Attention, I’ll suggest, is how we pay conscious awareness to our thinking. We pay attention to the sensory stream that’s available, and as working memory is has limits, our attention chooses what ends up being in working memory (which is where we see conscious thought). This is the picture I paint in Learning Science for Instructional Designers, my recent book on how we learn. That’s how I learned it in grad school, and little seemed to change that.

As an aside, I suggest that the basic human information processing loop is something that is critical to understand. This is true for learning designers, but I would suggest there’s broader applicability. Knowing how information flows:

• from sensory store to working memory via attention
• from working memory to long term memory via elaboration
• back to working memory via retrieval
• and to decision from working memory

As a simplified story, shows how humans work in many ways. It gets more complex in important ways, but this is a key basis. On top of it comes aspects of how we think, and learn, but this is the core. It benefits anyone dealing with people, basically: UI, marketing, etc. In short, most everyone.

Recent pictures of the information processing loop suggest, however, that attention has a bigger purview. They have it influencing most of the above. Which may be more accurate, in that if you need to attend to what’s in working memory, and manage the process of attending to information while evaluating what decision to make. You must maintain conscious focus on what you want to learn.

The myth, which still persists, is that our attention span has dropped to 8 seconds. Which folks tout as less than that of a goldfish. (How do we know what the attention span of a goldfish is?) The origin of this myth came from StatBrain misinterpreting a study, and was amplified since it was published by Microsoft Canada. Marketing, mind you, not their research group! A myth I busted in a previous book!

There is apparently some evidence that our attention span has dropped (to 4o-something seconds, not eight), but we can still disappear into movies, novels, and games for hours. I reckon it’s about how engaging it is. Which, not completely surprisingly, is the topic of my most recent book, Make It Meaningful.

So, please, avoid the myths, and learn the core. Attention is underrated, as is the whole human information processing loop. Learn it, and benefit.

This blog was originally published on Learnlets.

The post Attention is underrated first appeared on The Upside Learning Blog.

Several years ago, four colleagues in the field got together to point out the differences between what constitutes traditional eLearning with what would represent deeper learning, ultimately creating the Serious eLearning Manifesto. In it, they pointed out nuances that matter. Here, we’re revisiting those differences in a slightly different way, to the same end; trying to increase the quality of what is delivered by our industry.

1. Objectives:

The first-place learning can go wrong is just in characterizing what we’re trying to address. Too often, I see well-intentioned courses focusing on information or knowledge, e.g., awareness. Which would be comprehensible if we were formally logical reasoners. Unfortunately, we’re not, and we don’t change behavior just on the basis of new information.

To make meaningful change, we need to focus on actual performance issues. I usually find this in the form of decisions we need to make differently or better. There are barriers to this, for instance, that our experts have as much as 70% of their underlying thinking inaccessible to conscious awareness; they literally can’t tell us what they do!

Thus, ensuring that our objectives are focused on meaningful outcomes is the first area where we can and should apply deeper learning. We need to have something worthwhile to retain and transfer! We need to ensure that the focus of our efforts is appropriately directed. If you see learning that’s focused on information, not on behaviors, you’re seeing learning that’s not sufficiently deep.

2. Introductions:

We know that learning proceeds better when we’re emotionally engaged. If we care, we pay more attention, invest more effort, and thus our retention and transfer are improved. Yet too often our introductions to the learning fail to help learners comprehend the relevance of the learning.

When learners understand the personal relevance, the What’s In It For Me (WIIFM), they learn better. Yet, too often, we take for granted that they’ll comprehend the relevance and rationale. That’s not a safe bet. Instead, we should make a concrete effort to ensure that they know they need this. We can do this with the positive consequences of having the knowledge, or the negative consequences of not having the knowledge, but we should do it.

If you see learning that just starts talking about what is to be covered, without addressing the ‘why’, you’re seeing traditional versus deeper learning. Moreover, you’re seeing instruction for which it’s not clear learners will invest true effort into learning.

3. Content:

Too often, we see people talking about ‘content’. That is, information about what’s to be learned. What is a worry is where this content isn’t differentiated by its role in learning. There are specific types of content that serve important, but different, roles in supporting learning. If someone isn’t making the distinctions, you should be wary.

We know, for instance, that providing models gives learners frameworks to use for making the decisions that the objectives specified. Thus, we can use water in pipes as a metaphor to think about electricity. Good models provide a basis to infer the results of decisions, so you can make a choice that achieves the desired outcome. Not providing useful models leaves the learners to infer their own, which frequently can lead to wrong models.

A second critical form of content is examples. Here, we’re taking those models in different situations and showing how to use them in context as a guide to performance. This provides a basis for unpacking what’s critical from what’s ephemeral. Thus, we provide the necessary support for the transfer identified above as a necessary outcome of learning. Since we frequently can’t provide all the necessary situations, we should be choosing examples that support the appropriate inferences for new situations. Yet, too often, we just have a few examples that were told to us or came from materials, instead of explicitly considering the necessarily useful example.

Without a careful choice of models and examples, we’re minimizing the ability of learners to transfer the knowledge appropriately. We also make it harder to fill in gaps from the models when we’ve forgotten part of an approach, undermining retention as well. In both cases, we’re rendering our learning less likely to be a valuable investment.

4. Practices:

The final element that separates deeper learning from what we see too frequently is the element of practice. That is, problems or situations where the learner must actively make choices and get feedback afterward. Whether you term such activities as problems, assessments, or practice, what we know is that learners need to practice using the information to develop a persistent ability to apply it after the learning experience. In short, retention depends on sufficient practice.

To use a metaphor, we can’t build muscle overnight; instead, we need to exercise regularly over time. Only so much strengthening happens in any one day. So, too, with learning. The strength of the traces in our brains can only get so much stronger in any one day before we need rest and further strengthening. This suggests that the outcomes of a one-day ‘learning event’ are unlikely to persist unless we continue interventions. As a consequence, proper practice sufficient to develop a new ability to the necessary level is more than just “until they get it right”. Yet, too frequently we see limited practice.

A second problem is what the practice has the learner doing. All too often, we see practice activities focused on retrieving the knowledge, e.g., “This is the definition of that”. Which will lead to an ability to recite information, but not to be able to the use that knowledge where it matters. It’s the difference between asking whether you know the ingredients in a recipe and whether you can make the dish. To get better at using the information to perform better, we need to practice the performance. We need mini-scenarios and branching scenarios at a minimum.

The right type of practice, and sufficient quantities, are what lead to learning that is retained over time and transfers appropriately. This implies a focus on practice, not ‘content’. Yet we too frequently see the ratio between the two emphasizing the content at expense of practice. Again, the nuances matter.

Recognizing Nuances

Well-produced learning has introductions, content, and practice that looks elegant. Well-designed and well-produced learning does similarly. However, only the latter has a good chance of leading to meaningful outcomes. If you don’t understand the nuances, it’s easy to be sold a solution that, well, isn’t one.

Learning is a probabilistic game; you can lead a learner to learn, but you can’t make them think (to paraphrase an infamous saying). What good learning design does is increase the likelihood that learning occurs to a very high degree of probability. However, what differentiates effective learning from ‘content with a quiz’ are nuances that are subtle. Unless you’re aware of the differences, you can easily be misled by aesthetics and production values. Not everyone in the organization needs to know, but those who are expending resources on learning solutions do. We intend that this helps you be better equipped. We are committed to deeper learning, and we hope you can understand why. We’re happy to answer any questions.

Ready to redefine your approach to learning? Elevate your skills in Learning Experience Design (LXD) with Missing LXD and Upside Learning! Join our immersive workshop led by industry experts to unlock the inspiring power of LXD. Don’t miss out on this opportunity to elevate your instructional design expertise. Click the link below to register: https://ldaccelerator.com/member-workshop-events/missinglxd

The post Four Steps to Deeper Learning first appeared on The Upside Learning Blog.

While learning effectiveness still has a part to play in education, when we talk about workplace learning, it helps to focus on what we’re really designing for – learning transfer.

We don’t need people to merely learn better, we mainly need them to carry out their responsibilities with more efficiency and accuracy. To achieve that, they need to transfer what they have learned into (as Clark Quinn most eloquently puts it) “all appropriate and no inappropriate situations” in the course of their work.

I’m using this lens of learning transfer to explore some design fallacies that are distracting us from optimizing for better workplace performance.

Fallacy #1 – The transfer of learning is considered complete only when learners can recall the information provided in the learning intervention after the said training.

Wouldn’t it be a perfect world if we could avert workplace accidents just by getting all our employees to “recall” safety guidelines (and sing them to the tune of Twinkle Twinkle Little Star)?

That has never worked, right?

The transfer of learning, therefore, is complete only when learners have in some way demonstrated their ability and willingness to follow precautionary safety procedures and perform/trigger emergency protocol in case a safety incident occurs. In other words, such behavior has to become their second nature for the transfer of learning to be considered complete.

So essentially, since workplace learning is not for the sake of learning itself, the outcomes need to be measured against performance parameters and not knowledge parameters.

Fallacy #2 – All content should be created equal.

Once the goalpost moves from knowledge to performance, the lens with which we approach content needs to change too. Since workplace learning is aligned with performance outcomes, we need to wear the Performance Consultant hat instead of the Learning Consultant one – and a Performance Consultant’s aim is to enable learners to perform their jobs better.

We need to be able to segregate content into:

• Knowledge in the head – information that is vital to workplace performance, and needs to be retrievable from memory whenever and as many times as required; and
• Knowledge in the world – information that supports workplace performance and is fed as “cues” into the workplace environment so it can be readily referenced as and when required.

For instance, if the objective is for a set of learners to be able to use a new system, you need to segregate the system’s functions into:

• Actions that are integral to the work the audience does – The solution must be designed with the objective of enabling learners to perform these actions with various levels of automaticity in the real world. In other words, this is information they need to learn and retain in their memory.
• Actions that are “good-to-know” and/or may need to be performed as an exception – Considering that each of us only has finite storage space in our working memory, we can reduce cognitive load for learners by embedding this information in their work environment for easy access. The solution must therefore be designed with the objective of allowing learners to refer to this material with minimum effort when they need it, and in a form that allows them to perform the action “in the flow of work”.

Fallacy #3 – Excessive content and done-to-death page-turners.

For too long we’ve burdened learners with tons of content, and then added token knowledge checks that test recall. The result has been consistent – 80% assessment scores, but little measurable improvement in performance.

It’s time to turn this model on its head. If learners are expected to perform certain actions in the real world, we need to prime them to do exactly that – and allow them to fail in a safe space with no real-world consequences, over and over again, till they gain confidence and do it right.

Consider a parent teaching a child how to ride a bicycle. No number of theoretical sessions about the workings of a bicycle and the science of balance will enable that child to ride the bicycle. However, taking them out on a bike ride with training wheels on, can help them practice the skill of building balance in a safe space. And what’s more, falling a few times in a grassy field rather than a crowded street will help them understand and bear the consequences of mistakes in an environment that protects them from serious injury.

That’s exactly what we should be able to simulate in our learning programs – safe practice opportunities that incrementally build confidence and competency in the tasks tied to the performance outcomes.

And it’s only after we design these practice experiences that we plug in content.

But when we’re being so moderate and cautious with the amount of content we add, it puts that much more onus on us to ensure that it really counts!

What do learners really need in the form of content? The bare minimum…

• Causal explanations for why they’re supposed to do things a certain way, or in other words, mental models. It can be difficult to mug up a set of process steps, but once you understand the logic that drives the sequence for those steps, you’re much more likely to remember the process.
• Worked examples are another great way to cover varied contexts in which the content can be applied. This works especially well with novice learners. While we’re waking up to the importance of scenarios in learning, worked examples still remain a grossly underused instructional strategy. Narrative-driven examples that lay out a problem statement, the context in which it occurred, the application of a mental model to arrive at a solution, and the misconceptions that can imperil the desired outcome, are far more useful to learners than abstract concepts chunked into pages and pages of eLearning courses.

Arguably, those two forms of content are all learners need, apart from insightful and consequential feedback embedded into practice exercises.

To conclude, I’d just like to reiterate that, unlike students, workplace learners are neither required nor expected to “learn” – they’re expected to “perform”. For the longest time, learning designers have been made to mimic strategies used in education. Unlearning is never easy, but we need to start somewhere. Let’s start simply by asking ourselves at every step of the design phase – “Does this enable my audience to perform their jobs better?”

The post Design Fallacies Getting in the Way of Learning Transfer first appeared on The Upside Learning Blog.

The process is fairly straightforward, and I’d want to condense it down to something that I’d work with, inspired by Clark. Although I have condensed the process into three simple steps, the details and considerations under each step are what make this process a robust one in my opinion—and I feel that’s an area that Clark makes you think and reflect on. It’s the details of considerations that differentiates this process from everything else.

I would divide this process into three broad steps:

Step One: Determine Big Goals & Metrics

Clark states that we should frame our big goals with respect to business, learning, and performance in a way that they are observable and measurable. He also goes on to say that we should be able to determine when the learner is capable of performance and at what level of expertise and under which contexts.

When we’re framing the business, learning, and performance goals, we should keep in mind the following consideration:

Constraints: Are there any scope, budget, or resource constraints that get stretched with the framing of the goals and metrics?

We have to be precise when we’re framing metrics that we want to achieve with these meaningful learning interventions.

Step Two: Gain insights from SMEs & learners

SMEs are treasure troves of content. As designers, we have to ensure that we probe SMEs and find insights about the content that will help the performance of the learners from the complex knowledge architecture that SMEs possess. Knowledge can be extracted from the SMEs into four different buckets, namely:

• Mental models that SMEs have built over time about the decisions they make or are obvious to them,
• Common misconceptions about the knowledge and common mistakes that occur while they make those decisions,
• Stories around the decisions or knowledge that may be useful, and
• What intrinsic motivators made them develop expertise in the subject they’re an expert in.

Collecting insights across these four buckets should lead to a strong foundation for the learning intervention.

Learning about the learners is also extremely important. It’s not just about gathering the demographic details of the learners. We need to dig deeper to understand what their interests are, what they care about, and what motivates them to do whatever it is that they’re doing. If we’re going to be demanding their time and attention, we must get to know them better to serve them better. This helps us in designing experiences that are as close as possible to the expectations of the learners. These insights also help build better engagement with the learners.

Step Three: The [Goal>Role>World] Process for meaningful learning experiences

This is another (three-step process) within this larger three-step process. Its purpose would be to focus on the quality of experience you’d want to build for your learners. You can learn more about that process here. It’s majorly about determining Goal, Role, and World; choosing the treatment that you deem appropriate; and then running your early prototypes through a creativity checklist to fine-tune the experience.

Important Considerations

It’s not just about getting through these three steps; it’s also about doing them well and in an effective way. The way this can be achieved is by considering the following things:

Continuous Iteration: Keep testing the smallest ideas with outputs that take the lowest possible effort to communicate the idea and then keep building on it and improving it.

Documentation: Document almost everything. We need to borrow the principles of documentation from the world of products and game development. Documentation helps bring all the stakeholders on the same page and helps us stay on track and achieve the set goals.

Testing: We need to rethink the way testing is usually done. Clark has mentioned research where they’ve found that iterating between the expert review and user testing helps, and as few as 5 users can identify up to 80% of usability problems.

There’s a sequence that Clark suggests that we follow for the best results:

• First, do a usability testing,
• Secondly, test for its educational outcome, and
• Only then test for user engagement.

This sequence helps us to spend our testing resources wisely.

As I mentioned in the previous blog, designing meaningful learning experiences is not easy. But it’s fun—it’s hard fun, the thing we should be designing for in these experiences for the learners.

Clark says, “Learning should be hard fun”, who knew this would apply while you design for learning as well? Personally, for me, the learning interventions that I design or am a part of must have people excited. The people here refer to not just the audience now, it also translates to the team I’m working with.

After all (and paraphrasing Clark) we are at the forefront of delivering human transformation through educationally effective and emotionally engaging experiences—and how can that be easy?

The post Make It Meaningful by Clark Quinn – Idea Extractions – Part Four first appeared on The Upside Learning Blog.

The central tenet of making your learning a meaningful experience lies in Chapter Four: Tricks & Tips, according to me. Clark talks about and dives deeper into the idea of determining the [Goals > Role > World] process. He goes into the depths of how storytelling (Rance Greene’s Instructional Story Design is great reference material) can guide us all to designing for meaningful learning experiences.

Before diving deeper into the three-step process that I’ve derived from this chapter, I’d want to speak about a meta-skill that Clark insists that everyone should be nurturing. Clark mentions that whatever it is we want to design, be it courses, games, interactivities, or stories, we should find the inspiration for it by watching more movies, reading more stories, playing more games, visiting amusement parks, etc. just consuming everything that you’d want your learners to feel and just completely immersing ourselves in it. This helps us be more creative and observant, and understand the principles better.

From what I understand, adding creativity appropriately is also what makes the learning experience meaningful. This [Goals > Role > World] three-step process helps you do just that. It’s never easy to demystify a creative endeavor. Clark has done a splendid job of doing just that, so let’s get into it.

Step One: Determine [Goals, Role, and World]

These three also form the core components of a good story.

Determining Goals: We need to first determine the goals that need to be achieved

Key considerations while determining Goals:

• It must accurately convey the real-life task.
• We must not tinker much with the real-life decisions that need to be made to be able to do the task.

Determining Role: We must then define the role of the protagonist who will be achieving the defined goals

Key considerations while determining the Role:

• Put the players in a role they want to be in according to their interests.
• Determine the traits and quirks of the role that excite/relate to the audience.
• Determine the character arc of the role; ensure that the role completes the arc within the experience.

Determining the World: Finally, we determine the world in which that protagonist lives to achieve those goals set

Key considerations while determining the world:

• It could be real life or even anything mythical, but we need to ensure that it facilitates effective learning transfer. There’s a possibility of contexts getting lost, so choose your Worlds carefully.
• Worlds may also have inherent conflicts within them that make it difficult for the protagonist to achieve the goal. So, when the protagonist overcomes these conflicts, the conclusion is more fulfilling as the World has changed/evolved.

Step Two: Figure your treatment

Either you know this from the beginning, because of constraints, or because you just finished your step. We will need to determine how you want to treat this experience. It can be a story, game, interactivity, or anything else that we deem fit or suits the audience, as also the requirements, budget, and other constraints of the project.

Depending on the treatment we choose, we will need to move to production accordingly. The production guides are fairly standardized and there aren’t any considerations while at the production stage.

Once we have an early prototype ready, we could look at step number three.

Step Three: Creativity in Learning Checklist

Only when you’ve figured out the first two steps can you move to the creativity in learning checklist. The steps and the names for them are contrived by me, I’ve taken the information from the book and have arranged it in a way that suits my understanding and helps me make meaning out of it.

This checklist ensures that we tighten up the experience that we’re about to build, and is by no means exhaustive. It’s also all of the things that Clark mentions in his Tips and Tricks section. We can use this checklist as a guide to reflect on/improve the experiences that we’ve built/are building:

• Does the experience have a great introduction (especially with a backstory), and does it have an emotional hook at the very beginning that makes you want to know more?
• Are there decisions that have been crafted that initiate/invoke conflicts that make the audiences learn?
• Are the feedbacks that have been crafted individualized and personalized to every option provided?
• Do the character dialogues integrate appropriate humor, exaggeration, or any other emotion to create a suspension of disbelief?
• Is the experience able to adjust to difficulties, and maintain the ebb and flow of tension within the experience?
• Does the experience have flavors of relatedness, autonomy and competence within it to promote engagement within it?
• Are there intentionally built practices and rituals into the experience that help promote learning transfer?
• Are the activities/challenges framed in a way that converts all knowledge-level statements into application-level statements?
• Are there any uses of examples? How effectively have they been used?
• Can you map the quality of the experience against the quality of the objectives that were set?
• Is there a possibility to check if the experience has appropriate levels of learning transfer strategies built into it?

It’s not easy. It never was. Creating meaningful experiences is hard work and creating meaningful learning experiences is a lot harder than that. These three steps, hopefully, make it a little bit easier for me to get started. Hope this ‘note to self’ it helps you too!

The post Make It Meaningful by Clark Quinn – Idea Extractions – Part Three first appeared on The Upside Learning Blog.

In my previous blog, I covered the first big idea of Action-Reflection from Clark Quinn’s book. Now, I want to delve deeper into the next crucial idea: “Hook & Land.” Although Clark covers these concepts in separate chapters, I see them as complementary. On the surface, it sounds simple: hook learners with their motivators, then provide an exceptional experience so that learning sticks and impacts performance outcomes. However, Clark’s explanations have made me rethink my design practice.

(“Hook & Land” is what makes your learning experience meaningful)

Clark starts the chapter by saying, “To get people to engage, you have to open them emotionally before addressing them cognitively. People learn better when they care.” This statement sets the foundation for the entire chapter and highlights the importance of finding the right hook for learning.

In my opinion, you don’t need a single, perfect hook in a learning experience. Instead, many small hooks followed by small learning experiences can create a holistic learning experience.

The Emotional Buy-In

Before discussing how to get learners emotionally invested, Clark mentions two essential conditions that must be met:

• Identify tasks learners want to do but aren’t currently doing
• Identify a clear, observable outcome that determines learner performance

Once these conditions are fulfilled, you can design the hook for the learning experience. The tactics to create a great hook are described in detail in the book, and I wouldn’t do them justice by condensing them into this blog. However, here are some ways Clark suggests to build a great hook:

• Invoke learner curiosity
• Follow the three pillars of Self-Determination Theory: Autonomy, Competence & Relatedness
• Frame the “what’s in it for me” for learners carefully
• Show real-world consequences for learners
• Follow learner motivations

Once you have a great hook, you can focus on landing the learning experience. Clark defines landing as a state where learners are deeply engaged, in a flow, and aware of learning goals. To create a great learning experience that lands, Clark suggests several components:

• Learners should have a clear understanding of learning goals
• Challenges should be well-balanced
• Provide contexts where learning will be useful and can be dealt with creatively
• Show real consequences for wrong/mis-directed options, with feedback first focusing on consequences, followed by didactic feedback.

Clark also mentions that the principles behind great games and engaging learning are the same. He says that learning should be “hard fun,” and I would argue that to make learning engaging, it should be more game-like. Our world is becoming game-like, so why shouldn’t learning follow suit?

Finally, Clark emphasizes the importance of practice, particularly practice that boosts learner confidence to perform and make correct decisions on the job.

I’ve summarized Clark’s main ideas, but haven’t covered every single concept from his book. It’s time to put these concepts into practice!

The post Make It Meaningful by Clark Quinn – Idea Extractions – Part Two first appeared on The Upside Learning Blog.

Part One – Action & Reflection

Clark starts the book by saying, “The ability to make better decisions is why we learn.” As a gamification designer, I tend to think about everything from the perspective of a game, and it amazes me how this statement holds true for every single game.

We predict/suspect things working a certain way, and then act according to our prediction/suspicion in life as well as in a game. One of the common things in most games and in real life is RNG (Random Number Generators) or randomness, and however you choose to act, the outcome will always be laced with randomness.

Clark goes on to mention that according to a neuroscientist ‘Karl Friston’, “We learn to minimize the gap between our predictions and the real outcomes.”

Therefore, Learning = Better Decisions = Accurate Predictions & Outcomes

So our actions are essentially the calculated predictions that lead us to our desired outcomes.

A great poker player Annie Duke, the author of ‘Thinking in Bets’ would definitely agree with this as poker is about constantly trying to increase your odds of winning the hand through better decisions that you make by learning more about the play styles of other players.

(Oh, the interconnectedness!)

Clark goes on to mention, “Learning in life, I maintain, is action & reflection. We do things, and when we reflect on them, we can detect patterns and build explanatory models that guide our choices.” He also mentions that usually the action-reflection bit isn’t always consciously attained, but when we make a conscious effort to learn through the loops of action-reflection, we learn deeply – that’s how our cognitive architecture is designed.

All Chess Grandmasters reflect on their games and remember all the move sequences that were made by them and their opponents from 20 years back and the nuances of why those moves were made. Expert gamers have always similarly reflected on their games and actions.

While deliberate action reflection makes total sense within gaming contexts and is widely adopted by professional gamers and athletes, designing for guided reflections within learning experiences may get tricky in corporate settings, especially if the problem to be solved for isn’t diagnosed with clinical accuracy. This would be my only bone to pick with Clark.

What I understand from the broader idea of action-reflection is that effective learning transfer happens when we design actions that help the learners become better decision-makers in their role. This has to be supported by guiding them towards reflecting on those decisions so that better decisions and the reason for them being better are cemented in the learners’ brains. A continuous loop of action-reflection would make for a formidable learning experience.

Clark also stresses the idea that the focus of learning experiences should be decisions. He mentions that “What will typically make a difference in performance is the ability to make the right decisions.“ He also goes on to quote one of my favorite game designers ‘Sid Meier’, the designer of the famous Civilisation series who stated, “A game is a series of interesting decisions.” Clark adds to this by saying, ”Good learning is a series of important decisions.”

For those who haven’t played the Civilisation series, there’s no plot as such in Civilisation and yet every campaign has a different path to success, and you make stories that are extremely unique to you. So, Civilisation is essentially a series of game mechanics that, if put together, give you a different story every single game depending on how you play it and what decisions you make.

While ‘Good Learning’ according to Clark is a series of important decisions, can it also be a unique and personal experience every time you choose to go through those decisions to help guide your reflection with a lot more ease? Watch this space for more on this.

The post Make It Meaningful by Clark Quinn – Idea Extractions – Part One first appeared on The Upside Learning Blog.

An important thing to keep in mind is that people’s properties aren’t perfectly predictable. We might be able to design and build a bridge or a chair according to material specifications, but human brains have more variability. In fact, it’s been said that the human brain is the most complex thing in the known universe! To believe that we’re going to systematically impact it, changing the very behavior, with a waterfall approach, isn’t realistic. Instead, we’ll need to design, test, and refine.

Interface design, and similar related fields, are ahead of the learning design field when it comes to testing. They expect to test, and design it into their solutions. We notoriously haven’t, despite moves from ADDIE to iterative ADDIE. Newer models, such as Michael Allen’s SAM and Megan Torrance’s LLAMA have iteration built into their processes, as has David Merrill’s Pebble in a Pond. Do include testing and revision cycles in your project plan, allocating more as the scope of the work increases.

In our analysis phase, we should identify metrics that determine our outcome. Our performance objective stipulates how we will know they can do it, but it should also be derived from a real metric in the organization that needs to be moved. Then, we should iterate until our solution makes the needed change. However, there are other metrics we should care about.

For one, we need to test for usability before anything else, if there are problems in navigating the learning experience and making decisions that could mask a learning design problem. The standard things to evaluate are ability to accomplish tasks, errors in doing so, time to accomplish the tasks, time to learn, and to relearn, and satisfaction. All these should essentially be moved to where there’re no errors in accomplishing the goals, and tasks take only a minimum of time, e.g. one click to make a choice.

Once we’ve ensured that given the tasks, people can accomplish them with essentially no errors in a reasonable amount of time, then we want to know if the learning experience actually accomplishes the learning. Here, we’re looking for first ability to perform after the learning experience, then transfer from the learning experience to the workplace, and finally achieving the actual impact in the organization. The performance objective guiding the final practice achieves the ability to perform. Next, we need evidence from either instrumented tools (e.g. a digital record of action) or supervisor report to see if we’re achieving change in the workplace. We finally look at the business metrics to determine if we’ve moved the needle.

After we’ve determined that we’re achieving our goals, we should be also thinking about the ‘learner experience’. Tuning the experience to achieve ‘hard fun’ both increases learner experience and optimizes the time to success. If learning sticks faster and better, our results are obtained sooner. We don’t need to go to extremes, such as measuring adrenaline in the blood or galvanic skin resistance, learner subjective experience is sufficient. They can tell you!

Our process should be iterative and escalate slowly. Our testing should be low tech, and only gradually move in audience from the team to the learner. Our prototypes can evolve from mental simulation, to paper prototypes, to the key interactions before adding all the window dressing. We similarly should first test upon ourselves, then uninvolved teammates, reserving the (typically hard to arrange and possibly expensive) learners until the bugs are worked out and it’s deemed to be ready for such exposure.

Testing should be early and often, as the mantra has it. Also, the core interactions, the practice, should be prototyped and tested first. Practice is the critical step in learning, and it’s where interactions happen. Getting the practice right, then gradually developing associated materials, as well as the introduction and closing, is the recommended approach.

The usability field suggests that prototypes are supposed to be thrown away. The idea is that you’re less prone to be willing to start anew if you’ve invested too much, thus the call for low tech. Prototypes can be hand-drawn sheets of paper or made up of post-its, in early stages. Later on, you’ll probably implement sample practice interactions before working on models, examples, intros, and more.

There’s more beyond the testing and release. One of the questions I often like to ask my audiences is whether they’ve any content on their server or LMS that’s out of date, but still extant. Everyone raises their hands. This is content management. Every piece should have an owner, and a review date. For content that’s less volatile that date may be in years, but for things that change fast, you’ll want a more frequent review. Ownership of the content may be matrixed, with a design team member and a subject matter expert, but it needs a process.

Similarly, content granularity is an issue. The movement to microlearning, regardless of how ill-conceptualized, is yielding smaller chunks. If you look at content experts like web marketing, you’ll see that they have a well-defined structure and tags that allow them to assemble content by rule (e.g. recommendations, such as at Amazon) instead of hardwiring. Our long-term goal should be to do the same. To the extent we identify, and separate out, the elements of learning, we’re creating the infrastructure necessary to take advantage of personalization and adaptive learning platforms when we’re ready. The technology already exists, it’s now up to our initiative.

We also shouldn’t expect our learning to stand on its own. We should be thinking about extending the experience. I know one L&D unit that didn’t release content until the manager or supervisor training had also been developed. We typically can’t prepare learners completely, but get them to a minimal level and then expect them to continue to develop. We shouldn’t leave that to chance, but instead should have implemented mechanisms to support ongoing learning, whether on-the-job coaching, reactivation mechanisms, or communities of practice.

Learning is a complex process, and success in creating deeper learning is a matter of attending to the details. Yet, learning having impact is far superior to the current estimates that only 10% of training interventions are actually effective. That’s a tremendous waste of money that we can and should do something about. Doing Deeper Learning is a necessary start. Let’s take that step and start achieving real outcomes.

The post Developing Deeper Learning first appeared on The Upside Learning Blog.

Now, the term ‘emotion’ is a shorthand. In cognitive science, we talk about – in addition to the cognitive – the affective and conative parts of ourselves. The affective refers to, essentially, our personality. While this is important, it’s also relatively ‘fixed’, in that our inherent characteristics are presumed to be stable, at least by adulthood. To be clear, I’m talking about scientifically valid assessments of personality, such as the Big 5 or HEXACO. Consider yourself warned about other, for-profit, instruments!

The third element, conative, however, is important and different. Here, we’re talking about intention, whether to perform or learn. We can be committed, or not. The elements here tend to be characterized as motivation, anxiety, and confidence. While these are not, technically, emotions, the term helps differentiate them from the cognitive aspects of learning, and is easier to communicate. Hence, the label ‘emotion’. Each has its characteristics.

Motivation both engages learners initially, and maintains persistence. Of course, we have to build and maintain motivation for learning to be optimally effective. Few people learn best when they don’t have a vested interest in learning. Two major ways of thinking about motivation help, What’s In It For Me (WIIFM), and Self-Determination Theory.

I posit that the initial hook comes from learners recognizing the personal value of the learning to them. That is, they have to see that it’s relevant to their interests and their goals. In other words, it’s the WIIFM. I suggest that we can make this manifest to learners by helping them viscerally understand the positive consequences of having the knowledge or the negative consequences of its absence. If we’ve determined good performance objectives, through our analysis, the outcome should be valuable. We just have to help learners see it. That’s what an introduction is for, and I suggest opening up learners to learning should come even before activating relevant knowledge (another introduction task). I also believe that if we open up the emotional aspect of the learning experience with the introduction, we should finalize that trajectory in closing the experience.

Second, there’s the Self-Determination Theory of Deci & Ryan. Simplistically, this theory posits that motivation is comprised of autonomy to pursue interests, mastery of skills and support to achieve success, and relatedness to others who care about the individual and their goal. Tapping into these elements is an important component of maintaining interest. We provide an appropriate level of challenge in the practice, which builds the learners’ mastery (which we should communicate). They should have committed to the learning from the introduction, which is autonomy (it should be an option to participate, this suggests, or at least a choice). Finally, that the design explicitly cares about the learner experience demonstrates a concern for the learners’ progress, which helps establish relatedness.

An element that can interfere with successful learning is anxiety. I’ll suggest you won’t do your best learning if you’re really anxious about the experience. An important mechanism is establishing ‘safety’, in that it’s okay to ask questions and make mistakes. Providing practice before it’s measured is one way, another is for the ‘instructor’ (even in asynchronous learning) to make or share mistakes. Also, using an informal tone helps, and even some idiosyncratic (but relevant) humor. We often don’t explicitly consider this element, and we should.

The other component is confidence. We don’t expect our learners to start out competent, nor should we expect they’re confident. Can we help them develop their confidence? An important metric should be that they’re confident enough at the end of the learning experience to at least give it a try after the experience. We also should be developing beyond that level, over time.

Given that these elements are non-cognitive, we can’t assume we’ll get them right from the beginning. We have good guidance, but we’ll want to make our best attempt and then test and tune. (A recurrent theme here.) We should set metrics for engagement as well as effectiveness. A colleague recently opined that engagement doesn’t matter, but I’ll suggest that if you’re on target with the goal of learning, managing the challenge and making it safe are useful contributions to optimizing the learning experience. We can’t do it if we don’t understand emotion, and consciously address it.

Emotion matters in learning. To me, it’s the difference between instructional design and learning experience design. Let me be clear, I’m not talking about making learning trivially fun. There are some who advocate that learning should be enjoyable, but there’s little evidence that this makes for effective outcomes. Similarly, there are others who argue learning should be onerous, that you can’t learn unless you struggle. Again, is it optimal? I like the term I’ve heard attributed to educator Seymour Papert: learning should be “hard fun”. That is, learning that has you cognitively and emotionally engaged, where it’s difficult enough to be challenging, but not so hard as to be frustrating.

There’s converging evidence that we should consider creating experiences. For instance, game designer Raph Koster’s A Theory of Fun that postulates that what makes games fun is learning. There’s also an alignment between Csikszentmihalyi’s Flow and Vygotsky’s Zone of Proximal Development that suggests what optimal learning is. In both cases, the optimal zone is between challenges that are so easy so as to be boring and so difficult as to be frustrating. The optimal flow and optimal learning both occur in this zone! In my own research (c.f. Engaging Learning), I posited an alignment between what makes engaging experience and what makes effective education practice.

So, using emotion properly is a component of Deeper Learning. We want learning to be a true experience. Learning can, and should be ‘hard fun’! Let’s design for it.

The post The Role of Emotion in Deeper Learning first appeared on The Upside Learning Blog.

First, we typically don’t develop learners to a whole new level of expertise before returning them to the fray. That requires considerable time and, consequently, expense. Instead, we bring them to a minimum level of capability, and then expect to develop them while working. (At least, we should have this expectation!) Too often, we instead practice ‘til they get it right, not until they can’t get it wrong. This is a major problem with much of our current learning.

This further development (extension of experience) can take a number of different paths, depending on a variety of factors. If the task happens frequently, preparing supervisors to observe and coach, after the learning experience, makes sense. If it’s rarer, that should be coupled with reactivation, whether reconceptualization (new models), recontextualization (more examples), and/or reapplication (more practice). We can also prompt reflection or reports on how it’s being applied. However, we should explicitly design for and deliver the reflection that accompanies learner action after the learning experience.

That last option, the question about application, gives us insight into the actual impact. We can also ask supervisors for changes observed in the workplace, or look at external metrics (which we should be looking at to determine whether the intervention had the necessary effect). Our goals should be impact, and we not only want to engineer it, but then assess it as part of a process of determining whether we’re done.

A second area is the area of content. Too often, it appears to be a generic label for things that may be necessary. Instead, content should be minimal, focusing on two major categories of content that assists in learning: One is models, the other is examples. Each has specifics that we should understand. We get models and examples from our subject matter experts in analysis. Our job is to make these elements comprehensible and useful.

Models are conceptual and causal. Model elegance comes from providing the maximum benefit with the minimum content. They should provide guidance about how to proceed by explaining how the world works or serve as a framework for evaluating performance. They are frequently represented by visual support: a diagram or equation, or an animation if dynamics play a role.

They are causal in that they give a basis for predicting outcomes of choices. Our brains build causal explanations, and if we build a bad one, we don’t tend to replace it—instead, we patch it. It is therefore that we should provide good models up front, and show how they play out. They’re also conceptual, not tied to specific items. We want learners to match models to the specifics of a situation (appropriately).

That latter goal, seeing how models play out in real contexts, is the role of the other content component, examples. Examples are specific situations that are addressed. They detail situation, steps, and outcomes. They should also, importantly, show the underlying thinking. For instance, they should explicitly refer to the models.

Cognitively annotated examples, where the underlying thinking is made clear, are also known as worked examples. John Sweller’s research on cognitive load theory has shown that for novices, seeing worked examples is a useful precursor to actually engaging in practice. As a consequence, consider showing examples before practice, at least for those learning new skills as opposed to those continuing their development.

Examples are best when they’re minimal but meaningful. They should have the form of a story, and naturally incorporate the thinking as well as any necessary dialog or narrative. Formats can vary; graphic novel formats are engaging and allow thought bubbles to show underlying thinking, and videos can be compelling, but even prose can work. The point is to make the challenge and the outcomes plausible and visible. Ideally, the stories are intrinsically engaging, too, with important outcomes.

One other important element goes across examples and practice. The contexts that are seen and used cover both. Here I mean the particular situations, challenges and outcomes, and how they differ from one another. Most every skill we’re developing has a ‘space’ of application, a range of circumstances in which it’s relevant. For some, this is small, e.g. how to operate projector X. For others, such as negotiation, the space can be large, covering with vendors, employers, shopkeepers, and more. In broad transfer situations, we can’t provide sufficient contexts to span the entire space, so we have to provide a suite of contexts that will successfully transfer to the necessary areas. Those contexts, seen across examples and practice, provide the space of transfer. If there are too few, we likely won’t get sufficient retention nor transfer. If they are too narrow in scope, we won’t get sufficiently broad transfer. Yet, practically, we don’t want to build too many.

The success path is to identify the minimal set that provides the broadest transfer. The suite of contexts we need should include an initial one that serves as the simplest example. Then we should gradually add complexity at the same time that we cover different circumstances that will generalize best to the ones the learner should be able to handle. Unfortunately, there’s no algorithm to this, as it depends on the complexity of the skill, the space of application, frequency of application, and importance of the skill. The best approach is to create an initial draft, and test and refine until you get the outcomes you need.

This is more work than you’re probably used to. The good news is that it becomes easier and even second-nature, with practice. The bad news is it still adds in a requirement for testing. However, that’s to be expected when you’re moving from dumping content to actually creating sustained change. Which, after all, is the ultimate goal of deeper learning! It’s past time we do it right.

The post Deeper Learning Design: Beyond Practice first appeared on The Upside Learning Blog.