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

When you’re creating learning experiences, you want to worry about the outcomes, but there’s more to it than that.  I think there are 3 major components for learning as a practical matter, and I lump these under the E’s: Effectiveness, Efficiency, & Engagement. The latter may be more of a stretch, but I’ll make the case .

When you typically talk about learning, you talk about two goals: retention over time, and transfer to all appropriate (and no inappropriate) situations.  That’s learning effectiveness: it’s about ensuring that you achieve the outcomes you need.  To test retention and transfer, you have to measure more than performance at the end of the learning experience. (That is, unless your experience definition naturally includes this feedback as well.) Let alone just asking learners if they thought it was valuable.  You have to see if the learning has persisted later, and is being used as needed.

However, you don’t have unlimited resources to do this, you need to balance your investment in creating the experience with the impact on the individual and/or organization.  That’s efficiency. The investment is rewarded with a multiplier on the cost.  This is just good business.

Let’s be clear: investing without evaluating the impact is an act of faith that isn’t scrutable.  Similarly, achieving the outcome at an inappropriate expense isn’t sustainable.  Ultimately, you need to achieve reasonable changes to behavior under a viable expenditure.

A few of us have noticed problems sufficient to advocate quality in what we do.  While things may be trending upward (fingers crossed), I think there’s still ways to go when we’re still hearing about ‘rapid’ elearning instead of ‘outcomes’.  And I’ve argued that the necessary changes produce a cost differential that is marginal, and yet yields outcomes more than marginal.   There’s an obvious case for effectiveness and efficiency.

But why engagement? Is that necessary? People tout it as desirable. To be fair, most of the time they’re talking about design aesthetics, media embellishment, and even ‘gamification‘ instead of intrinsic engagement.  And I will maintain that there’s a lot more possible. There’s an open question, however: is it worth it?

My answer is yes. Tapping into intrinsic interest has several upsides that are worth the effort.  The good news is that you likely don’t need to achieve a situation where people are willing to pay money to attend your learning. Instead, you have the resources on hand to make this happen.

So, if you make your learning – and here in particular I mean your introductions, examples, and practice – engaging, you’re addressing motivation, anxiety, and potentially optimizing the learning experience.

• If your introduction helps learners connect to their own desires to be an agent of good, you’re increasing the likelihood that they’ll persist and that the learning will ‘stick’.
• If your examples are stories that illustrate situations the learner recognizes as important, and unpack the thinking that led to success, you’re increasing their comprehension and their knowledge.
• Most importantly, if your practice tasks are situated in contexts that are meaningful to learners both because they’re real and important, you’ll be developing their skills in ways closest to how they’ll perform.  And if the challenge in the progression of tasks is right, you’ll also accelerate them at the optimal speed (and increase engagement).

Engagement is a fine-tuning, and learner’s opinions on the experience aren’t the most important thing.  Instead, the improvement in learning outcomes is the rationale.  It takes some understanding and practice to get systematically good at doing this. Further, you can make learning engaging, it is an acquired capability.

So, is your learning engaging intrinsic interest, and making the learning persist? It’s an approach that affects effectiveness in a big way and efficiency in a small way. And that’s the way you want to go, right? Engage!

I recently read that Apple’s new office plan is receiving bad press. This surprises me, given that Apple usually has their handle on the latest in ideas.  Yet, upon investigation, it’s clear that they appear to not be particularly innovative in their approach to work spaces.  Here’s why.

The report I saw says that Apple is intending to use an open office plan. This is where all the tables are out in the open, or at best there are cubicles. The perceived benefits are open communication.  And this is plausible when folks like Stan McChrystal in Team of Teams are arguing for ‘radical transparency’.  The thought is that everyone will know what’s going on and it will streamline communication. Coupled with delegation, this should yield innovation, at the expense of some efficiency.

However, research hasn’t backed that up. Open space office plans can even drive folks away, as Apple’s hearing. When you want to engage with your colleagues and stay on top of what they’re doing, it’s good.  However, the lack of privacy means folks can’t focus when they’re doing heavy mental work. While it sounds good in theory, it doesn’t work in practice.

When I was keynoting at the Learning@Work conference in Sydney back in 2015, a major topic was about flexible work spaces. The concept here is to have a mix of office types: some open plan, some private offices, some small conference rooms. The view is that you take the type of space you need when you need it. Nothing’s fixed, so you travel with your laptop from place to place, but you can have the type of environment you need. Time alone, time with colleagues, time collaborating. And this was being touted both on principled and practical grounds with positive outcomes.

(Note that in McChrystal’s view, you needed to break down silos. He would strategically insert a person from one area with others, and have representatives engaged around all activities.  So even in the open space you’d want people mixed up, but most folks still tend to put groups together. Which undermines the principle.)

As Jay Cross let us know in his landmark Informal Learning, even the design of workspaces can facilitate innovation. Jay cited practices like having informal spaces to converse, and putting the mail room and coffee room together to facilitate casual conversation.  Where you work matters as well as how, and open plan has upsides but also downsides that can be mitigated.

Innovation is about culture, practices, beliefs, and  technology.  Putting it all together in a practical approach takes time and knowledge to figure out where to start, and how to scale.  As Sutton and Rao tell us, it’s a ground war, but the benefits are not just desirable, but increasingly necessary. Innovation is the key to transcending survival to thrival. Are you ready to (Qu)innovate?

In my youth, owing to my father’s tutelage and my desire for wheels, I learned how to work on cars. While not the master he was, I could rebuild a carburetor, gap points and sparkplugs, as well as adjust the timing. In short, I could do a tuneup on the car.  And I think that’s what Learning & Development (L&D) needs, a tuneup.

Cars have changed, and my mechanic skills are no longer relevant. What used to be done mechanically – adjusting to altitude, adapting through the stages of the engine warming up, and handling acceleration requests – are now done electronically. The air-fuel mixture and the spark advance are under the control of the fuel injection and electronic ignition systems (respectively) now.  With numerous sensors, we can optimize fuel efficiency and performance.

And that’s the thing: L&D is too often still operating in the old, mechanical, model. We have the view of a hierarchical model where a few plan and prepare and train folks to execute. We stick with face-to-face training or maybe elearning, putting everything in the head, when science shows that we often function better from information in the world or even in other people’s heads!  And this old approach no longer works.

As has been noted broadly and frequently, the world is changing faster and the pressure is on organizations to adapt more quickly. With widely disparate paths  pointing in the same direction, it’s easy to see that there’s something fundamental going on. In short, we need to move, as Jon Husband puts it, from hierarchy to wirearchy.  We need agility: experimentation, review, and reflection, iteratively and collectively. And in that move, there’s a central role for L&D.

The move may not be imminent, but it is unavoidable. Even staid and secure organizations are facing the consequences of increasing rates of change and new technology innovations. AI, networks, 3D printing, there are ramifications. Even traditional government agencies are facing change. Yet, this is all about people and learning.

As Harold Jarche tells us, work is learning and learning is the work. That means learning is moving from the classroom to the workplace and on the go. L&D needs a modern workplace learning approach, as Jane Hart lets us know. This new model is one where L&D moves from fount of knowledge to learning facilitator (or advisor, as she terms it).  People need to develop those communication and collaboration, but it won’t come from classes, but from coaching and more.

And, to return to the metaphor, I view this as an L&D tuneup. It’s not about throwing out what you’re doing (unless that’s the fastest path ;), but instead augmenting it. Shifts don’t happen overnight, but instead it means taking on some internal changes, and then working that outwards with stakeholders, reengineering the organizational relationships. It’s a journey, not an event. But like with a tuneup, it’s about figuring out what your new model should be, and then adjusting until you achieve it. It’s over a more extended period of time, but it’s still a tuning operation. You have to work through the stages to a new revolutionary way of working. So, are you ready for a tuneup?

I had the opportunity to attend a special event pondering the ethical issues that surround Artificial Intelligence (AI).  Hosted by the Institute for the Future, we gathered in groups beforehand to generate questions that were used in a subsequent session. Vint Cerf, co-developer of the TCP/IP protocol that enabled the internet, currently at Google, responded to the questions.  Quite the heady experience!

The questions were quite varied. Our group looked at Values and Responsibilities. I asked whether that was for the developers or the AI itself. Our conclusion was that it had to be the developers first. We also considered what else has been done in technology ethics (e.g. diseases, nuclear weapons), and what is unique to AI.  A respondent mentioned an EU initiative to register all internet AIs; I didn’t have the chance to ask about policing and consequences.  Those strike me as concomitant issues!

One of the unique areas was ‘agency’, the ability for AI to act.  This led to a discussion for a need to have oversight on AI decisions. However, I suggested that humans, if the AI was mostly right, would fatigue. So we pondered: could an AI monitor another AI?  I also thought that there’s evidence that consciousness is emergent, and so we’d need to keep the AIs from communicating. It was pointed out that the genie is already out of the bottle, with chatbots online. Vint suggests that our brain is layered pattern-matchers, so maybe consciousness is just the topmost layer.

One recourse is transparency, but it needs to be rigorous. Blockchain’s distributed transparency could be a model. Of course, one of the problems is that we can’t even explain our own cognition in all instances (we make stories that don’t always correlate with the evidence of what we do). And with machine learning, we may be making stories about what the system is using to analyze behaviors and make decisions, but it may not correlate.

Similarly, machine learning is very dependent on the training set. If we don’t pick the right inputs, we might miss some factors that would be important to incorporate in making answers.  Even if we have the right inputs, but don’t have a good training set of good and bad outcomes, we get biased decisions. It’s been said that what people are good at is crossing the silos, whereas the machines tend to be good in narrow domains. This is another argument for oversight.

The notion of agency also brought up the issue of decisions.  Vint inquired why we were so lazy in making decisions. He argued that we’re making systems we no longer understand!  I didn’t get the chance to answer that decision-making is cognitively taxing.  As a consequence, we often work to avoid it.  Moreover, some of us are interested in X, so are willing to invest the effort to learn it, while others are interested in Y. So it may not be reasonable to expect everyone to invest in every decision.  Also, our lives get more complex; when I grew up, you just had phone and TV, now you need to worry about internet, and cable, and mobile carriers, and smart homes, and…  So it’s not hard to see why we want to abrogate responsibility when we can!  But when can we, and when do we need to be careful?

Of course, one of the issues is about AI taking jobs.  Cerf stated that nnovation takes jobs, and generates jobs as well. However, the problem is that those who lose the jobs aren’t necessarily capable of taking the new ones.  Which brought up an increasing need for learning to learn, as the key ability for people. Which I support, of course.

The overall problem is that there isn’t a central agreement on what ethics a system should embody, if we could do it. We currently have different cultures with different values. Could we find agreement when some might have different view of what, say, acceptable surveillance would be? Is there some core set of values that are required for a society to ‘get along’?  However, that might vary by society.

At the end, there were two takeaways.  For one, the question is whether AI can helps us help ourselves!  And the recommendation is that we should continue to reflect and share our thoughts. This is my contribution.

Just what is the Future of Work about? Is it about new technology, or is it about how we work with people?  We’re seeing amazing new technologies: collaboration platforms, analytics, and deep learning. We’re also hearing about new work practices such as teams, working (or reflecting) out loud, and more.  Which is it? And/or how do they relate?

It’s very clear technology is changing the way we work. We now work digitally, communicating and collaborating.  But there’re more fundamental transitions happening. We’re integrating data across silos, and mining that data for new insights. We can consolidate platforms into single digital environments, facilitating the work.  And we’re getting smart systems that do things our brains quite literally can’t, whether it’s complex calculations or reliable rote execution at scale. Plus we have technology-augmented design and prototyping tools that are shortening the time to develop and test ideas. It’s a whole new world.

Similarly, we’re seeing a growing understanding of work practices that lead to new outcomes. We’re finding out that people work better when we create environments that are psychologically safe, when we tap into diversity, when we are open to new ideas, and when we have time for reflection. We find that working in teams, sharing and annotating our work, and developing learning and personal knowledge mastery skills all contribute. And we even have new  practices such as agile and design thinking that bring us closer to the actual problem.  In short, we’re aligning practices more closely with how we think, work, and learn.

Thus, either could be seen as ‘the Future of Work’.  Which is it?  Is there a reconciliation?  There’s a useful way to think about it that answers the question.  What if we do either without the other?

If we use the new technologies in old ways, we’ll get incremental improvements.  Command and control, silos, and transaction-based management can be supported, and even improved, but will still limit the possibilities. We can track closer.  But we’re not going to be fundamentally transformative.

On the other hand, if we change the work practices, creating an environment where trust allows both safety and accountability, we can get improvements whether we use technology or not. People have the capability to work together using old technology.  You won’t get the benefits of some of the improvements, but you’ll get a fundamentally different level of engagement and outcomes than with an old approach.

Together, of course, is where we really want to be. Technology can have a transformative amplification to those practices. Together, as they say, the whole is greater than the some of the parts.

I’ve argued that using new technologies like virtual reality and adaptive learning only make sense after you first implement good design (otherwise you’re putting lipstick on a pig, as the saying goes).  The same is true here. Implementing radical new technologies on top of old practices that don’t reflect what we know about people, is a recipe for stagnation.  Thus, to me, the Future of Work starts with practices that align with how we think, work, and learn, and are augmented with technology, not the other way around.  Does that make sense to you?

In much of the discussion about tapping into the power of people via networks and communities, we hear about the learning culture we need. Items like psychological safety, valuing diversity, openness, and time for reflection are up front. And I’m as guilty of this as anyone! However, one other element that appears in the more rigorous discussions (including Edmondson’s Teaming and Sutton & Rao’s Scaling Up Excellence) is accountability. (Which isn’t to say that it doesn’t appear in other pictures, but it’s certainly not foreground.) And it’s time to address this.

So, the model for becoming agile is creating an environment where people learn, alone and together.  But it’s informal learning. When you research, problem-solve, design, etc, you don’t know the answer when you start! Yet it’s not like anyone else has the answer, either.  And we know that the output is better when we search more broadly through the possible solution space. (Which is what we’re doing, really.) This means we need diverse inputs to keep us from prematurely converging.  Or searching too narrow a space. We also need also those different voices to contribute, or we won’t get there. And we have to be open to new ideas, or we could inadvertently cut off part of the solution space. We also need time and tools for reflection (hence reflecting out loud).

Typically, the process is iterative (real innovations percolate/ferment/incubate; they aren’t ‘driven’): going away, doing tasks, and returning.  Here, we need to ensure people are contributing, doing the work.  We don’t want to micromanage it, but we do want to assist people because we shouldn’t assume that they’re effective self and social learners.  In short, we can’t squelch the feeling of autonomy to accompany purpose (ala Dan Pink’s Drive), yet the job must get done!

When there’s purpose, and community, accountability is natural. When we comprehend how what we’re doing contributes, when we have reciprocal trust with our colleagues that we’ll each do our part, and when there’s transparency about what’s happening, it’s a natural. A transactional model, when it’s a network and not a community, doesn’t feel safe, and doesn’t work as well.  As Edmondson documents it, you want to be in the learning zone where you have both accountability and safety. And that’s not an easy balance.

So one of the steps to get there is to ensure accountability is part of the picture. And it’s not just calling someone on the carpet. Done right, it’s a tracking and regular support to succeed, so accountability is an ongoing relationship that suggests we want you to succeed, and we’ll help you do that. Accountability shouldn’t be a surprise! (Transparency helps.)

When we talk about the high-minded principles of making it safe and helping people feel welcome, some can view this as all touchy-feely and worry that it won’t get things done.  Which isn’t true, but I think it helps if we keep accountability in the picture to assuage those concerns.  Ultimately, we want outcomes, but new and improved ones, not just the same old things. The status quo isn’t really acceptable today. In this increasingly dynamic environment, the ability to adapt is key. And that’s the Learning Zone. Are you ready to go there?

Nathalie Nahai opened the second day of the FocuOn Learning conference. In a rapid fire presentation, she covered 7 principles that engage individuals into behaviors. With clear examples from familiar online experiences, she portrayed how these things work. Admirably, she finished with a call to ethical behavior.

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?

It’s Working Out Loud (WOL) Week, and that’s always a valuable time for reflection. It so happens that the past few weeks I’ve been working with an organization, and they were ripe for WOL. The problem was what, specifically, should they do to make this work? They had barriers.  My (off the cuff) recommendations were around creating some habits of work.

For context, they’re a very distributed organization, and have been for decades. They’ve a number of locations spread around over a space of hundreds of miles.  As a consequence, they’re well-practiced at a variety of distance communication modalities. They have well-equipped video conferencing rooms, social media tool, and of course email.  And yet, their communication is very formal. They’re busy of course, so while they recognize the benefits of sharing better, it’s hard for them to implement.

There would be rewards, of course. They have distributed teams supporting the same sorts of actions.  Various job roles do similar work with a variety of stakeholders, and would benefit by sharing best practices, creating communities of practice around those job roles. However, for a variety of reasons, including ineffective use of the available tools, time pressures, and general lack of awareness and practice, the practices not in play.

As part of my ‘critical friend‘ role, I made some suggestions, including working out loud. They asked for specific steps they might take. So what’d I recommend? Several things:

• Narrating their work: they need to find a way to represent their progress on each project, and include a ‘rationale’ that captures the thinking behind their decisions.
• Creating communities: they should establish a group (with whatever tool) for each role, and do some community management around it to generate dialog and learnings.
• Walking the walk: if the leadership (not of the overall organization, just the leaders of this learning unit, at least to start ;) practices the working (and failing) out loud, it would be motivation to others.  It runs better when everyone sees it’s safe to make mistakes as long as you share lessons learned).

This was off the cuff, and I might have suggested more, but this is a reconstituted list that I think captures some major necessary areas. It’s about practices that build the culture. These will need support, but they are the core ideas that can drive a move to a more open, sharing workplace. One that leads to continual improvement and innovation.

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.