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Clark Quinn’s Learnings about Learning

Engaging people at work

12 September 2023 by Clark Leave a Comment

Last week, Donald Taylor wrote an interesting post, wondering about ‘learner engagement’. That’s a topic I do talk a wee bit about ;). He closed with a call for feedback. So, while I did comment there, I thought it potentially would benefit from a longer response. I think it’s more general than learner engagement, so I’m talking about engaging people at work. (But it’s still relevant to his thesis without quibbling about that!)

In his post, he talked about three levels: asset, culture, and environment. I’m not sure I quite follow (to me, culture is an environmental level), and I’ve talked about individual, team, and organizational levels. To his point, however, there are steps to take at every level.

He starts at the individual level, talking about designing learning experiences. I agree with his ‘do deeper analysis’ recommendation, but I’d go further. To me, it’s not just if they recognize that content’s valuable, it’s about building, and maintaining, motivation while controlling anxiety (c.f. Make It Meaningful!). I don’t think he’d disagree.

At the next level up, it’s about making sure people are connected. Here, I’d point to Self-Determination Theory (SDT), and ‘relatedness’. I don’t mind Dan Pink’s reinterpretation of that to ‘purpose’, in that I think people need to know how what they’re doing contributes to something bigger, and that something bigger supports society as a whole.

Finally, to me, is culture. You want a ‘learning organization‘, as Don agrees. He says to start with a sympathetic manager, but I think L&D needs to create that culture internally first, then take it to the broader organization (and starting with said manager is a good next step).

I think that latter step solves Don’s final step of breaking down barriers, but he’s a smart guy and I’m willing to believe I’m missing some nuance. I do like his focus on ‘find a measure’ to use. However, ultimately, it should improve a lot of measures around adapting to change: innovation, retention, and success.  That’s my take, I welcome yours!

To design is human

5 September 2023 by Clark Leave a Comment

I maintaining a fascination in design, for several reasons. As Herb Simon famously said: “The proper study of mankind is the science of design.” My take is to twist the title of Henry Petroski’s book, To Engineer is Human into ‘to design is human’. To me, design is both a fascinating study in cognition, and an area of application. The latter of which seems to be flourishing!

I’ve talked in the past about various design processes (and design overall, a lot). As we’ve moved from waterfall models like the original ADDIE, we’ve shifted to more iterative approaches. So, I’ve mentioned Michael Allen’s SAM, Megan Torrance’s LLAMA, etc.

And I’ve been hit with a few more! Just in the past few days I’ve seen LeaPS and EnABLE. They’re increasingly aware of important issues in learning science. All of this is, to me, good. Whether they’re just learning design approaches, or more performance consulting (that is, starting with a premise that a course may not be the answer), it’s good to think consciously about design.

My interest in design came in a roundabout way. As an undergrad, I designed my own major on Computer-Based Education, and then got a job designing and programming educational computer games. What that didn’t do, was teach me much about design as a practice. However, going back to grad school (for several reasons, including knowing that we didn’t have a good enough foundation for those game designs) got me steeped in cognition and design. Of course, what emerges is that they link at the wrists and ankles.

So, my lab was studying designing interfaces. This included understanding how we think, so as to design to match. My twist was to also design for how we learn. However, more implicitly than explicitly perhaps, was also the topic of how to design. Just as we have cognitive limitations as users, we have limitations as designers. Thus, we need to design our design processes, so as to minimize the errors our cognitive architecture will introduce.

Ultimately, what separates us from other creatures is our ability to create solutions to problems, to design. I know there’s now generative AI, but…it’s built on the average. I still think the superlative will come from people. Knowing when and how is important. Design is really what we want people to do, so it’s increasingly the focus of our learning designs. And it’s the process we use to create those solutions. Underpinning both is how we think, work, and learn.

To design is human, and so we need to understand humans to design optimally. Both for the process, and the product. This, I think, makes the case that we do need to understand our cognitive architecture in most everything we do. What do you think?

FWIW, I’ll be talking about the science of learning at DevLearn. Hope to see you there. 

Top 10 tools for Learning 2023

31 August 2023 by Clark 3 Comments

Somehow I missed colleague Jane Hart’s annual survey of top 10 tools for learning ’til just today, yet it’s the last day! I’ve participated in the past, and find it a valuable chance for reflection on my own, as well as seeing the results come out. So here’s my (belated) list of top 10 tools for learning 2023.

I’m using  Harold Jarche’s Personal Knowledge Mastery framework for learning here. His categories of seek (search and feed), sense (interpret) and share (closely or broadly) seems like an interesting and relevant way to organize my tools.

Seek

I subscribe to blog posts via email, and I use Feedblitz because I use it as a way for people to sign up for Learnlets. I finally started paying so they didn’t show gross ads (you can now signup safely; they lie when they say the have ‘brand-safe’ ads), and fortunately my mail removes images (for safety, unless I ask), so I don’t see them.

I’m also continuing to explore Mastodon (@quinnovator@sfba.social). It has its problems (e.g. hard to find others, smaller overall population), but I do find the conversations to be richer.

I’m similarly experimenting with Discord. It’s a place where I can generally communicate with colleagues.

I’m using Slack as a way to stay in touch, and I regularly learn from it, too. Like the previous two, it’s both seek and share, of course.

Of course, web surfing is still a regular activity. I’ve been using DuckDuckGo as a search engine instead of more famous ones, as I like the privacy policies better.

Sense

I still use Graffle as a diagramming tool (Mac only). Though I’m intrigued to try Apple’s FreeForm, in recent cases I’ve been editing old diagrams to update, and it’s hard to switch.

Apple’s Keynote is also still my ‘goto’ presentation maker, e.g. for my LDA activities. I have to occasionally use or output to Powerpoint, but for me, it’s a more elegant tool.

I also continue to use Microsoft’s Word as a writing tool. I’ve messed with Apple’s Pages, but…it doesn’t transfer over, and some colleagues need Word. Plus, that outlining is still critical.

Share

My blog (e.g. what you’re reading ;) is still my best sharing tool, so WordPress remains a top learning tool.

LinkedIn has risen to replace Twitter (which I now minimize my use of, owing to the regressive policies that continue to emerge). It’s where I not only auto-post these screeds, but respond to others.

As a closing note, I know a lot of people are using generative AI tools as thinking partners. I’ve avoided that for several reasons. For one, it’s clear that they’ve used others’ work to build them, yet there’s no benefit to the folks whose work has been purloined. There are also mistakes.  Probably wrongly, but I still trust my brain first. So there’re my top 10 tools for learning 2023

Sloppy thinking?

29 August 2023 by Clark Leave a Comment

paint splatterOk, so I admit that I’m a bit of a pedant (and I hear you, saying “a bit?”). So, when I see categorizations, I should be more accepting of pragmatic approaches. Yet, I still get upset when I see same, when there are clear conceptual breakdowns that could be used instead. My hypothesis is that the good conceptual ones end up making the discriminations (eventually), with perhaps a bit more explanation needed, but…they don’t leave misconceptions as likely to occur. Thus, I’m not sure I’m happy with sloppy thinking.

One example comes from a textbook I’m reviewing. They’re talking about performance support, and differentiate between EPSS (electronic performance support systems), PSS (performance support systems), and MPSS (mobile performance support systems). First, wouldn’t you start with PSS as the beginning point? The definition used is online and offline. OK, so I can see EPSS as a subset of that. It’s only online, right? So why does it lead the list?

Then, MPSS is a separate category. Isn’t it a subset of EPSS? They note that it is, in fact, an EPSS on a mobile device. So it’s a subset of the first category. You’re going from the middle category to the broader and then to the narrower. That confounds a general trend to follow an order, increasing or decreasing, rather than apparently random. They do note that MPSS can do location-specific things, but that’s also a subset of doing contextually-based things. So, a wee bit facile, it seems to me.

Similarly, I’ve seen a categorization of game technologies, including having spreadsheet-based as a different category from branching. Yes, but spreadsheets are just a mechanism to implement a formal model. It can be in a spreadsheet, or code. Why does the implementation matter? Pragmatically, yes, it matters, but then have spreadsheets and code-implemented as subsets of programmed models. For that matter, you could have an analog implementation of the model!

These are fairly rigorous criteria, whereas all too often I’ll see a list of things (e.g. things you should/never do for X) that aren’t of the same type. Clearly, it’s a marketing person just aggregating a list of things, without a true understanding. I think it’s problematic, however. For one, it’s a missed opportunity to reflect important conceptual distinctions (can you tell I’ve been an educator?). For another, it might lead people to think that being scattershot is ok. Finally, it might undermine the actual development of important categorizations.

I’m willing to believe that, for practical reasons, people do need the pragmatic distinctions. I just feel that the conceptually clear ones can yield that, too. Yes, again, it may take a little more exposition, but isn’t educating folks part of the job too (e.g. good marketing is good customer education)? So, while I feel a wee bit of an old man yelling “get off my lawn”, this is the reason why I struggle with sloppy thinking. Am I off base?

We play as we practice

22 August 2023 by Clark 6 Comments

I”ve advocated, repeatedly, the importance of practice. Yet, too often, we still see an ‘event’-based model, where it’s one and done. Unfortunately, this doesn’t align with how our brains work! I was looking at one of Elevator 9‘s Liftology videos (caveat: I did the original scripting), where they mentioned ‘practice like we play’. I’d heard it before (in various incarnations), but this time it struck me that perhaps it’s the right vehicle to penetrate complacency about learning design. Should we emphasize “we play as we practice”?

The underlying phenomena is that we need lots of practice, for two reasons. For one, the ‘learning’ mechanism that strengthens our learning can only do so much before it needs sleep. If you want to truly develop a skill, sufficient practice, over time, is required. It’s like building muscle, or training for a sport; occasional practice isn’t sufficient. The right practice, repeated and improved over time, is necessary.

The other is that we are very context sensitive. That is, our consciousness is very much influenced by where and how things are happening. If you want to successfully generate transfer to many different situations (such as sales, or negotiation, or…things that happen in many different contexts with different people and different goals and…), you need sufficient practice across contexts. Our brain abstracts across the contexts seen to determine the space of transfer. Thus, we need widely varied practice to generate a generalized ability to do. 

Yet, too often, we see people getting it right ‘once’, and thinking that’s enough. It might be sufficient to tick a box, but it’s not sufficient to generate a new ability. The problem is, there’s a lot of pressure against this. Folks don’t want to take the time and money, they want to believe that new information will yield a behavior change, it’s just too hard!

So, I’m wondering if rethinking the messaging will help. If we emphasize that what we do is dependent on what we practice, maybe we can get away from the school mentality of ‘study, pass test, forget’. We want to get to the ‘practice practice practice to be good enough to play’ mentality.

I don’t know if “we play as we practice” is the best vehicle, or even one, but I’m kinda desperate, I guess. I’m very very tired of folks not getting that meaningful change requires sustained effort. And I’m really looking for a solution. It seems like this might tap into some useful mental frameworks. Can this help? If not, do you have a better solution? Please?

Don’t use AI unsupervised!

8 August 2023 by Clark Leave a Comment

A recent post on LinkedIn dubbed me in. In it, the author was decrying a post by our platform host, which mentioned Learning Styles. The post, as with several others, asks experts to weigh in. Which, I’ll suggest, is a broken model. Here’s my take on why I say don’t use AI unsupervised.

As a beginning, learning styles isn’t a thing. We’ve instruments, which don’t stand up to psychometric scrutiny. Further, reliable research to evaluate whether they have a measurable impact comes up saying ‘no’. So, despite fervent (and misguided) support, folks shouldn’t promote learning styles as a basis to adapt to. Yet that’s exactly what the article was suggesting!

So, as I’ve mentioned previously, you can’t trust the output of an LLM. They’re designed to string together sentences of the most probabilistic thing to say next. Further, they’ve been trained, essentially, on the internet. Which entails all the guff as well as the good stuff. So what can come out of it’s ‘mouth’ has a problematically high likelihood of saying something that’s utter bugwash (technical term).

In this case, LinkedIn (shamefully) is having AI write articles, and then circulating them for expert feedback. To me that’s wrong for two reasons. Each is bad enough in it’s own right, but together they’re really inexcusable.

The first reason is that they’ve a problematically high likelihood of saying something that’s utter bugwash! That gets out there, without scrutiny, obviously. Which, to me, doesn’t reflect well on LinkedIn for being willing to publicly demonstrate that they don’t review what they provide. Their unwillingness to interfere with obvious scams is bad enough, but this really seems expedient at best.

Worse, they’re asking so-called ‘experts’ to comment on it. I’ve had several requests to comment, and when I review them, they aren’t suitable for comment. However, asking folks to do this, for free on their generated content, is really asking for free work. Sure, we comment on each other’s posts. That’s part of community, helping everyone learn. And folks are contributing (mostly) their best thoughts. Willing, also, to get corrections and learn. (Ok, there’s blatant marketing and scams, but what keeps us there is community.) But when the hosting platform generates it’s own post, in ways that aren’t scrutable, and then invites people to improve it, it’s not community, it’s exploitation.

Simply, you can’t trust the output of LLMs. In general, you shouldn’t trust the output of anything, including other people, without some vetting. Some folks have earned the right to be trusted for what they say, including my own personal list of research translators. Then,  you shouldn’t ask people to comment on unscrutinized work. Even your own, unless it’s the product of legitimate thought! (For instance, I usually reread my posts, but it is hopefully also clear it’s just me thinking out loud.)

So, please don’t use AI unsupervised, or at least until you’ve done testing. For instance, you might put policies and procedures into a system, but then test the answers across a suite of potential questions. You probably can’t anticipate them all, but you can do a representative sample. Similarly, don’t trust content or questions generated by AI. Maybe we’ll solve the problem of veracity and clarity, but we haven’t yet. We can do one or the other, but not both. So, don’t use AI unsupervised!

Not Working harder

2 August 2023 by Clark Leave a Comment

Seek > Sense > Share

A colleague recently suggested that I write about how I get so much done. Which is amusing to me, since I don’t think I get done much at all! Still, her point is that I turn around requests for posts the next day, generate webinars quickly, etc. So, I thought I’d talk a bit about how I work (at risk of revealing how much I, er, goof off). It’s all about not working harder! It may be that I’m not doing a lot compared to folks who work in more normal situations, but apparently at least perceived as productive.

So, as background, I have a passion for learning. I remember sitting on the floor, poring through the (diagrams in) the World Book. My folks reinforced this, in a story I think I’ve told about how the only excuse for being excused from the dinner table was looking things up. Actually, while I did well in school, it wasn’t perfect because I was learning to learn, not to do well in school. That was just a lucky side effect. I went on and got a Ph.D. in cognitive science, which I argue is the best foundation for dealing with folks. (Channeling my advisor.)

So, I’ve been lucky to have a good foundation. I do recall another story, which I may have also regaled you with. This is about my father’s friend who succeeded in a job despite having stated to the effect that if it appeared he was asleep, he was working, and he’d still do the work of two. (He did.) The point being, that taking time to learn and reflect was useful. I did the same, spending time reading magazines with my feet up on the desk in my first job out of college, but still producing good work.

That’s continued. Including through my graduate school career, academic life, workplace work, and as a consultant. The latter wasn’t my chosen approach, it was involuntary (despite appearing to be desirable). Somehow, it became a way of life. (And I’ve realized there are lots of things I wouldn’t have been able to do if I had had a real job).  What I do, regularly, are two major things which I think are key.

The first is that I continue to learn. I read (a lot). Partly it’s to stay up on the news in general, but also try to track what happens in our field. I check in on LinkedIn, largely through the folks I follow. I’ve tried to practice Harold Jarche’s PKM, as I understand it. That is, I update the folks I follow (on a variety of media), as well as media (for instance, Twitter is dwindling and I’m now more on Mastodon).

I also allow time for my thoughts to percolate. For instance, I take walks at least a couple of times a week. I can put a question or thought in my mind and head out. To capture thoughts, I use dictation in Apple’s Notes. I also read fiction and play games, to allow thoughts to ferment. (My preferred metaphor, you can also choose percolate or incubate. ;).  I even do household chores as a way to allow time to think. Basically, it looks like I’m spending a lot of time not working. Yet, this is critical to coming up with new ideas!

I also take time to organize my thoughts. Diagramming things is one way I understand them. I blog (like this), for the same reason. These are my personal processing mechanisms. When I do presentations and write articles for others, they’re the result of the time I’ve spent here. If you look at Harold’s process, I set up good feeds to ‘seek’ (and do searches as well), I process actively, through diagramming and posting, and then I share (er, through posting) and presentations and workshops and books and…

How models connect to context to make predictions.Note that it’s not about remembering rote things, but it’s about seeing how they connect. That takes time. And work. But it pays off. I’ll suggest that turning the ideas into models, connected causal stories, helps. So, it’s about understanding how things work, not just ‘knowing’ things. It’s about being able to predict and explain outcomes, not just to tout statistics and facts.

With this prep, I can put together ideas quickly. I’ve thought them through, so I have formed opinions. It’s then much easier to decide how to string them together for a particular goal. The list of things I’ve thought about continues to grow (even if I’ve forgotten some and joyfully rediscover!). I can write it out, or create a presentation, which are basically just linear paths through the connections.

How do I have time to do this? Well, I work from home, so that makes it easier. I also don’t work a regular job, and have gotten reasonably effective at using tools to get things done. For instance, I’m now using Apple’s Reminders to track ‘todos’, along with its Calendar. (I’m cheap, so I’ve used fancier tools, but have found these suffice.) Needless to say, I’m quite serious when I say “if a commitment I make doesn’t get into my device, we never had the conversation.”

Thus, it’s about working smarter. I don’t have an org, so it’s just my practices. If you saw it, you’d see that it’s bursts of productivity combined with lots of ‘down time’. That’s hard to see, as an org, yet that’s the way we work best. As we start having tools that automate more of our rote tasks, we should retain doing creative things like painting, music, and more, not relegate that to AI. Then we can start working more like the creative beings we are, and start recognizing that taking time out for the non-productive is actually more productive. That’s how we work smarter, and are not working harder.

From platitudes to pragmatics

4 July 2023 by Clark Leave a Comment

It’s easy to talk principle. (And I do. ;) Yet, there are pragmatics we have to deal with, as well. For instance, with ‘clients’ (internal or external), giving us their desired outcomes that are vague and unfocused. We generally don’t want to educate them about our business, yet we need more focused guidance. Particularly when it comes to designing meaningful practice. How, then, do we get from platitudes to pragmatics?

To be clear, what’s driving this is trying to create practice that will lead to actual outcomes. That’s, first, because our practice is the most tangible manifestation of the performance objectives.  Also, because it is also the biggest contributor to learning actually having an impact! We need good objectives to know what we’re targeting and then the next thing we need to do is design the practice. After we design practice, we can develop the associated content, etc. How do we get this focus?

I see several ways. Ideally, we can engage with clients in a productive conversation. We can do the advocated ‘yes and…’ approach, where we turn the conversation to the outcomes they’re looking for, and ideally even to metrics. E.g. “how will we know when we’ve succeeded?” When we hear “our sales cycle takes too long” or “our closure rate isn’t good enough” if the topic is sale, there’re metrics there. If we hear “too many errors in manufacturing” or “customer service ratings aren’t high enough”, that’s quantifiable, and we have a target.

There are other situations, however. We might not get metrics, so then we might have to infer them from the performance outcomes.  When we hear “we need sales training” or “we need to review the manufacturing process” or “we need a refresher on customer service”, it’s a bit vaguer.  We should try and dig in (“what part of sales isn’t up to scratch” or “what are customers complaining about”), but we may not always have the opportunity. Still, we can make practice assignments around these. We can provide practice around the specific associated tasks.

What really is the biggest problem is ‘awareness’ courses. “I just want folks to know this.” (Which begs the question: why?) I fear that part of the answer is a legacy belief that we’re formal logical reasoning beings and so new information will change our behavior. (NOT!) It can also be because the client just doesn’t know any better, nor have any greater insight than “if they know it, it is good”. However, I still think there’s something we can do here. Even if it’s a case of ‘easier to get forgiveness than permission’.

I think we can infer what people would do with the information. If they insist we need to be aware of harassment, or diversity, or… we can ask ourselves “what would folks do differently?” One decision is to intervene, or report, or ignore. Another might be where and how to do those things. In general, even though the requester isn’t aware, there’s something they actually expect people to do. We have to infer what that can be. Then, they can critique, but it’s more effective for the organization  and more engaging for the learner. That, to me, is a reasonable justification!

Whether it’s mapped to multiple choice questions (see Patti Shank’s seminal book on the topic), scenarios (Christy Tucker is one of our gurus), or full games (I have my own book on that ;), we need to give learners practice in dealing with the situations that use the information. I think we can work from platitudes to pragmatics, and should. What do you think?

Two steps for L&D

6 June 2023 by Clark Leave a Comment

In a conversation, we were discussing how L&D fares. Badly, of course, but we were looking at why. One of the problems is that L&D folks don’t have credibility. Another was that they don’t measure. I didn’t raise it in the conversation, but it’s come up before that they’re also not being strategic. That came up in another conversation. Overall, there are two steps for L&D to really make an impact on.

Now, I joke that L&D isn’t doing well what it’s supposed to be doing, and isn’t doing enough. My first complaint is that we’re not doing a good job. In the second conversation, up-skilling came up as an important trend. My take is that it’s all well and good to want to do it, but if you really want persistent new skill development, you have to do it right! That is, shooting for retention and transfer. Which will be, by the way, the topic of my presentation at DevLearn this year, I’ve just found out. Also the topic of the Missing LXD workshop (coming in Asia Pacific times this July/Aug), in linking that learning science grounding to engagement as well.

I’ve argued that the most important thing L&D can do is start measuring, because it will point out what works (and doesn’t). That’s a barrier that came up in the first conversation; how do we move people forward in their measurements. We were talking about little steps; if they’re doing learner surveys (c.f. Thalheimer), let’s encourage them to move to survey some time after. If they’re doing that, let’s also have them ask supervisors. Etc.

So, this is a necessary step. It’s not enough, of course. You might throw courses at things where they don’t make sense, e.g. where performance support would work better. Measurement should tell you that, in that a course isn’t working, but it won’t necessarily point you directly to performance support. Still, measurement is a step along the way. There’s another step, however.

The second thing I argue we should do is start looking at going beyond courses. Not just performance support, but here I’m talking about informal and social learning, e.g. innovation. There are both principled and practical reasons for this. The principled reason is that innovation is learning; you don’t know the answer when you start. Thus, knowing how learning works provides a good basis for assisting here. The practical reason is it gives a way for L&D to contribute to the most important part of organizational success. Instead of being an appendage that can be cut when times are tough, L&D can be facilitating the survival and thrival strategies that will keep the organization agile.

Of course, we’re running a workshop on this as well. I’m not touting it because it’s on offer, I’m behind it because it’s something I’ve organized specifically because it’s so important! We’ll cover the gamut, from individual learning skills, to team, and organizational success. We’ll also cover strategy. Importantly, we have some of the best people in the world to assist! I’ve managed to convince  Harold Jarche, Emma Weber, Kat Koppett, and Mark Britz (each of which alone would be worth the price of entry!), on top of myself and Matt Richter. Because it’s the Learning Development Accelerator, it will be evidence-based. It’ll also be interactive, and practically focused.

Look, there are lots of things you can do. There are some things you should do. There are two steps for L&D to do, and you have the opportunity to get on top of each. You can do it any way you want, of course, but please, please start making these moves!

A placebo effect?

30 May 2023 by Clark 1 Comment

I was thinking about what too often we see as elearning. That is, the usual content dump and knowledge test. There’s good reason to believe that it isn’t effective. So, why are we seeing it continue? Is it a placebo effect?

I tend to view this as a superstition. That is, the belief that information presentation will lead to behavior change is held implicitly. I think it originates from a legacy perspective that we’re logical, and therefore new information will yield impact. (Not.) Regardless, it exists.

I was inclined to wonder if, really, it’s a placebo. That is, doing something with a hope that things change, but the onus is on the individual, not the intervention. There’s not going to be any actual effect, but it makes people feel better. Of course, the role is different here; the placebo makes the doctor feel better! (Or the health system? I’m muddling my metaphor…:)

It may not be that in practice, of course. There is a ‘faith’ that “if we build it, it is good”. So, biz units can ask for a course, and get one. They’ve provided content and access to SMEs. However, they push back when asked “what’s the actual problem”, let alone asked for measures.  It’s like they think the job can be done with information. They are happy with the appearance of a solution, because it’s easy, and no one’s checking.

We, of course, have to change this perception. If we continue to let folks believe they can give us content and we’ll deliver meaningful change, shame on us. Of course they don’t care about the measures, and they want things to be easy. We have to, however.  It may as well be a placebo effect, because the ultimate impact is a likely null as sugar pills, unless the patient wants to change. It’s probably not a great metaphor, but somehow it still seems apt. Thoughts?

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