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Tools for LXD?

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I’ve been thinking on LXD for a while now, not least because I’ve an upcoming workshop at DevLearn in Lost Wages in October. And one of the things I’ve been thinking about are the tools we use for LXD. I’ve created diagrams (such as the Education Engagement Alignment), and quips, but here I’m thinking something else. We know that job aids are helpful; things like checklists, and decision trees, and lookup tables. And I’ve created some aids for the Udemy course on deeper elearning I developed. But here I want to know what  you are using as tools for LXD? How do you use external resources to keep your design on track?

The simple rationale, of course, is that there are things our brains are good at, and things they’re not. We are pattern-matchers and meaning-makers, naturally making up explanations for things that happen. We’re also creative, finding solutions under constraints. Our cognitive architecture is designed to do this; to help us adapt to the first-level world we evolved in.

However, our brains aren’t particularly good at the second-level world we have created. Complex ideas require external representation. We’re bad at remembering rote and arbitrary steps and details. We’re also bad at complex calculations.  This makes the case for tools that help scaffold these gaps in our cognition.

And, in particular, for design. Design tends to involve complex responses, in this case in terms of an experience design. That maps out over content, time, and tools. Consequently, there are opportunities to go awry. Therefore, tools are a plausible adjunct.

You might be using templates for good design. Here, you’d have a draft storyboard, for instance, that insures you’re including a meaningful introduction, causal conceptual model, examples, etc. Or you might have a checklist that details the elements you should be including. You could have a model course that you use as a reference.

My question, to you, is what tools are you using to increase the likelihood of a quality design, and how are they working for you?  I’d like to know what you’ve found helpful as tools for LXD, as I look to create the best support I can. Please share!

Clear about the concept

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I went to hear a talk the other day. It was about competency-based education (CBE) for organizations. Ostensibly. And, while I’m now affiliated with IBSTPI, it’s not like I’m a competency expert. And maybe I expect too much, but I really hope for people to be clear about the concept. Alas, that’s not what I found.

So, it started out reasonably well, talking about how competencies are valuable. There were a number of points, and many made sense, although some were redundant. Maybe I missed some nuance? I try to be open-minded. It’s about creating clear definitions of performance, and aligning those with assessments. Thus, you’re working on very clear descriptions of what people should be doing.

It got  interesting when the speaker decided to link CBE to Universal Design for Learning (UDL).  And it’s a good program.  UDL talks about using multiple representations to increase the likelihood for different learners to be able to comprehend and respond. This, in the talk, was mapped to three different segments: engaging the learners in multiple ways, communicating concepts in multiple ways, and allowing assessment in multiple ways. And this is good. For learning. Does it make sense for CBE?

To start, the argument was, you should make the rationale for the learning in multiple ways. While in general CBE inherently embodies meaningfulness in the nature of clear and needed skills, I don’t have a problem with this. I argue you should hook learners in emotionally  and cognitively, and those can be separate activities. There was a brief mention of something like ‘learning styles’, but while now wary, I was ready to let it go.

However, the talk went on to make a case for multiple representations of content. And here the slide  explicitly  said ‘learning styles’ and used VARK. And don’t get me wrong, multiple representations and media are good,  but not for learning styles! The current status is that there’s essentially no valid instrument to measure learning styles, and no evidence that even if you did, that it makes a difference. None. So, of course, I raised the issue. And we agreed that maybe not for learning styles, but multiple representations weren’t bad.

The final point was that there could be multiple forms of assessment. At this point, I wasn’t going to interrupt again, but at the end of the session raised the point that the critical element of CBE is aligning the assessment with the performance! You can’t have them do an interpretative dance about identifying fire hazards, for instance, you have to have them identify fire hazards! So, here the audience ultimately agreed that variability was acceptable  as long as it measured the actual performance. Again, I don’t think the speaker was clear about the concept.

There were two major flaws in this talk. One was casually mashing up a couple of essentially incommensurate ideas. CBE and UDL aren’t natural partners. There can be overlapping concepts, but… The second, of course, is using a popular but fundamentally flawed myth about learning. If you’re going to claim authority, don’t depend on broken concepts.

To put it another way, I think it’s fair to expect speakers to be clear about the concept. (Either that, or maybe the lesson is that Clark shouldn’t be allowed to listen to normal speakers. ;)  Please, please, know what you’re talking about before you talk about it. Is that too much to ask?

Craft and commercial?

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Occasionally I try to look at the broader swings we see (in a variety of things). In learning technology, there’s been a gross pendulum swing, and maybe smaller ones. I think we’ve swung between craft and commercial approaches to design, and I’m hoping we’re on a return swing.

When we first started playing with learning technology, every approach was pretty much hand-crafted. We didn’t have specific tools for learning outcomes, and we had to apply generic tools like computer systems and the like. Early approaches like Plato were custom crafted, and the individual applications on top of that. And a small industry was built upon this basis to build solutions at scale, but the market never emerged. The whole solution was too costly, despite the power.

The PC revolution initially meant individuals or small teams built solutions. There did emerge authoring systems (e.g. Pilot) and even a meta-language for developing human-computer learning interactions. However, the usage was small. People handbuilt things like games (e.g. Robot Odyssey and SnooperTroops), though a few companies arose to do this systematically.

As technology changed, so to did the platforms. Video discs and Computer- and then Web-Based Training emerged. Companies emerged to do them at scale, but things were changing rather fast. Flash came about as a web-based lingua franca, where programs could run in most browsers with a plug in.  And, specifically for learning, Authorware became a powerful tool.

Still as things changed quickly, most solutions were driven by a real need, and hand-crafting was the norm.  But, of course, this changed.

With the horrors of 9/11, travel went from an increasingly affordable luxury to undesirable. The demand came for ‘elearning’, reducing costs from travel and overhead. With it came tools that made it easy to take content, add a quiz, and pop it up on a screen. A shift came from quality to quantity.

And this has continued in many guises. The difference, I  hope, is that the pendulum is swinging back.  The signs I see are an increase in interest in learning science. Several contributions may come from the Guild’s DemoFest, Julie Dirksen’s Design for How People Learn, Will Thalheimer’s Debunker Club, and the Serious eLearning Manifesto. We’re learning more about good design, and more people are picking up on it. We’re talking learning experience design, integrating learning science with engagement.

If you look at other industries like automobiles, we went from craft to commercial (c.f. assembly line manufacturing). While we’re unlikely to go back to fully crafted, owing to safety regulations, we’re seeing more options for establishing individual representation. And in furniture and clothing we’re seeing more craft.

The quality is important, and if we swing back to craft now, maybe when we swing back we’ll be commercial reflecting learning quality, not expediency. In some sense it doesn’t matter between craft and commercial, as long as it’s good. And hopefully that becomes a defining characteristic of our industry. Fingers crossed!

LXD Roles

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In my thinking about LXD strategy, I also was thinking about what roles are necessary. While you can do handoffs, what are the core skills you need to make this happen? And it’s not that you need all these people, but you need these roles. Someone may be a polymath (though I’m somewhat resistant that one person can do them all sufficiently well), but it’s really not fair to expect someone to be a one-person shop. So what  are the core LXD roles?

At core are two component skills.

Designer: this person looks at the performance need, and validates that this is a role for a learning experience, and then designs it, including any tools that don’t exist. Interacting with SMEs is a component; digesting down into the critical decisions, and then embedding practice in contexts. It’s creative, and while drawing on the strengths of the team, it’s the source of engagement strategy as well as learning outcomes. Also determining data collection and interpretation around design success. So it’s ID, and more.

Technologist: ensures that the developed media are integrated and delivered. This includes being able to use AR and VR when necessary, and string together the media and embed appropriately. This isn’t necessarily a programmer, but instead is technically capable. Such as putting in xAPI statements.

Then, there’s the media.  All of them!  So we’re talking a variety of roles here.

Writing: this is writing both to read  and to hear.  Dialog is different than prose, for one.  And prose for reading is different than, say, academese! It’s about taking the prose and boiling it down. I may have previously recounted how on an early project, my dev partner hired a script writer. That person took my elegantly crafted prose and hacked it up by 30-40%.  All to the good. I now can do the same on pretty much anyone’s (including my own). We often think we can write, but there’s as much skill in writing well as there is any other media production. Like, say,…

Graphics: this is about both images and and  graphics. Again, different, but I’d expect someone to be able to generate a diagram or infographic, but also source and masterfully integrate any images. It includes knowledge about fonts and colors and space and how they generate thoughts and feelings. It’s look and feel, and more.

Video: here we’re not talking about script writing (see above) nor acting, but instead filming and directing. Creating dynamic visuals. That is, knowing how to produce video so that what comes out looks professional and meets the need. Lighting, editing, and more.  It’s not just the filming. There are two different skills here (look at the credits in movies!), but I think we can roll them up. And it can be inserting images, animations, etc too. Video, here, includes compiling what ends up on screen. This may overlap with…

Audio: this is similar to video, but works  with it. It’s about synching the audio, but also music, and sound effects, and combining them in a way that ends up working with the video to produce an output. It can be standalone as well, say with a podcast. It’s about microphones, clips, and more.

Two additional roles (that might be combined).

Resource coordinator: this person is a bit like a project manager, but is responsible for finding images, actors, and any other resources as well as permissions. This person is resourceful and savvy at negotiating bureaucracies as well as intellectual property rights.

Project manager: in my best projects, there has been a project manager who’s developed and maintained the project schedule. This person knows when to prompt about upcoming deadlines, and chase malingerers.

And, of course, oversight.

Leader: someone who knows a bit about all of it, and can review and provide feedback. The development of the individuals should come from the assignments and the feedback. There’s also a role for collecting data about performance as a basis for strategy setting and tracking. And, of course, creating a culture of safety coupled with accountability, and experimentation.

 To be clear, not all of these are necessarily dedicated to the one project, as the categorization suggests. Project managers often are responsible for several (read: too many) projects, and media production may be a central resource. The point is that you need the required expertise, not to be winging it. And I know far too many folks are expected to do so. Yet I worry that the resulting output may not actually be effective.

And this is in a context, assuming there’s performance consulting up front, and a coaching and development program (and resources) in place at the backend. Ongoing development is part of the design, but at some point that gets handed over to the community of practice and manager/leader ongoing activities. Determining that point is part of the design. So too ensuring that there’s a sufficiency of self-learning resources and their use is modeled.

These are the LXD roles I think are necessary. I’m sure I’m forgetting something, so I welcome you weighing in.

LXD Strategy

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In the continuing process of resolving what I want to do when I grow up (rest assured, not happening), I’ve been toying with a concept. And I’ve come up with the phrase: Learning Experience Design (LXD)  Strategist. Which of course, begs the question of just what LXD strategy  is. So here’s my thinking.

To me, LXD is about the successful integration of learning science and engagement. Yes, cognitive science studies both learning and engagement, but in my experience the two aren’t integrated specifically well. You either get something flashy but empty, or something worthwhile but dreary dull. I remember a particular company that produced rigorous learning that you’d rather tear your eyes out than actually consume. And, similarly, seeing an award winning product that was flashy, but underneath was just drill and kill. For something that shouldn’t be.

Learning experiences should emotionally hook you (e.g. ensuring you know that you need it, and that you don’t know it). Then it should take the necessary steps such as sufficient spaced meaningful practice resourced with appropriate models and examples and specifically feedback. Ultimately, it should transform the learner. Learners go from not having a clue to having a basic ability to do  and how to continue to develop.

What is LXD  strategy?  Here I’m thinking about helping orgs restructure their design processes, and their org structure, to support delivering learning experience designs. This includes ensuring up front that this really does deserve learning instead of some other intervention, such as performance support. Then it includes how you work with SMEs, how you discern key decisions, wrap practice into contexts, etc. It’s also about using the tools – media and technology – to create a well-integrated experience. Note that the integration can include classrooms, ambient content and interactivity, and more. It’s about getting the design right, then implementing.

LXD strategy is about ensuring that resources and practices are aligned to create experiences that meet real org needs under pragmatic constraints. That’s what I’ve been doing in much of my work, and where my interests lead me as well. And it’s still a part of the performance ecosystem. Understanding that relationship is critical, when you start thinking about moving individuals from novices, through practitioners, to expertise. And the numbers of areas that will need this are going to increase.

LXD is, in my mind, the way we should be thinking about ID is now as LXD. And we need to not only think about what it is, and how to do it, but also how we organize to get it done. That, I think, is an important and worthwhile endeavor. So, what’s  your thinking?

Level of polish?

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A debate broke out amongst some colleagues the other day about the desirable level of polish in our elearning. One colleague was adamant that we were undermining our position by using low quality production. There was a lot of agreement. I had a slightly different view. Even after finding out he was talking more about external-facing content than internal, I still have some differences. After weighing in, I thought it required a longer response, and of course it has to go here.

So, the main complaint was that so much elearning looks dated and incomplete. And I agree!  And others chimed in that this doesn’t have to be, while all agreed that it doesn’t need to approach game quality in effect. Then, in my mind, the question switches to “what is good enough?” And I think we do need an answer to that. And, it turns out, to also answer “and what does it take?”

What is good enough?

So, my first concern is the quality of the design. My mantra on design states that it has to be right first. Then you can implement it. If it isn’t right from the get-go, it doesn’t matter  how you implement it. And the conversation took some time to sort this out. But let’s assume that the design’s right. Then, how much production values do you need?

The original complaint was that we’re looking slack by comparison. When you look at what’s being done in other, related, fields, our production values look last decade, if not last century!  And I couldn’t agree more. But does that matter?  And that’s where we start getting into nuances. My bottom line question is: “what’s the business case?”

So, I suggest that the investment in production values is based upon how important the ‘experience’ is. If it’s internal, and it’s a critical skill, the production values should be only enough to ensure that learners can identify the situation and perform appropriately (or get feedback).  It needs a minimum level of professionalism, and that’s it.  If you’re selling it to high-end customers and want to charge a premium price, you’ll need much more, of course.

The issue was that we’re losing credibility if we don’t approach a minimal level of competency. There were many arguments about the locus: fear of going out of bounds, managers oppression, low level tools, lack of skills, and more. And these all have validity. We should stipulate a minimal level. Perhaps the serious eLearning  Design Manifesto? :) We can do better.

What does it take?

This was the other issue. It was pointed out that design teams in other disciplines work in layers: from concept to realization. Jesse James Garrett has a lovely diagram that represents this for information architecture. And others pointed out that there are multiple skills involved, from dialog writing, through media production and interface design (they’re conceptually separate), and the quality of the programming and more. The more you need polish, the more you need to invest in the appropriate skill sets.  This again is a matter of marshaling the appropriate resources against the business case.

I think one of the issues is that we overuse courses when other solutions are more effective and efficient. Thus, we don’t have and properly allocate the resources to do the job right when it does positively absolutely has to be in the head. Thus, we do have a lot of boring, information dump courses. And we could be doing more with engaging practice, and less content presentation. That’s a design issue to begin, and then a presentation one.

Ultimately, I agree that bad elearning undermines our credibility. I do think, however, that we don’t need  unnecessary polish. Gilded bad design is still bad design. But then we should align our investment with the professional reception we need. And if we have trouble doing that, we need to rethink our approaches. The right level of investment for the context is the right response; we need the right live of polish. But the assessment the context is complex. We shouldn’t treat is simplistically, but instead systemically. If we get that right, we have a chance to impress folks with our astute sense of doing the right thing with the right resources. Less than that is a path to irrelevancy, and doing more is a path to redundancy. Where do  you want to go?

The roots of LXD

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Instructional design, as is well documented, has it roots in meeting the needs for training in WWII. User experience (UX) came from the Human Computer Interaction (HCI) revolution towards User Centered Design. With a vibrant cross-fertilization of ideas, it’s natural that evolutions in one can influence the other (or not).  It’s worth thinking about the trajectories and the intersections that are the roots of LXD, Learning eXperience Design.

I came from a background of computer science and education. In the job for doing the computer support for the office doing the tutoring I had also engaged in, I saw the possibilities of the intersection. Eager to continue my education, I avidly explored learning and instruction, technology (particularly AI), and design. And the relationships, as well.

Starting with HCI (aka Usability), the lab I was in for grad school was leading the charge. The book User-Centered System Design  was being pulled together as a collection of articles from the visitors who came and gave seminars, and an emergent view was coming. The approaches pulled from a variety of disciplines such as architecture and theater, and focused on elements including participatory design, situated design, and iterative design. All items that now are incorporated in design thinking.

At that time, instructional design was going through some transitions. Charles Reigeluth was pulling together theories in the infamous ‘green book’  Instructional Design Theories and Models.  David Merrill was switching from Component Display Theory to ID2.  And there was a transition from behavioral to cognitive ID.

This was a dynamic time, though there wasn’t as much cross-talk as would’ve made sense. Frankly, I did a lot of my presentations at EdTech conferences on implications from HCI for ID approaches. HCI was going broad in exploring a variety of fields to tap in popular media (a lot was sparked by the excitement around  Pinball Construction Set), and not necessarily finding anything unique in instructional design. And EdTech was playing with trying to map ID approaches to technology environments that were in rapid flux.

These days, LXD has emerged. As an outgrowth of the HCI field, UX emerged with a separate society being created. The principles of UX, as cited above, became of interest to the learning design community. Explorations of efforts from related fields – agile, design thinking, etc, – made the notion of going beyond instructional design appealing.

Thus, thinking about the roots of LXD, it has a place, and is a useful label. It moves thinking away from ‘instruction’ (which I fear makes it all to easy to focus on content presentation). And it brings in the emotional side. Further, I think it also enables thinking about the extended experience, not just ‘the course’.  So I’m still a fan of Learning Experience Design (and now think of myself as an LXD strategist, considering platforms and policies to enable desirable outcomes).

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As a side note, Customer Experience is a similarly new phenomena, that apparently arose on it’s own. And it’s been growing, from a start in post-purchase experience, through Net Promoter Scores and Customer Relationship Management. And it’s a good thing, now including everything from the initial contact to post-purchase satisfaction and everything in between. Further, people are recognizing that a good Employee Experience is a valuable contributor to the ability to deliver Customer Experience. I’m all for that.

Lucky on Foundations

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I was thinking about my next directions, and it led to me to think a bit about my foundations. And I realized I’ve been very lucky (and I’m grateful). I’ve had good parents, mentors, colleagues, and friends. But I’ve also had some fortunate timings, and it’s worth reflecting how I’ve been lucky on foundations upon which to build. (A personal reflection, not necessarily worth your time ;)

It started with college, really. I’d always been a typical lad, but with an extra serving of geek (I didn’t fit in with any clique so hung with a few similarly chaotic-good chaps :).  I started college interested in marine bio, but there was no formal link between undergrad study and Scripps. The bio program was all cut-throat med, and while I  could cut it, it was all rote memorization and deadly boring. So…

I took some comp sci classes, and was tutoring for extra money on the side.  Lucky chance: I got a job doing the computer support for the office that coordinated the tutoring. That sparked my awareness of the connections between computers and learning. Of course, back then, at my school, there was no such program. Luck 2: my school had a program where you could design your  own major. I found a couple of professors doing a project on using email for classroom discussion (circa ’78; we had the DARPAnet, otherwise there  was no email; more luck). They agreed to sponsor my project.

After graduating, I looked all over the country for an org that wanted someone interested in computers and learning. More luck, I finally came across Jim Schuyler, and as he was starting DesignWare, I got a job! And, importantly, it was designing and programming on the earliest personal computers. And I realized that there was real potential for learning in games! But I also realized that we didn’t know enough how to design them. And then I read about ‘cognitive engineering’ (applying what we know about cognition to the design of systems).

I was accepted into the cog program with Don Norman, who’d written the article. And this was another major stroke of luck. While Don’s students were researching how to build systems for how people think, my twist was about how people learn. I got to study behavioral, cognitive, social, even machine learning!  Also, Don’s lab partner Dave Rumelhart was conducting his research with Jay McClleland on what became neural nets. You can’t help but get exposed to related research through lab meetings, seminars, and more, even if you’re not active in the particular work. And Ed Hutchins was doing his work on distributed cognition.  This was a fundamental shift in perspective from formal to situated cognition.

The lab ran a Unix system, so I was getting steeped in computing systems to complement my personal computer work, along with the cognition focus. I subsequently did a post-doc at LRDC, getting deeper steeped into cognitive learning, and then joined a school of Computer Science, getting further background in computation. I was on the internet before there was a web (and foolishly was rather complacent about it)! And it’s enabled me to keep an eye on new developments like mobile and content and more, and understand their core affordance.

I also got steeped in design, having a chance to look at graphic, industrial, software, architecture, and other approaches (more luck). I combined that with a study of the academic literature, of course. These three foundations have been the basis of my work: applying cognitive and learning sciences to the design of technology to create learning and performance systems.

There’s much more to the story, of course. Serendipity continued in jobs and people to guide me, I’m happy to say.  Mentors being shy, you can’t really thank folks enough, so if I’ve been lucky in foundations, it’s my job to pass it on. I hope that this blog helps in  some way!

Direct Instruction and Learning Experience Design

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After my previous article on direct instruction versus guided discovery, some discussion mentioned Engelmann’s Direct Instruction (DI). And, something again pointed me to the most comprehensive survey of educational effects. So, I tracked both of these down, and found some interesting results that both supported, and confounded, my learning. Ultimately, of course, it expanded my understanding, which is always my desire. So it’s time to think a bit deeper about Direct Instruction and Learning Experience Design.

Engelmann’s Direct Instruction is very scripted. It is rigorous in its goals, and has a high amount of responses from learners.  Empirically, DI has great success, with some complaints about lack of teacher flexibility. It strikes me as very good for developing core skills like reading and maths.  I was worried about the intersection of many responses a minute and more complex tasks, though it appears that’s an issue that has been addressed. I couldn’t find the paper that makes that case, however.

Another direction, however, proved fruitful.  John Hattie, an educational researcher, collected and conducted reviews of 800+ meta-analyses to look at what worked (and didn’t) in education.  It’s a monumental work, collected in his book Visible Learning. I’d heard of it before, but hadn’t tracked it down. It was time.

And it’s impressive in breadth  and depth.  This is arguably the single most important work in education. And it opened my eyes in several ways.  To illustrate, let me collect for you the top (>.4)  impacts found, which have some really interesting implications:

  • Reciprocal teaching (.74)
  • Providing feedback (.72)
  • Teaching student self-verbalization (.67)
  • Meta-cognition strategies (.67)
  • Direction instruction (.59)
  • Mastery learning (.57)
  • Goals-challenging (.56)
  • Frequent/effects of testing (.46)
  • Behavioral organizers (.41)

Reciprocal teaching and meta-cognition strategies coming out highly, a great outcome. And of course I am not surprised to see the importance of feedback. I have to say that I  was surprised to see direct instruction and mastery learning coming out so high.  So what’s going on?  It’s related to what I mentioned in the afore-mentioned article, about just what the definition of DI is.

So, Hattie says: …”what the critics mean by direct instruction is didactic teacher-led talking from the front…” And, indeed, that’s my fear of using the label. He goes on to point out the major steps of DI (in my words):

  1. Have clear learning objectives: what should the learner be able to  do?
  2. Clear success criteria (which to me is part of 1)
  3. Engagement: an emotional ‘hook’
  4. A clear pedagogy: info (models & examples), modeling, checking for understanding
  5. Guided practice
  6. Closure of the learning experience
  7. Reactivation: spaced and varied practice

And, of course, this is pretty much everything I argue for as being key to successful learning experience design. And, as I suspected, DI is not what the label would lead you to believe (which I  do think is a problem).  As I mentioned in a subsequent post, I’ve synthesized my approach across many elements, integrating the emotional elements along with effective education practice (see the alignment).  There’s so much more here, but it’s a very interesting result. Direct Instruction and Learning Experience Design have a really nice alignment.

And a perfect opportunity to remind you that I’ll be offering a Learning Experience Design workshop at DevLearn, which will include the results of my continuing investigation (over decades) to create an approach that’s doable and works. Hope to see you there!

Theory or Research?

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There’s a lot of call for evidence-based methods (as mentioned yesterday): L&D, learning design, and more. And this is a good thing. But…do you want to be basing your steps on a particular empirical study, or the framework within which that study emerged? Let me make the case for one approach. My answer to theory or research is theory. Here’s why.

Most research experiments are done in the context of a theoretical framework. For instance, the work on worked examples comes from John Sweller’s Cognitive Load theory. Ann Brown & Ann-Marie Palincsar’s experiments on reading were framed within Reciprocal Teaching, etc. Theory generates experiments which refine theory.

The individual experiments illuminate aspects of the broader perspective. Researchers tend to run experiments driven by a theory. The theory leads to a hypothesis, and then that hypothesis is testable. There  are some exploratory studies done, but typically a theoretical explanation is generated to explain the results. That explanation is then subject to further testing.

Some theories are even meta-theories! Collins & Brown’s Cognitive Apprenticeship  (a favorite) is based upon integrating several different theories, including the Reciprocal Teaching, Alan Schoenfeld’s work on examples in math, and the work of Scardemalia & Bereiter on scaffolding writing. And, of course, most theories have to account for others’ results from other frameworks if they’re empirically sound.

The approach I discuss in things like my Learning Experience Design workshops is a synthesis of theories as well. It’s an eclectic mix including the above mentioned, Cognitive Flexibility, Elaboration, ARCS, and more. If I were in a research setting, I’d be conducting experiments on engagement (pushing beyond ARCS) to test my own theories of what makes experiences as engaging and effective. Which, not coincidentally, was the research I was doing when I  was  an academic (and led to  Engaging Learning). (As well as integration of systems for a ubiquitous coaching environment, which generates many related topics.)

While individual results, such as the benefits of relearning, are valuable and easy to point to, it’s the extended body of work on topics that provides for longevity and applicability. Any one study may or may not be directly applicable to your work, but the theoretical implications give you a basis to make decisions even in situations that don’t directly map. There’s the possibility to extend to far, but it’s better than having no guidance at all.

Having theories to hand that complement each other is a principled way to design individual solutions  and design processes. Similarly for strategic work as well (Revolutionize L&D) is a similar integration of diverse elements to make a coherent whole. Knowing, and mastering, the valid and useful theories is a good basis for making organizational learning decisions. And avoiding myths!  Being able to apply them, of course, is also critical ;).

So, while they’re complementary, in the choice between theory or research I’ll point to one having more utility. Here’s to theories and those who develop and advance them!