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Clark Quinn’s Learnings about Learning

Solutions for Tight Cycles of Assessment

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

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

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

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

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

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

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

My Professional Learner’s Toolkit

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My colleague, Harold Jarche, recently posted about his professional learning toolkit, reflecting our colleague Jane Hart’s post about a Modern Learner’s Toolkit. It’s a different cut through the top 10 tools.  So I thought I’d share mine, and my reflections.

Favorite browser and search engine: I use Safari and Google, by default. Of course, I keep Chrome and Firefox around for when something doesn’t work (e.g. Qualtrics).  I would prefer another search engine, probably DuckDuckGo, but I’m not facile with it, for instance finding images.

A set of trusted web resources: That’d be Wikipedia, pretty much. And online magazines, such as eLearnMag and Learning Solutions, and ones for my personal interests. I use Pixabay many times to find images.

A number of news and curation tools: I use Google News and the ABC (Oz, not US) in my browser, and the BBC and News apps on my iDevices. I also use Feedblitz to bring blogposts into my email.  I keep my own bookmarks using my browser.

Favorite web course platforms: I haven’t really taken online courses. I’ve used Zoom to share.

A range of social networks: I use LinkedIn professionally, as well as Slack. And Twitter, of course.  I stay in touch with my ITA colleagues via Skype.  Facebook is largely personal.

A personal information system: I use both Notability and Notes to take notes.  Notes more for personal stuff, Notability for work-related. I use Omnigraffle for diagrams and mindmaps.  And OmniOutliner also helps when I want to think hierarchically.

A blogging or website tool: I use WordPress for Learnlets (i.e. here), and I use Rapidweaver for my sites: Quinnovation and my book sites.

A variety of productivity apps and tools: Calendar is crucial, and Pagico keeps me on track for projects. I use Google Maps for navigation. I use SplashID for passwords and other private data. I often read and markup documents on my iPad with GoodReader. CloudClip lets me share a multi-item clipboard across my devices.    Reflection: this overlaps with the personal information system.

A preferred office suite: I don’t have a preferred suite, though I’d like to use the Apple Suite. I use Word to write (Pages hasn’t had industrial-strength outlining), Keynote to create presentations (e.g. one from each suite). I don’t create sheets often.

A range of  communication and collaboration tools: I use Google Drive to collaborate on representations.  I have used Dropbox to share documents as well. And of course Mail for email.   Reflection: this overlaps with social networks.

1 or more smart devices: I’d be lost without my iPhone and iPad (neither of which is the latest model). I use the phone for ‘in the moment’ things, the iPad for when I have longer time frames.

So, that’s my toolkit, what’s yours?

Jane's toolkit diagram

#AECT17 Conference Contributions

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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.

#AECT17 Reflections

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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.

Tom Reeves AECT Keynote Mindmap

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

Mindmap

Derek Cabrera AECT Keynote Mindmap

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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.

Mindmap

Marcy Driscoll AECT Keynote Mindmap

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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.

Mindmap

Revisiting 70:20:10

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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 a good solution for the performance part of the picture, and the specific  parts of the development.

Rules for AI

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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!

Addressing Changes

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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?