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

16 November 2017

#AECT17 Conference Contributions

Clark @ 8:04 AM

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

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

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

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

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

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

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

The sessions were fun, and the feedback was valuable.

15 November 2017

#AECT17 Reflections

Clark @ 8:10 AM

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

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

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

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

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

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

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

10 November 2017

Tom Reeves AECT Keynote Mindmap

Clark @ 7:11 AM

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


9 November 2017

Derek Cabrera AECT Keynote Mindmap

Clark @ 7:25 AM

Derek Cabrera opened the second day of the AECT conference with an insightful talk about systems thinking and the implications for education. With humorous examples he covered the elements of systems thinking and why it means we need to switch pedagogies to a constructivist approach.


8 November 2017

Marcy Driscoll AECT Keynote Mindmap

Clark @ 10:48 AM

Marcy Driscoll kicked off the Association for Educational Communications and Technology’s annual conference with a thoughtful keynote on leadership. She used her experience as a Dean to explore possibilities and suggestions for what this could and should mean.


7 November 2017

Revisiting 70:20:10

Clark @ 8:03 AM

Last week, the Debunker Club (led by Will Thalheimer) held a twitter debate on 70:20:10 (the tweet stream can be downloaded if you’re curious).  In ‘attendance’ were two of the major proponents of 70:20:10, Charles Jennings and Jos Arets.  I joined Will as a moderator, but he did the heavy lifting of organizing the event and queueing up questions.  And there were some insights from the conversations and my own reflections.

Learning curveTo start, 70:20:10 is a framework, it’s not a specific ratio but a guide to thinking about the whole picture of developing organizational solutions to performance problems. In the book by Jos & Charles, along with their colleague Vivian Heijnen, on the topic, there’s a whole methodology that encompasses 5 roles and 28 steps. The approach goes from a problem to a solution that incorporates tools, formal learning, coaching, and more.

The numbers come from a study on leaders, who felt that 10% of what they learned to do their jobs came from formal learning, 20% came from working with others and coaching, and 70% they learned from trying and reflecting on the outcomes. The framework’s role is to help people recognize this, and not leave the 70 and 20 to chance. The goal is to help people along the learning curve, not just leave them to chance after the ‘event’.

First, my impression was that a lot of people like that the 70:20:10 framework provides a push beyond the event model of ‘the course’. Also, a number struggle with the numbers as a brand, because they feel that the numbers are misleading. And some folks clearly believe that good instructional design should include the social and the activity, so the framework is a distraction. A colleague felt that there were also some who feel that formal learning is a waste of time, but I don’t think that many truly ignore the 10, they just want it in the proper perspective (and I could be wrong).

MoreFormalNow, there are times when the ratio changes. In roles where the consequences of failure are drastic (read: aerospace, medical, military), you tend to have a lot more formal.  It can go quite a ways up the learning curve. Ideally, we’d do this for every situation, but in real life we have to strike a balance. If we can do the job right in the 10, and then similarly ensure good practices around the 20 and the 70, we’ll get people up the curve.

Another issue, for me, is that 70:20:10 not only provides a push towards thinking of the whole picture, but like Kirkpatrick (and perhaps better) it serves as a design tool. You should start from what the situation looks like at the end and figure out what can be in the world and what has to be in the head, and then go backwards. You then design your tools, and then your training, and 70:20:10 suggests including coaching, etc.  But starting with the 70 is one of the messages.

So, I like the realization of 70:20:10 (except typing all those redundant zeros and colons, I often refer to it as 721 ;).  The focus on designing the full solution, including tools and coaching and more.  I don’t see 70:20:10 being the full solution, as the element of continual innovation and a learning culture are separate, but it’s as good a solution for the performance part of the picture, and the specific parts of the development.

2 November 2017

Rules for AI

Clark @ 8:02 AM

After my presentation in Shanghai on AI for L&D, there were a number of conversations that ensued, and led to some reflections. I’m boiling them down here to a few rules that seem to make sense going forward.

  1. Don’t worry about AI overlords. At least, not yet ;).  Rodney Brooks wrote a really nice article talking about why we might be fearing AI, and why we shouldn’t. In it, he cited Amara’s Law: we tend to overestimate technology in the short-term, and underestimate the impact in the long term. I think we’re in the short-term of AI, and while it’s easy to extrapolate from smart behavior in a limited domain to similar behavior in another (and sensible for humans), it turns out to be hard to get computers to do so.
  2. Do be concerned about how AI is being used. AI can be used for ill or good, and we should be concerned about the human impact.  I realize that a focus on short-term returns might suggest replacing people when possible. And anything rote enough possibly should be replaced, since it’s a sad use of human ability.  Still, there are strong reasons to consider the impact on the people being affected, not least humanitarian, but also practical. Which leads to:
  3. Don’t have AI without human oversight (at least in most cases).  As stated above in 1, AI doesn’t generalize well.  While it can be trained to work within the scope you describe, it will suffer at the boundary conditions, and any ambiguous or unique situations. It may well make a better judgment in those cases, but it also may not. In most cases, it will be best to have an external review process for all decisions being made, or at least ones at the periphery. Because:
  4. Your AI is only as good as it’s data set and/or it’s algorithms. Much of machine learning essentially runs on historical datasets. And historical datasets can have historical biases in them.  For instance, if you were to look at building a career counselor based upon what’s been done in many examples across schools, you might find that women were being steered away from math-intensive careers. Similarly, if you’re using a mismatched algorithm (as happens often in statistics, for example), you could be biasing your results.
  5. Design as if AI means Augmented Intelligence, not Artificial Intelligence (perhaps an extension of 3). There are things humans do well, and things that computers do well. AI is an attempt to address the intersection, but if our goal is (as it should be) to get the best outcome, it’s likely to be a hybrid of the two. Yes, automate what can and should be automated, but first consider what the best total solution would be, and then if it’s ok to just use the AI do so. But don’t assume so.
  6. AI on top of a bad system is a bad system. This is, perhaps, a corollary to 4, but it goes further. So, for instance, if you create a really intriguing simulated avatar for practicing soft skills, but you’re still not really providing a good model to guide performance, and good examples, you’re either requiring considerable more practice or risking an inappropriate emergent model.  AI is not a panacea, but instead a tool in designing solutions (see 5).  If the rest of the system has flaws, so will the resulting solution.

This is by no means a full set, nor a completely independent one. But it does reflect some principles that emerged from my interactions around some applications and discussions with people. I welcome your extensions, amendments, or even contrary views!

25 October 2017

Addressing Changes

Clark @ 8:03 AM

Yesterday, I listed some of the major changes that L&D needs to acknowledge. What we need now is to look at the top steps that need to be taken.  As serious practitioners in a potentially valuable field, we need to adapt to the changing environment as much as we need to assist our charges to do so. So what’s involved?

We need to get a grasp on technology affordances. We don’t need to that the latest technology exists, whether AI, AR, or VR.  Instead, we have to understand what they mean in the context of our brains.  What key capabilities are brought?  Can VR go beyond entertainment to help us learn better? How can AI partner with us?  If we can make practical use of AR, what would we do with it?

In conjunction, we need to understand the realities about us.  We need to take ownership and have a suitable background in how people really think, work, and learn. Further, we need to recognize that they’re all tied together, not separate things. So, for instance, we learn as we work, we think as we learn, etc.

For example, we need to understand situated and distributed cognition. That is, we need to grasp that we’re not formal logical thinkers, but instead very context dependent, and that our thinking is across our tools. As a consequence, we need to design solutions that recognize our individual situations, and leverage technology as an augment. So we want to design human/computer system solutions to problems, not just human or system solutions.

We also need to understand cultural elements. We work better when we are given meaningful work, freedom to pursue those goals, and get the necessary support to succeed. This is not micromanagement, but instead, is leadership and coaching. We also need an environment where it’s safe, expected even, to experiment and even to make mistakes.

We also need to understand that we work better (read: produce better results), when we work together in particular ways. Where we understand that we should allow individual thought first, but then pool those ideas. And we need to show our work and the underlying thinking. Moreover, again, it has to be safe to do so!

And, these are all tied together into a systemic approach!  It can’t be piecemeal, because working together and out loud can’t be divorced from the technology used to enable these capabilities. And giving people meaningful work and not letting them work together, or vice-versa, just won’t achieve the necessary critical mass.

Finally, we also need to do this in alignment with the business. And, lets be clear, in ways that can be measured!  We need to be understanding what are the critical performance needs of the organization, and demonstrate that we’re impacting them in the ways above.

This can be done, and it will be the hallmark of successful organization. We’re already seeing a wide variety of converging evidence that these changes lead to success. The question is, are you going to lead your organization forward into the future, or keep your head down and do what you’ve always done?

24 October 2017

Acknowledging Changes

Clark @ 8:08 AM

There are a serious number of changes that are affecting organizations.  We’re seeing changes in the information flow, in technology, and in what we know about ourselves. Importantly, these are things that L&D needs to acknowledge and respond to.  What are these changes?

It’s old news that things are happening faster. We’re being overwhelmed with information, and that rate is accelerating. On the other hand, our tools to manage the information flow are also advancing.

Which is the second topic. We’re getting more powerful technology. We can create systems that do tasks that used to be limited to humans. They can also partner with us, providing information based upon who we are, what we’re doing, and what else is going on.

And there are increasing demands for accountability (and transparency). Your actions should be justified. What are you doing, why, and what effect is it having? If you can’t answer these questions, you’re going to be looking for a job.

Most importantly, we’ve learned quite a bit about ourselves that is contrary to many pre-existing beliefs. Specifically ones that influence organizational approaches.  Our myths about how we think, work, and learn are holding us back from achieving optimal outcomes.

For one, there’s a persistent belief that our thinking is in our heads.  Yet research shows that our thinking is distributed across our tools. We use external representations to capture at least part of our thinking, and access information that we can’t keep in our heads effectively.  Yet we seem to depend on courses to put it in the head instead of tools to put it in the world.

Our thinking is also distributed across others. “You’re no longer what you know, but who you know” is a new mantra. So is “the room is smarter than the smartest person in the room” (with the caveat: if you manage the process right ;). Informal and social learning is the work. Yet we still act as if we believe that people should solve problems independently.

And we also act as if how we learn is by information dump.  Add a quiz, so we know they can recognize the right answer if they see it, and they’ve learned!  Er, no. Science tells us that this is perhaps the worst thing we could do to facilitate learning.

In short, our practices are out of date. We’re using patch-it (or ignore-it) solutions to systemic issues.  We address simple things as if they’re not all connected. It’s time to get on top of what’s known, and then act accordingly.  Are you ready to join the 21st century?

18 October 2017

Stay Curious

Clark @ 8:09 AM

One of my ongoing recommendations to people grew out of a toss-off line, playing off an advertisement. Someone asked about a strategy for continuing to learn (if memory serves), and I quipped “stay curious, my friends”.  However, as I ponder it, I think more and more that such an approach is key.

I was thinking of this trend the the other day as “intellectual restlessness”. What I’m talking about is being intrigued by things you don’t understand that have persisted or recently crossed your awareness, and pursuing it. It’s not just saying, how interesting, but recognizing connections, and pondering how it could change what you do. Even to the point of actually changing!

It also would include pointing interesting things to other people who would benefit.  This doesn’t always have to happen, but in the spirit of cooperation (in the Jarche sense), we could and should contribute, curate, when we can.  And, ideally, leaving trails of your explorations that others can benefit from. Writings, diagrams, videos, what have you, helps yourself as well as others.

Old Infoworld magazinesI was reminiscing that more than 30 years ago, on top of my job designing educational computer games, I was already curious. I still have copies of the magazines containing reviews I did (one hardware, one software), as well as a journal article based upon undergraduate research I was fortunate to participate in.

And that persistence in curiosity has led to a trail of artefacts. You may have come across the books, book chapters, articles, presentations, etc. And, of course, this blog for the past decade and more. (May it continue!) However, I’m not here to tout my wares, but instead to point to the benefit of being curious.

As things change faster, a continuing interest is what provides an ongoing ability to adapt. All the news about the ongoing changes in jobs and work isn’t likely to lessen.  Staying curious benefits you, your colleagues and friends, and I reckon society in general.  You want to look at many sources of information, track tangential fields, and be open to new ideas.

This isn’t just your choice, of course, ideally your organization is supportive. These lateral inputs are a component of innovation, as is time to allow for serendipity and incubation. Orgs that want to be able to be agile will need this capabilities as well. I suppose organizations need to stay curious as well!


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