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

22 November 2017

Solutions for Tight Cycles of Assessment

Clark @ 8:03 AM

In general, in a learning experience stretching out over days (as spaced learning would suggest), learners want to regularly get feedback about how they’re doing. As a consequence, you want regular cycles of assessment. However, there’s a conflict.  In workplace performance we produce complex outputs (RFPs, product specs, sales proposals, strategies, etc). These still typically require human oversight to evaluate.  Yet resource limitations are likely in most such situations, so we prefer auto-marked solutions (read: multiple choice, fill-in-the-blank), etc.  How do we reconcile meaningful assessment with realistic constraints?  This is one of the questions I’ve been thinking about, and I thought I’d share my reflections with you.

In workplace learning, at times we can get by with auto-assessment, particularly if we use coaching beyond the learning event.  Yet if it matters, we’d rather them practice things that matter before they actually are used for real work.  And for formal education, we want learners to have at least weekly cycles of performance and assessment.  Yet we also don’t want just rote knowledge checks, as they don’t lead to meaningful performance.  We need some intermediate steps, and that’s what I’ve been thinking on.

Multiple choice mini-scenario structureSo first, in Engaging Learning, I wrote about what I called ‘mini-scenarios’. These are really just better-written multiple-choice questions.  However, such questions don’t ask learners to identify definitions or the like (simple recognition), but instead put learners in contextual situations.  Here, the learner chooses between different decisions. Which means retrieving the information, mapping it to the context,  and then choosing the best answer.  Such a question has a story context, a precipitating situation, and then alternative decisions. (And the alternatives are ways learners go wrong, not silly or obviously incorrect choices).  I suggest that your questions should be like this, but are there more?

Branching scenarios are another, rich form of practice. Here it’s about tying together the decisions (they do tend to travel in packs) and consequences. When you do so, you can provide an immersive experience.  (When designed well, of course.)  They’re a pragmatic approximation of a full game experience.  Full games are really good when you need lots of practice (or can amortize over a large audience), but they’re an additional level of complexity to develop.

Another one that Tom Reeves presented in an article was intriguing. You not only have to make the right choice, but then you also choose the reason why you made that choice. It’s only an additional step, but it gets at the choice and the thinking.  And this is important. It would minimize the likelihood of guessing, and provide a richer basis for diagnosis and feedback.  Of course, no one is producing a ‘question type’ like this that I know of, but it’d be a good one.

An approach we used in the past was to have learners create a complex answer, but have the learner evaluate it! In this case it was a verbal response to a question (we were working on speaking to the media), but then the learner could hear their own answer and a model one.  Of course, you’d want to pair this with an evaluation guide as well. The learner creates a response, and then is presented with their response, a good response, and a rubric about what makes a good answer. Then we ask the learner to self evaluate against the rubric.  This has the additional benefit that learners are evaluating work with guidance, and can internalize the behavior to become a self-improving learner. (This is the basis of ‘reciprocal teaching’, one of the component approaches in Cognitive Apprenticeship.)

Each of these is aut0-(or self-) marked, yet provides valuable feedback to the learner and valuable practice of skills. Which shouldn’t be at the expense of also having instructor-marked complex work products or performances, but can supplement them. The goal is to provide the learner with guidance about how their understanding is progressing while keeping marking loads to a minimum. It’s not ideal, but it’s practical.  And it’s not exclusive of knowledge test as well, but it’s more applied and therefore is likely to be more valuable to the learner and the learning. I’m percolating on this, but I welcome hearing what approaches (and reflections) you have.

16 November 2017

#AECT17 Conference Contributions

Clark @ 8:04 AM

So, at the recent AECT 2017 conference, I participated in three ways that are worth noting.  I had the honor of participating in two sessions based upon writings I’d contributed, and one based upon my own cogitations. I thought I’d share the thinking.

For my own presentation, I shared my efforts to move ‘rapid elearning’ forward. I put Van Merrienboer’s 4 Component ID and Guy Wallace’s Lean ISD as a goal, but recognized the need for intermediate steps like Michael Allen’s SAM, David Merrill’s ‘Pebble in a Pond‘, and Cathy Moore’s Action Mapping. I suggested that these might be too far, and want steps that might be slight improvements on their existing processes. These included three thing: heuristics, tools, and collaboration. Here I was indicating specifics for each that could move from well-produced to well-designed.

In short, I suggest that while collaboration is good, many corporate situations want to minimize staff. Consequently, I suggest identifying those critical points where collaboration will be useful. Then, I suggest short cuts in processes to the full approach. So, for instance, when working with SMEs focus on decisions to keep the discussion away from unnecessary knowledge. Finally, I suggest the use of tools to support the gaps our brain architectures create.   Unfortunately, the audience was small (27 parallel sessions and at the end of the conference) so there wasn’t a lot of feedback. Still, I did have some good discussion with attendees.

Then, for one of the two participation session, the book I contributed to solicited a wide variety of position papers from respected ed tech individuals, and then solicited responses to same.  I had responded to a paper suggesting three trends in learning: a lifelong learning record system, a highly personalized learning environment, and expanded learner control of time, place and pace of instruction. To those 3 points I added two more: the integration of meta-learning skills and the breakdown of the barrier between formal learning and lifelong learning. I believe both are going to be important, the former because of the decreasing half-life of knowledge, the latter because of the ubiquity of technology.

Because the original author wasn’t present, I was paired for discussion with another author who shares my passion for engaging learning, and that was the topic of our discussion table.  The format was fun; we were distributed in pairs around tables, and attendees chose where to sit. We had an eager group who were interested in games, and my colleague and I took turns answering and commenting on each other’s comments. It was a nice combination. We talked about the processes for design, selling the concept, and more.

For the other participation session, the book was a series of monographs on important topics.  The discussion chose a subset of four topics: MOOCs, Social Media, Open Resources, and mLearning. I had written the mLearning chapter.  The chapter format included ‘take home’ lessons, and the editor wanted our presentations to focus on these. I posited the basic mindshifts necessary to take advantage of mlearning. These included five basic principles:

  1. mlearning is not just mobile elearning; mlearning is a wide variety of things.
  2. the focus should be on augmenting us, whether our formal learning, or via performance support, social, etc.
  3. the Least Assistance Principle, in focusing on the core stuff given the limited interface.
  4. leverage context, take advantage of the sensors and situation to minimize content and maximize opportunity.
  5. recognize that mobile is a platform, not a tactic or an app; once you ‘go mobile’, folks will want more.

The sessions were fun, and the feedback was valuable.

15 November 2017

#AECT17 Reflections

Clark @ 8:10 AM

Ok, so I was an academic for a brief and remarkably good period of time (a long time ago). Mind you, I’ve kept my hand in: reviewing journal and conference submissions, writing the occasional book chapter, contributing to some research, even playing a small role in some grant-funded projects. I like academics, it’s just that circumstances took me away (and I like consulting too; different, not one better). However, there’re a lot of benefits from being engaged, particularly keeping up with the state of the art. At least one perspective… Hence, I attended the most recent meeting of the Association of Educational Communications & Technology, pretty much the society for academics in instructional technology.

The event features many of your typical components: keynotes, sessions, receptions, and the interstitial social connections. One of the differences is that there’s no vendor exhibition. And there are a lot of concurrent sessions: roughly 27 per time slot!   Now, you have to understand, there are multiple agendas, including giving students and new faculty members opportunities for presentations and feedback. There are also sessions designed for tapping into the wisdom of the elders, and working sessions to progress understandings. This was only my second, so I may have the overall tenor wrong.  Regardless, here are some reflections from the event:

For one, it’s clear that there’s an overall awareness of what could, and should, be happening in education. In the keynotes, the speakers repeatedly conveyed messages about effective learning. What wasn’t effectively addressed was the comprehensive resistance of the education system to meaningful change.  Still, all three keynotes, Driscoll, Cabrera, and Reeves, commented in one way or another on problems and opportunities in education. Given that many of the faculty members come from Departments of Education, this is understandable.

Another repeated emergent theme (at least for me) was the need for meaningful research. What was expressed by Tom Reeves in a separate session was the need for a new approach to research grounded in focusing on real problems. I’ve been a fan of his call for Design-Based Research, and liked what he said: all thesis students should introduce their topics with the statement “the problem I’m looking at is”. The sessions, however, seemed to include too many small studies. (In my most cynical moments, I wonder how many studies have looked at teaching students or teacher professional development and their reflections/use of technology…).

One session I attended was quite exciting. The topic was the use of neuroscience in learning, and the panel were all people using scans and other neuroscience data to inform learning design. While I generally deride the hype that usually accompanies the topic, here were real researchers talking actual data and the implications, e.g. for dyslexia.  While most of the results from research that have implications for design are still are at the cognitive level, it’s important to continue to push the boundaries.

I focused my attendance mostly on the Organizational Training & Performance group, and heard a couple of good talks.  One was a nice survey of mentoring, looking across the research, and identifying what results there were, and where there were still opportunities for research. Another study did a nice job of synthesizing models for human performance technology, though the subsequent validation approach concerned me.

I did a couple of presentations myself that I’ll summarize in tomorrow’s post, but it was a valuable experience. The challenges are different than in corporate learning technology, but there are interesting outcomes that are worth tracking.  A valuable experience.

10 November 2017

Tom Reeves AECT Keynote Mindmap

Clark @ 7:11 AM

Thomas Reeves opened the third day of the AECT conference with an engaging keynote that used the value of conation to drive the argument for Authentic Learning. Conation is the component of cognition that consists of your intent to learn, and is under-considered. Authentic learning is very much collaborative problem-solving. He used the challenges from robots/AI to motivate the argument.

Mindmap

6 October 2017

Two good books on learning

Clark @ 8:10 AM

In addition to the existing good books out there (Julie Dirksen’s Design for How People Learn, Patti Schank’s new series, e.g. her book on Practice & Feedback, & Brown, et al’s Make it Stick), I was pointed to two others. One I’d heard about but hadn’t gotten to yet, the other was new to me. And now that I’ve finished them, both are worth recommending and adding to your reading list.

Benedict Carey’s How We Learn is an accessible overview of the science of learning. As a journalist (not a scientist), he documents his own unlikely journey as a learner, and how that matches up with what’s known. His idiosyncratic study habits, he discovers, are actually not that far off from what really does work for learning (as opposed to passing tests, and that’s an important distinction).  He includes practical implications and maintains a motivating style to help others to put the practice advice to work. His point, it’s what you do as much as how.

This is a book to give to learners to help them understand themselves as learners. The colloquial style and personal anecdotes make the messages comprehensible and relevant. The book includes a full suite of advice about how to learn best.  While it may be hard to convince learners to read a book on learning, this may well be the most valuable investment they can make.

On the other hand, Anders Ericsson’s Peak is very much the translated (co-authored by Robert Pool, a journalist) science book. It’s full of revelations, but laid out with scientific experiments to complement a very thorough set of case studies. What it does beautifully is unmask the myth of ‘native talent’ and unpack the details that lead to expertise. And those details, specifically are about deliberate practice.  

Most importantly, in my mind, is the summary that points out that our focus should not be so much on expert performance but instead on helping so many achieve meaningful levels of ability that they’ve been turned off to by bad stories.  Too often people will say “I can’t do math” and instead such abilities can be developed wonderfully. This book, while relevant to individuals, has much more insight to provide for learning designers.  It separates out why you want models like activity-based learning.  And why what we do too often in classrooms and online aren’t helpful.

I’d put these near the top of my recommended reading lists.

5 October 2017

So I was, at least partly, wrong

Clark @ 8:08 AM

A number of years ago, I wrote that pre-testing learners was user abusive (with a caveat). My argument was sensible, qualified, but apparently wrong. Now that I’ve more of the story, it’s time to rectify my mistake. Of course, there are still remaining questions ;).

My claim was that while pre-testing might have some small benefit, forcing users to test on things they don’t know isn’t nice.  Moreover, I attributed that benefit to activating relevant material, and suggested that there were more humane ways to do it. However, if the pre-test could show that learners did know it, and so be able to skip it, it’d be worthwhile.

However, research has now shown more benefits to pre-testing. That is, causing learners to search for information they don’t have somehow makes the memory traces more susceptible to successful learning subsequently.  Without a full neurological explanation, it appears that the activation goes deeper than just associative awakening. It also appears to be for more than just memory, but actual performance.

This, then, argues that pre-testing is a good thing. Now, I haven’t been able to find a comparison where this pre-testing was compared to a compelling story or question that didn’t require an actual response. Still, I’m willing to believe that the actual requirement for search in a test is more powerful than mere related stories.

And this also makes the case stronger, in my mind, for problem-based learning. That is, if you’re faced with a problem you don’t know the answer to (and it’s a comprehensive question representing the overall learning goal), both the need to look for the answer and (ideally) a compelling story in which it’s important make a good case for the learning to be more effective.

Which doesn’t mean I don’t still feel it’s abusive, but it’s in a good cause.  And it still could be that the learner doesn’t actually have to take a ‘test’, but instead in some less formal way is asked to retrieve the answer.  And it might not.

Regardless, I feel obligated to change my opinion when data contravenes, even in part, a story I previously believed. And it doesn’t even hurt much ;).  Here’s to good design!

24 August 2017

Extending Engagement

Clark @ 8:09 AM

My post on why ‘engagement’ should be added to effective and efficient led to some discussion on LinkedIn. In particular, some questions were asked that I thought I should reflect on.  So here are my responses to the issue of how to ‘monetize’ engagement, and how it relates to the effectiveness of learning.

So the first issue was how to justify the extra investment engagement would entail. It was an assumption that it would take extra investment, but I believe it will. Here’s why. To make a learning experience engaging, you need some additional things: knowing why this is of interest and relevance to practitioners, and putting that into the introduction, examples, and practice.  With practice, that’s going to come with only a marginal overhead. More importantly, that is part of also making it more effective. There is some additional information needed, and more careful design, and that certainly is more than most of what’s being done now. (Even if it should be.)

So why would you put in this extra effort?  What are the benefits? As the article suggested, the payoffs are several:

  • First, learners know more intrinsically why they should pay attention. This means they’ll pay more attention, and the learning will be more effective. And that’s valuable, because it should increase the outcomes of the learning.
  • Second, the practice is distributed across more intriguing contexts. This means that the practice will have higher motivation.  When they’re performing, they’re motivated because it matters. If we have more motivation in the learning practice, it’s closer to the performance context, so we’re making the transfer gap smaller. Again, this will make the learning more effective.
  • Third, that if you unpack the meaningfulness of the examples, you’ll make the underlying thinking easier to assimilate. The examples are comprehended better, and that leads to more effectiveness.

If learning’s a probabilistic game (and it is), and you increase the likelihood of it sticking, you’re increasing the return on your investment. If the margin to do it right is less than the value of the improvement in the learning, that’s a business case. And I’ll suggest that these steps are part of making learning effective, period. So it’s really going from a low likelihood of transfer – 20-30% say – to effective learning – maybe 70-80%.  Yes, I’m making these numbers up, but…

This is really all part of going from information dump & knowledge test to elaborated examples and contextualized practice.  So that’s really not about engagement, it’s about effectiveness. And a lot of what’s done under the banner of ‘rapid elearning’ is ineffective.  It may be engaging, but it isn’t leading to new skills.

Which is the other issue: a claim that engagement doesn’t equal better learning. And in general I agree (see: activity doesn’t mean effectiveness in a social media tool). It depends on what you mean by engagement; I don’t mean trivialized scores equalling more activity. I mean fundamental cognitive engagement: ‘hard fun’, not just fun.  Intrinsic relevance. Not marketing flare, but real value add.

Hopefully this helps!  I really want to convince you that you want deep learning design if you care about the outcomes.  (And if you don’t, why are you bothering? ;).  It goes to effectiveness, and requires addressing engagement. I’ll also suggest that while it does affect efficiency, it does so in marginal ways compared to substantial increases in impact.  And that strikes me as the type of step one should be taking. Agreed?

 

16 August 2017

3 E’s of Learning: why Engagement

Clark @ 8:07 AM

Letter EWhen 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!

9 August 2017

Simulations versus games

Clark @ 8:04 AM

At the recent Realities 360 conference, I saw some confusion about the difference between a simulation and a game. And while I made some important distinctions in my book on the topic, I realize that it’s possible that it’s time to revisit them. So here I’m talking about some conceptual discriminations that I think are important.

Simulations

As I’ve mentioned, simulations are models of the world. They capture certain relationships we believe to be true about the world. (For that matter, they can represent worlds that aren’t real, certainly the case in games.). They don’t (can’t) capture all the world, but a segment we feel it is important to model. We tend to validate these models by testing them to see if they behave like our real world.  You can also think about simulations as being in a ‘state’ (set of values in variables), and move to others by rules.  Frequently, we include some variability in these models, just as is reflected in the real world. Similarly, these simulations can model considerable complexity.

Such simulations are built out of sets of variables that represent the state of the world, and rules that represent the relationships present. There are several ways things change. Some variables can be changed by rules that act on the basis of time (while countdown timer = on, countdown = countdown -1). Variables can also interact (if countdown=0: if 1 g adamantium and 1 g dilithium, Temperature = Temperature +1000, adamantium = adamantium – 1g, dilithium = dilithium – 1g).  Other changes are based upon learner actions (if learner flips the switch, countdown timer = on).

Note that you may already have a simulation. In business, there may already exist a model of particular processes, particularly if they’re proprietary systems.

From a learning point of view, simulations allow motivated and self-effective learners to explore the relationships they need to understand. However, we can’t always assume motivated and self-effective learners. So we need some additional work to turn a simulation into a learning experience.

Scenarios

One effective way to leverage simulations is to choose an initial state (or ‘space of states’, a start point with some variation), and a state (or set) that constitutes ‘win’. We also typically have states that also represent ‘fail’.  We choose those states so that the learner can’t get to ‘win’ without understanding the necessary relationships.   The learner can try and fail until they discover the necessary relationships.  These start and goal states serve as scaffolding for the learning process.  I call these simulations with start and stop states ‘scenarios’.

This is somewhat complicated by the existence of ‘branching scenarios’. There are initial and goal states and learner actions, but they are not represented by variable and rules. The relationships in branching scenarios are implicit in the links instead of explicit in the variables and rules. And they’re easier to build!  Still, they don’t have the variability that typically is possible in a simulation. There’s an inflection point (qualitative, not quantitative) where the complexity of controlling the branches renders it more sensible to model the world as a simulation rather than track all the branches.

Games

The problem here is that too often people will build a simulation and call it a game. I once reviewed a journal submission about a ‘game’ where the authors admitted that players thought it was boring. Sorry, then it’s not a game!  The difference between a simulation and a game is a subjective experience of engagement on the part of the player.

So how do you get from a simulation to a game?  It’s about tuning.  It’s about adjusting the frequency of events, and their consequences, such that the challenge moves to fall into the zone between boring and frustrating. Now, for learning, you can’t change the fundamental relationships you’re modeling, but you can adjust items like how quickly events occur, and the importance of being correct. And it takes testing and refinement. Will Wright, a game designers’ game designer, once proposed that tuning is 9/10’s of the work!  Now that’s for a commercial game, but it gives you and idea.

You can also use gamification, scores to add competition, but, please, only after you first expend the effort to make the game intrinsically interesting. Tap into why they should care about the experience, and bake that it.

Is it worth it to actually expend effort to make the experience engaging?  I believe that the answer is yes. Perhaps not to the level of a game people will pay $60 to play, but some effort to manifest the innate meaningfulness is worth it. Games minimize the time to obtain competency because they optimize the challenge.  You will have sticks as well as carrots, so you don’t need to put in $M budgets, but do tune until your learners have an engaging and effective experience.

So, does this help? What questions do you still have?

8 August 2017

L&D Tuneup

Clark @ 8:00 AM

auto engineIn 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?

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