Learnlets

Secondary

Clark Quinn’s Learnings about Learning

Archives for 2020

Separate content from description

by 3 Comments

Once again facing folks who aren’t using styles, I was triggered to think more deeply about the underlying principle. That is, to separate content from description. It’s a step forward in what we can do with systems to bring about a more powerful human-aligned system.

And, as always, here’s the text, in case you (like me) prefer to read ;).

I‘ve ranted before about styles, but I want to make a slightly different pitch today. It‘s not just about styles, it‘s about the thinking behind it. The point is to separate content from description.

So, the point about styles is that they‘re a definition of formatting. You have elements of documents like headings at various levels, and body text, and special paragraphs like quotes, and so on. Then you have features, like font size, bolding and italics, color, etc. And what you see, too often, is people hand-formatting documents, choosing to do headers by increasing the font size, bolding, etc. And, importantly, having to go through and change them all manually if there‘s a desire for a change in look.  

The point of styles is instead merely to say this is a heading 1, this is a figure, this is a caption, and so on. Then, you separately say: heading 1s will be font size 16 bold and left-justified. Figures and captions will be centered, in font size 12. And so on. Then, should someone want to change how the document‘s formatted, you just change the definition of heading 1, and all the heading ones change.  

It goes further. You can define that all heading ones have a page break before (e.g. a new chapter in a book). And you can define new styles, like for a callout box (e.g. colored background), etc. You can have different heading ones for a book than for a white paper. And some styles can be based on others. So your headings can use the same font as your body text, and if you want it all to change, you change the source and the rest will change.  

Which is wonderful for writing, but the concept behind this is what‘s really important to get your head around. That is, separating out role from description. That‘s what‘s led me to be keen on content systems. The notion of pulling up content by description instead of hardwiring together content into an experience is the dream.  

It‘s all about beginning to use semantics, that is the meaning of things, as a manipulable tool. Many years ago, I led a project creating an adaptive learning system. We were going to have content objects defined by topic, and learning role, and tagged in terms of media, difficulty, and more. So you could   say: “pull a video on an example of diversity set in a sales office”. Our goal, with a suite of rules about what when to move up or back in difficulty, was to specify what learning content the learner should see next, etc.  

This is how adaptive platforms work. When Amazon or Netflix make a recommendation for you, there‘s not someone watching your behavior, instead it‘s a set of rules matching your particular actions to content recommendations. If you‘ve ordered a lot of British mysteries, and you haven‘t seen a particular series that lots of other people like, it‘ll be likely to be offered to you.  

This is the opportunity of the future. We can start doing this with learning (and coaching)!   We can start pulling together your learning goals, job role, current progress, current location (in time and space), etc, and offer you particular things that are appropriate for you. And, like our learning system, it might be recommendations of content, but you can choose others, or ignore, or…As Wayne Hodgins used to say, present the ‘right stuff‘: the right content to the right person at the right time in the right way….

The point being, just like styles, if we stop hardwiring things together, hand-formatting learning experiences, we can start offering personalized and even adaptive learning. Yes, there are technical backend issues, and more rigor in development, but this is the direction we can, and should, go. At least, that‘s my proposal, what say you?

Performance Ecosystem Maturity Model

by 3 Comments

Someone on LinkedIn asked about a way to evaluate orgs on their learning infrastructure. And I had developed a Performance Ecosystem Maturity Model as part of Revolutionize Learning & Development, but…I hadn’t presented it. At least not in full.  Here I rectify that; my holiday gift to you!

(A bit over 5 mins.)  And, of course, if you want help with it, let me know! Also, the prose (and the diagram):

Script

Hi, I‘m Clark Quinn, of Quinnovation. Recently, someone asked about an organizational learning maturity model. And, coincidentally, I had one. However, it was locked away in my book from 2018. So I want to present that model, and hope it‘s of use.  

In my book, Revolutionize Learning & Development, I pushed for the performance ecosystem, going beyond ‘the course‘ to talk about all the ways that L&D that could assist organizational learning. I posit that optimal execution is only the cost of entry, and the only sustainable differentiator is continual innovation. And I argued for what that meant. I want organizations to have a concrete picture of what this looked like.

In the book I had a set of six categories, each with two components. So I created a ‘maturity model‘. The intent is to provide a tool whereby organizations can understand what the possibilities are, assess themselves, and prioritize directions to move and improve. Let‘s be clear, this is conceptual, not empirical. This is a proposal about what should be the elements, and an effort to consider what fits in each stage of each component.  

The first area is culture. How we‘re learning, and what our orientation is. And you‘ll see as we fill out the table, what this means. Starting with learning, we transition from a competitive environment, (which is unhealthy), through being willing to cooperate (say for team projects), to where we‘re collaborating of our own initiative, ending up where we‘re working to also improve our processes as well as our outcomes. And our orientation shifts from learning for our own benefit, to learning as an organization, then to where we consider the impacts on society, ending up where we‘re looking to ensure this works across those elements.

Then we look at the formal learning elements, how we design and who‘s using the learning. We start with the traditional knowledge dump, then on making it more engaging and lean, to where we‘re focusing on making it meaningful to the org and the individual, and finally creating transformative experiences. And we look at who‘s using the learning. It starts with taking orders for courses, through having some times where the effort is to truly address a need, to where our efforts are aligned with key organizational needs, and finally where we‘re being discerning about methods, measures, and outcomes.

We then consider going beyond the course to times when knowledge can be in the world, not in the head. We worry about resources and the ways people can get access to them. Instead of idiosyncratic aids, we move to having L&D creating support, and then recognizing the value of curation, and finally with a mixed initiative with everyone contributing. And we move from the siloed access to these resources, through an intermediate stage of some focused development, to a user-centric access based upon the users needs (not the originators location), ending at a dynamic matching of need to resource.  

We also start looking at informal learning, the continual innovation. This includes both the status of networks and the usage of those networks. It starts where if there‘s network usage, it‘s not org-originated, through some units having a system, to having an organizational focus on communication, and then actively facilitating social learning. And we move from the Miranda organization (where anything you say can and will be used against you), to where some folks are experimenting, to where people are comfortable contributing, and finally where folks recognize that active engagement is key to organizational thrival.

We also consider the use of metrics in the organization, both to evaluate and to improve. We should move from just looking at the cost of a butt in a seat for an hour to whether that time is benefiting the org. Then we incorporate informal learning, and finally where measurement‘s naturally part of our practices. We move our focus from internal to a broader picture, and start evaluating our processes themselves, and move to looking at these as a synergistic whole  

Finally, we look at our infrastructure, our underlying approach and our systematicity. We move from handcrafted solutions to systemic approaches, then looking to incorporate semantics, and finally looking at systems with emergent capabilities. And we move to look at our approaches as platforms, and then integrate those platforms, and finally allow our systems to adapt as conditions change.  

This is an updated version of the model in the book, and I think provides a useful framework for thinking about L&D. I hope this made sense, and is of interest. And I welcome feedback! Thanks for listening. You can find out more at Quinnovation.com, and at revolutionizelnd.com.  

Five trends for 2021

by 2 Comments

As frequently happens, I get asked for my predictions. And, of course, I have reservations. Here’s a video that provides the qualifications, and five trends for 2021 that I’d expect, or like, to see.

And the script:

Hi, I‘m Clark Quinn, of Quinnovation (a boutique learning experience design strategy consultancy). I was recently asked about what trends I thought would be seen next year.  

Two relevant quotes to set the stage. For one, Alan Kay famously said “the best way to predict the future is to invent it.” So I tend to talk about trends we should see. The other is “never predict anything, particularly the future.” I heard an expert talk about having looked at predictions and outcomes, and the noticeable trend is that it went as expected, with one unforeseen twist. So, expecting I‘ll get it wrong, here are some trends I‘m either expecting or keen to see:

The first trend I‘m seeing and think will continue is an emphasis on learning science. And that‘s all to the good! Admittedly, I‘m part of this, what with running a course on learning science and having a forthcoming book on the topic. But I‘m seeing more and more people talking about it, and not all hype and even mostly right! There are more books, the Learning Guild‘s regular research reports are good, the launch of an event past summer and an associated new society focused on evidence-based learning (the Learning Development Accelerator) are all signs of growing momentum.

Second, when there‘s a lot of hype about something, it tends to be followed by a backlash.  This may be farther out than 2021, but with all the buzz about AI, I think we might see some more awareness of limitations. Yes, it can do some very useful things, but it also isn‘t a panacea. We‘re seeing a growing awareness of the problems with bias in data sets, the limitations of ungrounded knowledge, and concerns about the human costs.  

Three. On a related note, then, I expect more emphasis on the importance of meaningful practice. This comes from learning science, but also the focus on engagement. Thus, the push for Short Sims, and better written multiple choice questions, and in general a focus on ‘do‘, not know.   Hopefully, we‘ll see tool vendors aligning their content and assessment capabilities towards designing scenarios and contextualized practice, along with specific feedback for each wrong answer and support for reflection.

Fourth, I hope for a push towards content systems as well. This, too, may not be in the short term, but ultimately we have to realize that hardwiring experiences may make sense for formal systems, but not for adaptive learning.LXPs are a good move here, even if misnamed (really, they‘re smart portals, not learning experience platforms). Ultimately, we‘ll be better off if we can deliver content by description and rules, like recommendation system, rather than by having to handcraft content to create a ‘one-size fits all‘ solution.  

Finally, I think that our collaboration tools haven‘t lived up to the promise of technology. They‘re very much oriented towards particular modes, instead of supporting really rich interaction. This, too, is more long term, but we really should be able to talk together while working to create representations that capture our evolving thinking. Easily and elegantly! There‘s real opportunity here to engage multiple representations in an elegant suite.  

So there you have it, a wishful list of five trends for 2021. So what do you expect, or hope, to see?

Foundations of Learning Science

by 1 Comment

Another video, this time (ok, again ;) about learning science.

They like me to do this to push the course, but I did hear the feedback on LinkedIn that the video format works. Nice to know. As always, also the script.

And, announcing one other thing…

I‘ve argued in an earlier post for the value of learning science, but I want to go a little deeper. I want to talk a little about the evolution, and a little bit about what‘s involved. It‘s about establishing the foundations of learning science.

And I‘ve mentioned in a previous post  that learning science is interdisciplinary, and relatively new. While education had been proceeding for a long time, the approaches were ad hoc. Experimental science itself didn‘t emerge ‘til the medieval ages at the earliest. My take is that most of school still replicated what had been done since the Prussians invented school, somewhat modeled on religious lectures. The notion of scientific education had yet to emerge.

The first real systematic study of learning came from the field of educational psychology. Here, the focus was on schooling, and included cultural and motivational factors.

A different approach came from behavioral psychology roots. During World War II, the military was faced with training many soldiers, and behavioral psychologists created the field of instructional design. Here, the focus was more on training, including the influence of media and elements of instruction.

Learning science as a field was arguably created when the International Society for the Learning Science was created in the 1990s, perhaps sparked by the creation of the Institute of the Learning Sciences at Northwestern University. This is an integrative approach that looks beyond schools and training to more forms of learning including informal learning and even machine learning.

Having been involved in one way or another with all of these, I tend to create a synthesis. I think the care is cognitive science: how we process information. While there are neural underpinnings, most of the results and prescriptions operate at the cognitive level, or above. Within the information processing cycle we cover core processes like attention, elaboration, and retrieval. This is our core mental architecture.

Interesting results for learning emerge from this architecture, including the role of models and examples, and the core importance of practice. A post-cognitive perspective reflects that our thinking isn‘t formally logical, but instead is emergent, distributed, and social.

Two other important areas are the emotional aspects of learning, and meta-learning. The former is more the conative area of intent to learn, e.g. motivation and anxiety, rather than the affective area of personality. The latter has to do with learning to learn, including looking at our own learning processes.

All these affect the elements that contribute to learning. Our introductions, concepts, examples, practice, and closings should reflect what‘s known about learning. And the components of science and engagement need to be elegantly integrated to yield the best outcomes.

Of course, these foundations of learning science are what I cover in the learning science courses I‘m offering through HR.Com and the Allen Academy. Stay tuned for more ;).

Measuring Impact (or not)

by 6 Comments

So I saw a twitter thread pointing to an argument about how ROI is dead. And, well, that’s largely okay with me. However, the trigger for the post was from the results of Chief Learning Officer 2020 State of Learning report.  And, when I saw them, I saw some problems. The question is whether we’re measuring impact, or not. I’d like to go through them and evaluate each.

(Back to my usual prose, as I need visual support for this. ;)

So, in the report, they indicated that the respondents indicated the demonstrated impact of training in these ways:

  • General training output data
  • Training output data aligned with corporate initiatives
  • Learner satisfaction with training
  • Employee satisfaction with training availability
  • Employee engagement
  • Business impact
  • Employee performance data
  • Planned to actual budget, expense, revenue data for training group
  • Stakeholder satisfaction with training data
  • ROI measures
  • Net promoter score
  • Employee satisfaction with company data

Yikes!  Some of these are problematic at best.  Let’s look at why some of these might not be good measures of impact. And, let’s be clear; impact should be about positively affecting the organization in a meaningful way. Moving needles like fewer errors, more revenue, reduced costs, happier employees and customers, etc.

So, first, what  is  general training output data? If it’s like what I saw in (then) ASTD’s State of the Industry report, it’s metrics like employees served per L&D employee or cost/seat/hour for training. Which might a useful measure of efficiency, if you can come up with a principled basis for what a good number would be, and then see if you’re above or below that. Unfortunately, what people do is just compare themselves to the industry average. Is that a good indicator? How do you know? Do you want to be just ‘better than average’?

Then, training output data  aligned with corporate  initiatives.  Again, hard to say what this means (and I can’t seem to find the report). However, it sounds like it’s still efficiency, just doing that for things the business thinks are important.

And we go worse: learner satisfaction with training? Er, research I’ve read and heard cited (I think it’s from Salas, et al, but memory fails)  says that’s not valid. There’s a .09 correlation between what learners think of learning impact, and it’s actual impact. That’s zero with a rounding error. That’s all about making learning ‘fun’ (instead of ‘hard fun’). Yes, you do want them to think it’s  also been a good experience if you’re focusing on LXD, but that’s secondary.

Similarly, with satisfaction with training availability. What’s that matter? That’s not  impact!

Some good things buried here: employee engagement should be good; more engaged employees is a good thing. As long as it’s not at the cost of something else, like, say, impact?  And business impact is obviously good, as is employee performance data. Presumably positive business impact, and employee performance improving.

Planned to … stuff is all about efficiency again. And that’s ok, but only  after impact. Otherwise, well, we’re not  costing too much…?!?

Satisfaction again not good.

And, to the original point of the article. ROI?  Yes, what it costs you to move a needle should be less than the cost of what the needle was costing you.  However, I could be doing things that return the biggest ROI without doing the most important things. They can be different (e.g. a small program with a better ROI but less overall impact). So it’s only secondary.

Finally, employee satisfaction with company data? I have no idea what that means? But, again, ‘satisfaction’ isn’t really meaningful unless it’s based on real impact.

I’ve complained before about L&D measurement. Here it is, right in front of us. The answer to the question of whether we’re measuring impact or not appears to be ‘mostly not’.  We’re still (largely) measuring the wrong things. And we wonder why we don’t have credibility. Please, please, start designing to improve measurable gaps, and then actually improve the outcome. Otherwise, you’ve no idea whether that bum in that seat for an hour is doing the organization any good, versus just not costing too much relative to the industry average.

AI and Meaningful Practice

by 3 Comments

Again, a video of an idea I want to talk about. This time about AI and Meaningful Practice (just around 2 minutes). I welcome your thoughts.

By the way, I’m experimenting with video as a blog mechanism. A colleague mentioned that no one remembers the author of an article or post, but they do remember the speaker in a video. And much as I hate to do it, I do want people to associate my ideas with me! I welcome, very much, your feedback on this too!

Script:

Hi, I‘m Clark Quinn, Executive Director of Quinnovation (my vehicle for learning experience design strategy).    Today I want to talk about an insight I had, sparked in a conversation I had with a colleague.  We were talking about learning (of course), and the difference between knowledge versus practice.

I was reminded that we‘re now seeing AI technologies that can parse content and then answer questions about it.  We even see ones that can ask you questions about the content!  Which is part of learning.  But not all.

My realization was that, increasingly, these systems will take over this form of content presentation.  That is, we‘ll write a white paper, and an AI will parse it, then present it, and drill it.  Which is, after all, way too much of corporate learning.  See, for instance, the Serious eLearning Manifesto as a response.

Now, I‘ve always maintained that such systems aren‘t sufficient for real learning.  Meaningful learning includes more: motivation and contextualized practice.  Content presentation and quiz questions may be necessary, but by no means are they sufficient.  And that for now and the foreseeable future, AI will not be able to create those elements.  

This is the job of LXD: integrating learning science and deep engagement into experiences that transform us.  Which means that what L&D needs to do is stop doing information dump and knowledge test,  and learn how to do real learning experience design!  That, I suggest, is a noble pursuit (and, to be fair, what we should have been doing all along).  

Of course, there‘s also the necessary new role, per my last post/video, of being a facilitator of informal learning.  Coupling the optimal execution with continual innovation.  But, for now, I‘m suggesting we truly have to master everything that makes learning work, in particular meaningful practice.

That‘s my take, I welcome hearing yours.  Thanks for watching!

 

The Future of L&D? A pitch

by 1 Comment

I was talking with a colleague the other day, and got a wee bit dramatic. I also thought it was an important point. So here, for your dining enjoyment, I’ve roughly recreated the pitch (in 3 mins and 30 secs):

I hope this makes sense. I welcome your thoughts and feedback.

The Script:

Hi, I‘m Clark Quinn, of Quinnovation, and I‘ve been around the elearning for well-nigh forever, and around L&D for the past couple of decades.  So…I joke that:

L&D isn‘t doing near what it could and should, and what it is doing, it‘s doing badly.  Other than that, it‘s fine.  

Seriously, I think there‘s the obviously important role for L&D,  but also a really important opportunity.  

Things aren’t getting any simpler.    We‘re facing increasing complexity and uncertainty.  And, going forward, I suggest, optimal execution is only the cost of entry.  Continual innovation will be the necessary differentiator.  That is, we will have to do well what we know we have to do,  but we also have to become agile, nimble, and able to pivot in the face of change.  So that means doing courses right, when courses are the answer.  That‘s the optimizing role, going beyond being efficient to being effective.

And it also means that organizations will have to get good at problem-solving, research, design, and more.The thing is with those things, when you start you don‘t know the answer.  That is: They. Are. Learning!  And that is the important opportunity.

Going back to being effective, that means that when we design courses, we need to effectively integrate learning science with true engagement.  Deep LXD,  not tarted up quiz shows and ‘click to see more‘. And, we should only do that when it‘s the right answer!  It‘s not ‘we need a course on this‘, but instead  “we can identify that we have a skills gap and we need to improve our performance”.

And then, it‘s about facilitating social and informal learning:  tapping into the power of our people, creating a learning culture, assisting the organization in systematic in good practices.  

How do we get there?  I argue there are two major steps.  First, we need to measure,  and here I mean more than just efficiency.  It‘s not how much it costs to have a bum in a seat for an hour,  but instead whether that bum in that seat for that hour does the organization any good.  Right now, we don‘t know whether our efforts are really moving any needles.  It‘s a matter of faith that if it look like school, it must be learning.

Second, it means we have to start practicing those principles within L&D: smart experimentation; collaborating; and learning continually and out loud.  We can‘t have credibility if we haven‘t walked the walk.It won‘t happen overnight.  We‘ll have to build back our reputation as scrutable practitioners.  We‘ll have to continually educate.  And likely have to do the ‘better to seek forgiveness than permission‘.

Here‘s the vision I see.  When we‘re not only ensuring good execution on what we know we have to do,  but are responsible for the ongoing success of the organization,  we‘ve moved to an indispensable position.  We‘re key to success in the toughest times!  As key as IT and Finance.  Other groups can and will take it on if we don‘t  but we‘re supposed to be the ones who understand how we learn.  And learning, going forward, is the key to not just surviving, but thriving.  Our orgs need it, the employees need it, and our professional standards demand it.  So let‘s do it.    Let‘s reengineer our status in, and value to, the organization.

Thanks for listening.

Flow, workflow, and learning

by 3 Comments

On LinkedIn, a colleague asked “Why do people think that integrating content in the flow of work equals learning in the flow of work?” An apt question. My (flip) response was “because marketing”. And I think there’s a lot to that. But, a comment prompted me to think a little bit deeper, because ‘flow’ is its own meaningful concept and we need to be careful about meaning. So here are some reflections on flow, workflow, and learning.

The response that triggered my reflection was:

I can’t recall the last time I told someone that I was in the “flow” of work today and learned so much!!

Flow state(Which is pretty funny!) The comment was a bit pointed, but it made me think about being in the ‘flow’ state, and the relationship with learning. I’ve previously pointed out how Csikszentmihalyi’s Flow and Vygotsky’s Zone of Proximal Development  (ZoPD) are essentially the same. If the difficulty is too far above your skill level, the experience is frustrating. If it’s too easy, it’s boring. And in between is the flow state, and where learning happens.

Now, when we’re in the ‘flow’ at work (which is different than being in the workflow), we’re performing optimally. And I’m not sure learning happens there. Similarly with the ZoPD. You’re working and I’m not sure learning happens  then. When I state that learning is action and reflection, I think reflection is a necessary component.

Now, the original complaint talked about learning in the workflow, and opined that content in the workflow won’t necessarily equal learning. Another comment pointed out what I believe is often conflated with “workflow learning”, and that’s performance support. There are lots of reasons that we might want content in the workflow to help us succeed, but it may have nothing to do with learning. If, indeed, learning is to happen, it might need some content, and feedback, and so actually break the flow!

Now, I also recognize that many times we’re in the flow of work, but not in the ‘flow’  zone. So, we could definitely be learning in the workflow. And it happens by deciding to look up the answer to some contextually relevant question. Or from a comment from a person. But it’s a bit different than being in the zone, and we’d like to be there in our work too!

And, I wonder whether Vygostky’s ZoPD really aligns with the Flow Zone, or if it needs to be coupled with some offline reflection. It’s certainly possible. Maybe the flow zone is a superset of the ZoPD. More to ponder.

There isn’t a real revelation here about flow, workflow, and learning, other than we have to keep our concepts straight. We need to recognize when we’re supporting performance, and when we’re learning. And we need to be clear about workflow, and being in the flow zone. And there may be more here to unpack. Thoughts?

 

In Defense of Science

by 3 Comments

I previously wrote in defense of cog psych. Here, I want to go broader. Not my usual topic, but… I feel the need to rail in defense of science.

When I’m talking about science, I’m talking about the systematic exploration of how things work. It’s about theorizing, and testing, and refining. It’s about having rigor in that, being systematic and principled. And, importantly, it’s about sharing the results and building on the works of others. Interestingly, it’s  not a human universal, but instead emerged relatively recently. However, there’s a reason it’s been so successful.

And, let’s be honest, science has flaws. There have been recent problems in replicability. Another problem is academic politics; new ideas can struggle to get recognized. Pressure to publish can lead to fake data. Folks with money can influence what research gets done, and the outcomes. Similarly, politics can play a role. Like democracy, it’s not perfect, but…and this is an important  but…there’s nothing better.

The evidence for science are the things that we’ve come to count on: sanitation, transportation, medicine, the list goes on. Your ability to read this depends on science. Most of what we use all day every day has been improved by science. So, too, some bad things, like how successful marketing can be at snagging your attention. Yes, we need to use it wisely. Science can help there, too! And, importantly, most scientists are ethical, caring, diligent individuals. They do what they do  for science, not for wealth, not for fame (except amongst their colleagues, which goes back to doing it  for science), and most certainly not to support conspiracies.

So, trying to pick and choose what science to believe isn’t a great bet. Unless you have a deep background in a particular domain, trying to ascertain the validity is challenging. You may listen to disparate voices, but not if they’re flying the face of a concerted viewpoint of people who have spent the requisite time to be true experts. In my mind, you’re either for science, or not. Saying “well, I’m not for  this science because someone said it’s controversial”, then, is just not on.

Yes, there are controversies around most science:  that’s how it advances. But there’s also essential truths that most every reputable scientist in the field will agree to. And that’s how we build products, services, and ultimately societies.  I was trained as a scientist (though I’m more of an engineer, tracking it and applying it to solve real problems). I know, in my field, what makes sense and what’s silly. And then, in other fields, I look to what the received wisdom is. And I know what sorts of people to listen to, and it’s not politicians, or pundits. Unless they listen to science.  The best guidance comes from the folks who know the field in question. And that holds true for medicine as well as meteorology.

And sure, I too could wish I lived in a world where magic worked. But if you think about it, they, too, use systematic experimentation to find out what works. Whether Earthsea or Hogwarts, they go to schools to learn and there the professors are studying. But here, magical thinking doesn’t work. Science is what has let us knock back polio, generate electricity from sunlight, and walk on the moon.

So, if you do want to go against what the scientists or reliable interpreters tell you, don’t do it piecemeal. Abandon all the science, because you’re unlikely to get it right in a domain that’s not your expertise. If  anyone is telling you contrary to what’s known, question their motives! People mislead for lots of reasons, from money to mischief. If you let them, they’ll hurt you in ways that may be stark or subtle.

If they’re steering you away from something that has been shown to be better than the alternative, you should be wary. Their tricks are myriad: lack of context, distortions, selected subsets, and outright lying. For instance, our brains are wired to see patterns. If we’re pointed to them, we’ll see them. We’re also biased to look for evidence that confirms our beliefs, and avoid what contradicts it. Thus, it’s easy to gin up potential conspiracies, despite the incredible challenge in actually pulling them off!

I’m putting it out there. I can say that, in defense of science, it’s better than any other approach. That’s my stance, what’s yours?

 

Ritual

by 2 Comments

I’ve talked before about the power of ritual, but while powerful, it also seemed piecemeal. That is, there were lots of hints, but not a coherent theory. That has now changed. I recently found a paper by Nicholas Hobson & colleagues (Schroeder, Risen, Xylagatas, & Inzlicht; warning, PDF) titled  The Psychology of Rituals  that creates an integrated framework. And while my take simplifies it down, I found it interesting.

At core, what the model suggests is that there are two components that are linked together. The first element are things that involve the senses. The second element are the semantics we’re looking to create allegiance and adherence too. And there are important elements about this relationship.

There are a number of elements that are on tap for involving the senses. Certain movements, sounds, and words said or to be spoken can be used. There can also be food, drink, smells, and more. Objects also. Timing is an element; at the micro level of things in order, and at the macro level of the triggers for the ritual.

Semantics come, of course, from your needs. It can be about things you want people to believe, or a set of values you want people to subscribe to. Or, of course, both. From the design purpose, I’d suggest it’s about agreeing to be a member of a community of practice; to undertake certain actions when appropriate, and to uphold certain values.

Interestingly, according to their model, the relationship between the two is effectively arbitrary. That is, there is no intrinsic relationship between what you’re signifying, and how you do so. Rituals are about the practices. Which means you could in theory do just about  anything to make the relationship.

The other thing is that the ritual has to be invariable in its aspects. You define it, and so do it. Note that the execution can vary considerably; from several times a day to upon certain triggering conditions. So, for instance, having completed a course, or before engaging in certain activities.

While such a definition gives us lots of freedom, it also doesn’t necessarily serve as a guide for design. Still, thinking about it in this way does suggest the utility in developing deeply held beliefs and appropriately practiced behaviors. At least, that’s how I see it. You?