. People share there, which makes it useful.
  7. OmniGraffle is my diagramming tool, and diagramming is a way I play with representing my understandings. I will put down some concepts in shapes, connect them, and tweak until I think I’ve captured what I believe. I also use it to mindmap keynotes.
  8. Word is a tool I use to play with words as another way to explore my thinking. I use outlines heavily and I haven’t found a better way to switch between outlines and prose. This is where things like articles, chapters, and books come from. At least until I find a better tool (haven’t really got my mind around Scrivener’s organization, though I’ve tried).
  9. WordPress is my blogging tool (what I’m using here), and serves both as a thinking tool (if I write it out, it forces me to process it), but it’s also a share tool (obviously).
  10. Keynote is my presentation tool. It’s where I’ll noodle out ways to share my thinking. My presentations may get rendered to Powerpoint eventually out of necessity, but it’s my creation and preferred presentation tool.

Those are my tools, now what are yours?  Use the link to let Jane know, her collection and analysis of the tools is always interesting.

12 April 2017

Artificial Intelligence or Intelligence Augmentation

Clark @ 8:09 am

In one of my networks, a recent conversation has been on Artificial Intelligence (AI) vs Intelligence Augmentation (IA). I’m a fan of both, but my focus is more on the IA side. It triggered some thoughts that I penned to them and thought I’d share here [notes to clarify inserted with square brackets like this]:

As context, I’m an AI ‘groupie’, and was a grad student at UCSD when Rumelhart and McClelland were coming up with PDP (parallel distributed processing, aka connectionist or neural networks). I personally was a wee bit enamored of genetic algorithms, another form of machine learning (but a bit easier to extract semantics, or maybe just simpler for me to understand ;).

Ed Hutchins was talking about distributed cognition at the same time, and that remains a piece of my thinking about augmenting ourselves. We don’t do it all in our heads, so what can be in the world and what has to be in the head?  [the IA bit, in the context of Doug Engelbart]

And yes, we were following fuzzy logic too (our school was definitely on the left-coast of AI ;).  Symbolic logic was considered passe’! Maybe that’s why Zadeh [progenitor of fuzzy logic] wasn’t more prominent here (making formal logic probabilistic may have seemed like patching a bad core premise)?  And I managed (by hook and crook, courtesy of Don Norman ;) to attend an elite AI convocation held at an MIT retreat with folks like McCarthy, Dennett, Minsky, Feigenbaum, and other lights of both schools.  (I think Newell were there, but I can’t state for certain.)  It was groupie heaven!

Similarly, it was the time of emergence of ‘situated cognition’ too (a contentious debate with proponents like Greeno and even Bill Clancy while old school symbolics like Anderson and Simon argued to the contrary).  Which reminds me of Harnad’s Symbol Grounding problem, a much meatier objection to real AI than Dreyfuss’ or the Chinese room concerns, in my opinion.

I do believe we ultimately will achieve machine consciousness, but it’s much further out than we think. We’ll have to understand our own consciousness first, and that’s going to be tough, MRI and other such research not withstanding. And it may mean simulating our cognitive architecture on a sensor equipped processor that must learn through experimentation and feedback as we do. e.g. taking a few years just to learn to speak! (“What would it take to build a baby?” was a developmental psych assignment I foolishly attempted ;)

In the meantime, I agree with Roger Schank (I think he was at the retreat too) that most of what we’re seeing, e.g. Watson, is just fast search, or pattern-learning. It’s not really intelligent, even if it’s doing it like we do (the pattern learning). It’s useful, but it’s not intelligent.

And, philosophically, I agree with those who have stated that we must own the responsibility to choose what we take on and what we outsource. I’m all for self-driving vehicles, because the alternative is pretty bad (tho’ could we do better in driver training or licensing, like in Germany?).  And I do want my doctor augmented by powerful rote operations that surpass our own abilities, and also by checklists and policies and procedures, anything that increases the likelihood of a good diagnosis and prescription.  But I want my human doctor in the loop.  We still haven’t achieved the integration of separate pattern-matching, and exception handling, that our own cognitive processor provides.

4 April 2017

Continual Exploration

Clark @ 8:09 am

CompassI was reading about Digital Business Platforms, which is a move away from  siloed IT systems to create a unified environment. Which, naturally, seems like a sensible thing to do. The benefits are about continual innovation, but I wonder if a more apt phrase is instead continual exploration.

The premise is that it’s now possible to migrate from separate business systems and databases, and converge that data into a unified platform. The immediate benefits are that you can easily link information that was previously siloed, and track real time changes. The upside is the ability to try out new business models easily.  And while that’s a good thing, I think it’s not going to get fully utilized out of the box.

The concomitant component, it seems to me, is the classic ‘culture’ of learning. As I pointed out in last week’s post, I think that there are significant benefits to leveraging the power of social media to unleash organizational outcomes. Here, the opportunity is to facilitate easier experimentation. But that takes more than sophisticated tools.

These tools, by integrating the data, allow new combinations of data and formulas to be tried and tested easily. This sort of experimentation is critical to innovation, where small trials can be conducted, evaluated, and reviewed to refine or shift direction.   This sort of willingness to make trials, however, isn’t necessarily going to be successful in all situations.  If it’s not safe to experiment, learn from it, and share those learnings, it’s unlikely to happen.

Thus, the willingness to continually experiment is valuable. But I wonder if a better mindset is exploration. You don’t want to just experiment, you want to map out the space of possibilities, and track the outcomes that result from different ‘geographies’.  To innovate, you need to try new things. To do that, you need to know what the things are you could try, e.g .the places you haven’t been and perhaps look promising.

It has to be safe to be trying out different things. There is trust and communication required as well as resources and permission. So here’s to systematic experimentation to yield continual exploration!

21 March 2017

Top down or bottom up strategy?

Clark @ 8:09 am

In a recent discussion around HR strategy, the question arose about where to start.  That is, if you’ve bought into moving into the digital age, where do you begin.  The flip answer from the host of the event, a large consulting agency, was to hire them (and my flip reply is to ask whether you want newly minted MBAs following a process designed to be ‘heavy’, or someone coming in light and fast with an adaptive approach ;). But then they got serious, and responded that you shouldn’t be reactive to people’s stated needs, and you needed data to identify what problems are crucial.  And I wasn’t satisfied with that, for two related reasons.  In short, I thought that was still reactive and that it wasn’t going to help you focus ahead, and that you needed top-down to complement bottom up.

This was buttressed by a post pointed out to me by my ITA colleagues that was arguing a good design strategy was to find out what people needed. And I’m reminded of the quote by Steve Jobs that you can’t just give people what they want, because by the time you do, they’ve changed their minds.  And just finding what people need and doing it is a bit reactive, it seems to me, regardless.  Even, to be honest, finding the company’s biggest barriers, and addressing them, isn’t a sufficient response.  It’s a good one, but it’s not enough.

Interestingly, an HR Director sitting next to me was nodding her head during that response about the data. So afterward I asked her what sort of data she had in mind. I asked about both survey data, and business metrics, and she indicated both (and anything else ;).  And I think that’s a good basis. But not a sufficient one.

If you look at most design in the real world, you’ll see that designers cycle between top-down and bottom-up.  It helps to check that you’re indeed draining the swamp, but also to ensure you’re not getting eaten by alligators.  And that’s the point I want to make.

I’m (obviously) a believer in frameworks. I want conceptual clarity. And I don’t want best practices, I want to abstract best principles and recontextualize them.  But I also believe you need to check how you’re going, and regularly test.  There are some overarching results that should be incorporated: culture, innovation, performance support, etc. And they can be instituted in ways that address problems yet also develop your ability.

So I do think collecting data on what’s going on, and identifying barriers is important.  But if you’re not also looking at the horizon and figuring out where you’re going in the longer term, you could be metaphorically ensuring no flat tires on a trip to the wrong neighborhood.  My short answer to their question would’ve been to document where you are, and where you want to get, and then figure out which of the top issues the data indicate sets you on a path to address the rest and build your capability and credibility.

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