Mike Taylor

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Category: Learning Science

  • 10 Popular L&D Ideas Worth Revisiting

    10 Popular L&D Ideas Worth Revisiting

    Recently, I read The Untold Story of Bloom’s Taxonomy by Carl Hendrick.

    That name instantly took me back.

    Like many people in Learning and Development, I was introduced to Bloom’s years ago. I’ve seen it referenced in conference presentations, certification programs, instructional design books, and countless conversations about learning objectives. But as I read the article, I realized something uncomfortable: much of what I thought I knew about Bloom didn’t come from Bloom.

    It came from pyramids, templates, conference talks, and other people who had learned it the same way.

    That made me wonder:

    How many L&D “facts” are actually simplified versions of the original idea?

    This isn’t an attempt to tear down the profession’s favorite models. Most became popular because they’re useful. But useful isn’t the same as proven, and a model isn’t a law.

    Ideas also change as they travel. Someone trims a complex concept for a workshop. Someone else turns it into a graphic. A catchy statistic gets added. Eventually, the simplified version becomes easier to remember than the source.

    And that’s when things get interesting.

    The goal isn’t to debunk these ideas. It’s to revisit them. Some turn out to be myths. Some are good models that have been stretched beyond their original purpose. Others work quite well, but only under the right conditions.

    Here are ten L&D ideas that became simpler, more certain, and sometimes less accurate as they traveled.

    1. People Learn Best in Their Learning Style

    Verdict: 🚫 Myth

    Visual learners need pictures. Auditory learners need sound. Kinesthetic learners need hands-on practice.

    It has a nice ring to it. It also feels respectful. People are different, so surely we should teach each person in the way they prefer.

    The first part is true. People do have preferences. The second part is where the evidence weakens.

    There has been a lot of research on whether matching instruction to a learner’s preferred style improves outcomes. All of them concluded there was not an adequate evidence base for using learning-style assessments in general educational practice.

    That doesn’t mean visuals are bad or hands-on practice has no value. The best format often depends on the content. A map helps people navigate. Audio helps with pronunciation. Physical practice helps when learning a physical skill.

    The content should guide the method, not a label attached to the learner.

    Takeaway: Preferences are real. The evidence supporting instruction matched to individual learning styles is much weaker than many people assume.

    Sources

    • Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2009). Learning Styles: Concepts and Evidence.
    • Coffield, F., Moseley, D., Hall, E., and Ecclestone, K. (2004). Learning Styles and Pedagogy in Post-16 Learning.

    2. 70-20-10 Is a Proven Formula

    Verdict: ⚠️ Useful Idea, Weak Evidence

    You probably know the numbers:

    • 70% from experience
    • 20% from other people
    • 10% from formal learning

    They’re easy to remember, which may explain why so many people started treating them as a formula rather than an observation.

    The model grew from reports about how successful managers believed they had developed during their careers. It wasn’t a controlled experiment proving that everyone learns in those exact ratios.

    Researcher Alan Clardy reviewed the evidence and argued that support for the specific percentages was far weaker than many practitioners assume.

    None of that makes workplace experience unimportant. People clearly learn through assignments, coaching, observation, feedback, and mistakes. The issue is that an observation gradually became a prescription. Somewhere along the way, a useful reminder to look beyond formal training started being treated like a universal rule.

    Takeaway: Use 70-20-10 as a prompt to think broadly about development, not as arithmetic you must obey.

    Sources

    • Lombardo, M., and Eichinger, R. (1996). The Career Architect Development Planner.
    • Clardy, A. (2018). 70-20-10 and the Dominance of Informal Learning: A Fact in Search of Evidence.

    3. Bloom’s Taxonomy Is a Staircase

    Verdict: ⚠️ Useful Model, Often Misapplied

    Most of us know Bloom’s as six levels stacked in a pyramid:

    Remember. Understand. Apply. Analyze. Evaluate. Create.

    That image encourages a tidy story. Start at the bottom, move upward, and aim for the top because creating is always best.

    The real story is more nuanced.

    Bloom’s original taxonomy was designed to classify educational objectives and create a shared language for discussing them. It included a hierarchy, but it wasn’t intended as a universal instructional process that every lesson must follow.

    Creating isn’t automatically the right goal. If I’m learning an emergency procedure, accurate recall may matter far more than producing something new. Experts also move back and forth among different types of thinking rather than climbing a ladder one rung at a time.

    Takeaway: Bloom’s can help align goals, instruction, and assessment. It shouldn’t dictate a rigid sequence for every learning experience.

    Sources

    • Bloom, B. (1956). Taxonomy of Educational Objectives.
    • Anderson, L., and Krathwohl, D. (2001). A Taxonomy for Learning, Teaching, and Assessing.
    • Hendrick, C. The Untold Story of Bloom’s Taxonomy.

    4. People Remember 90 Percent of What They Do

    Verdict: 🚫 Myth

    You’ve probably seen the graphic.

    People remember:

    • 10% of what they read
    • 20% of what they hear
    • 90% of what they do

    Those numbers are frequently connected to Edgar Dale’s Cone of Experience.

    Perhaps the most surprising part of the famous retention chart is that the percentages most people remember never appeared in Dale’s original work.

    His cone described different types of learning experiences ranging from more concrete to more abstract. The retention statistics appeared later and have no clear research foundation.

    That doesn’t mean practice is ineffective. Practice can be incredibly valuable. But retention depends on many factors, including prior knowledge, attention, retrieval, feedback, spacing, and context. No honest chart can reduce all of that complexity to three neat percentages.

    Takeaway: Learning by doing can be powerful. The famous retention numbers are not supported by strong evidence.

    Sources

    • Dale, E. (1946). Audio-Visual Methods in Teaching.
    • Thalheimer, W. (2006). People Remember 10%, 20%…Oh Really?

    5. People Have an Eight-Second Attention Span

    Verdict: 🚫 Myth

    The story goes like this:

    Human attention spans have shrunk to eight seconds. That’s supposedly shorter than a goldfish.

    Therefore, every learning experience should be tiny.

    It’s a memorable claim. It’s just not a well-supported one.

    While attention is undoubtedly affected by our environment, researchers have struggled to find evidence supporting a universal eight-second human attention span. People still lose themselves in novels, hobbies, games, and multi-hour projects.

    The issue isn’t that humans suddenly forgot how to focus. It’s that attention now competes with more distractions than ever.

    Takeaway: Don’t design around a goldfish statistic. Earn attention through relevance, clarity, challenge, and good pacing.

    6. Adults Learn in a Completely Different Way Than Children

    Verdict: ⚠️ Useful Perspective, Ongoing Debate

    Adult Learning Theory gave L&D an important reminder: adults bring experience, responsibilities, motivations, and a need for relevance.

    The trouble begins when those useful observations become a rigid divide.

    Adults are self-directed. Children are dependent. Adults are internally motivated. Children are externally motivated.

    Real people are messier than that.

    Recent critiques have questioned whether adult and child learning differ as sharply as andragogy is often portrayed. Some scholars argue that while adults and children may learn in different contexts, the underlying mechanisms of learning are often more similar than traditional descriptions suggest.

    That’s not an argument against relevance, autonomy, or experience. It’s a reminder that human learning is usually more complicated than a checklist.

    Takeaway: Design for the people and situation in front of you, not for a simplified description of what all adults supposedly have in common.

    Sources

    • Knowles, M. (1973). The Adult Learner: A Neglected Species.
    • Knowles, M. (1980). The Modern Practice of Adult Education.
    • Moll, I. (2024). A Psychological Critique of Knowles’ Andragogy as a Theory of Learning.

    7. Kirkpatrick Proves the Training Worked

    Verdict: ⚠️ Useful Framework, Often Oversimplified

    The problem with Kirkpatrick isn’t Kirkpatrick. It’s the tidy staircase many of us were taught.

    Too often, the four levels become a checklist: Did people like the training? Did they pass the test? Did their behavior change? Did business results improve?

    That creates a neat story from Reaction to Results. But a neat story is not proof. Each connection still needs evidence.

    The updated model is more connected and iterative than the familiar staircase suggests. It starts with the results an organization needs, works backward to the behaviors that support them, and asks what must happen on the job to sustain those behaviors. Training is only one part of that system.

    So the question isn’t whether Kirkpatrick is right or wrong. It’s whether we use the model to guide better questions or treat it as automatic proof that training caused the result.

    Takeaway: Use Kirkpatrick to build a chain of evidence, not a ladder of assumptions. Start with the result, identify the behavior that could influence it, and test each connection.

    Sources

    • Kirkpatrick, J. D., & Kirkpatrick, W. K. (2016). Kirkpatrick’s Four Levels of Training Evaluation. ATD Press.
    • Cahapay, M. (2021). Kirkpatrick Model: Its Limitations as Used in Higher Education Evaluation.

    8. Engagement Means Learning

    Verdict: ⚠️ Useful Measure, Weak Evidence

    The room was lively. The chat was flying. Learners were clicking, commenting, laughing, and participating.

    Success, right?

    Maybe.

    One of the easiest traps in L&D is confusing evidence of activity with evidence of learning. The two often overlap, but they’re not the same thing.

    Researchers and learning scientists have long argued that learners are not especially good judges of their own learning. In Make It Stick, Brown, Roediger, and McDaniel describe how learners often mistake familiarity, fluency, and enjoyment for durable learning. Techniques that feel easy and engaging can create an illusion of learning, while techniques that feel more difficult may produce stronger long-term retention.

    The same theme appears throughout How Learning Happens by Paul Kirschner and Carl Hendrick. Learning is not always obvious while it’s happening. A learner can feel confident without learning much, and they can feel challenged or uncertain while making significant progress.

    That doesn’t mean engagement is unimportant. Few people learn effectively when they are distracted, confused, or completely disengaged. Attention matters. Motivation matters. Participation matters.

    The problem comes when we stop there.

    A crowded discussion board doesn’t prove understanding. High completion rates don’t prove retention. A great satisfaction score doesn’t prove behavior change or improved performance.

    In other words, engagement may be a useful leading indicator, but it’s a poor substitute for evidence that learning actually occurred.

    Takeaway: Engagement can support learning, but it isn’t learning. Measure attention, participation, and satisfaction if they’re useful. Just don’t mistake them for retention, transfer, or performance improvement.

    Sources

    • Brown, P., Roediger, H., and McDaniel, M. (2014). Make It Stick: The Science of Successful Learning.
    • Kirschner, P., and Hendrick, C. (2020). How Learning Happens.
    • Clark, R., and Mayer, R. (2023). e-Learning and the Science of Instruction.

    9. Microlearning Is Better

    Verdict: ✅ Context Dependent

    Unlike some items on this list, microlearning has meaningful evidence behind it.

    Recent systematic reviews have reported generally positive outcomes across multiple studies while also noting significant variation in definitions, implementation approaches, and research methods.

    The issue isn’t that microlearning never works. The issue is that “make it shorter” has become an answer before we’ve asked the question.

    A short lesson may be ideal for a refresher, job aid, or narrowly focused task. Building a complex skill is a different challenge entirely. Some capabilities require time, feedback, repetition, coaching, and sustained practice.

    Breaking a weak course into ten shorter pieces doesn’t improve the content. It simply gives you ten smaller pieces of weak content.

    Takeaway: Short is a format, not a strategy. Good design starts with the need.

    Sources

    • Monib, W., Qazi, A., and Apong, R. (2025). Microlearning Beyond Boundaries: A Systematic Review and a Novel Framework for Improving Learning Outcomes.

    10. Gamification Works

    Verdict: ✅ Context Dependent

    Points. Badges. Leaderboards.

    Done, right?

    Not quite.

    Research generally supports the idea that well-designed gamification can improve learning outcomes. However, the size of those effects varies considerably depending on the audience, content, design approach, duration, and learning environment.

    That’s a much more useful conclusion than either extreme: “Gamification works” or “Gamification is a gimmick.”

    A leaderboard may motivate one audience and discourage another. A storyline may add meaning or add noise. A challenge may promote useful practice or reward speed on the wrong task.

    Like most instructional tools, it depends on how it’s used.

    Takeaway: Game elements are ingredients. Their value depends on what you’re trying to make.

    Sources

    • Li, M., Ma, S., and Shi, Y. (2023). Examining the Effectiveness of Gamification as a Tool Promoting Teaching and Learning in Educational Settings: A Meta-Analysis.
    • Kapp, K. (2012). The Gamification of Learning and Instruction.

    The Bigger Lesson

    The point isn’t that L&D has been foolish. It’s that useful ideas change as they travel.

    A model starts with nuance. Someone trims it for a workshop. Someone turns it into a pyramid. A memorable statistic gets attached. Before long, the simplified version becomes easier to remember than the source.

    That’s how a guide becomes a rule, a ratio becomes a law, and a preference becomes a learning need. The transformation happens gradually, usually with good intentions. Each simplification makes the idea easier to teach and remember. Unfortunately, it can also make the idea less accurate.

    We don’t need to throw away every familiar model. We do need to hold them a little more lightly.

    Ask where an idea came from. Check what the original source actually claimed. Look for the limits, not just the headline. Then decide whether the model fits the problem in front of you.

    Models help us think. Problems begin when we stop thinking and let the model do it for us.

    The goal isn’t to collect more frameworks. It’s to stay curious enough to revisit the ones we already know.

    Because in a profession built around learning, some of the most valuable lessons come from what we’re willing to unlearn.

  • Why Your 30-Minute Course Is Losing to a 60-Second Video

    Why Your 30-Minute Course Is Losing to a 60-Second Video

    A learner opens their laptop on Monday morning.

    Before they’ve finished their first cup of coffee, they’re facing dozens of unread emails, a handful of Teams notifications, back-to-back meetings, and a growing list of project deadlines. Somewhere in the middle of all that sits your carefully designed 30-minute eLearning course.

    Now ask yourself a simple question:

    What are the odds your course wins that battle for attention?

    Not against another course.

    Against everything else competing for that learner’s time.

    That’s the reality many learning teams are facing today. We’re no longer operating in a world where training has a captive audience. Every piece of learning content must compete with email, chat messages, meetings, project work, internal communications, and an endless stream of digital distractions.

    The challenge isn’t that people don’t want to learn.

    The challenge is that attention has become one of the most valuable and limited resources in the workplace.

    Learning Doesn’t Compete With Learning Anymore

    For years, many L&D teams have operated under an assumption that sounds reasonable on the surface:

    If we create good content, people will engage with it.

    Sometimes they do.

    But increasingly, the quality of the content is only part of the equation. Before learning can happen, learners must first decide that the experience is worth their time and attention.

    That’s why a two-minute video can sometimes outperform a 30-minute course.

    Not because it’s inherently better.

    Not because it contains more information.

    But because people actually watch it.

    This isn’t really a discussion about video length. It’s a discussion about attention.

    This Is Not an Argument for Shorter Learning

    Let’s clear up a common misconception.

    I’m not suggesting every course should become microlearning.

    Complex skills take time to develop. Leadership capabilities aren’t built in sixty seconds. Technical expertise doesn’t emerge from watching a few short videos. Practice, coaching, reflection, and application still matter.

    Depth matters.

    What has changed is the path people take to reach that depth.

    Many learning teams still design as though attention is guaranteed. Modern learners remind us every day that it isn’t.

    Attention must be earned first.

    Only then can learning begin.

    What the Research Actually Says

    One reason lengthy learning experiences often struggle is cognitive overload.

    Cognitive Load Theory suggests that instructional designers should reduce unnecessary mental effort so learners can devote their limited working memory to what actually matters: learning. Research consistently shows that instructional choices can either support or hinder that process. [link.springer.com], [journals.sagepub.com]

    The implication isn’t that shorter is always better.

    The implication is that focused is better.

    Clear is better.

    Relevant is better.

    Designing a 30-minute course isn’t the problem. Designing a bloated 30-minute course often is.

    Every screen, interaction, example, and activity should earn its place.

    Marketers Solved This Problem Years Ago

    The interesting thing is that another profession has been dealing with attention challenges for decades.

    Marketing.

    Marketers know that an audience rarely takes action after a single exposure. That’s why they don’t launch a campaign with one email and hope for the best.

    They build ecosystems.

    One webinar becomes social posts, short videos, podcasts, infographics, email sequences, and follow-up content. The goal isn’t simply distribution. The goal is reinforcement.

    The same principle applies to learning.

    Too often, a learning need is identified, a course gets built, and everyone moves on.

    From the project’s perspective, the work is done.

    From the learner’s perspective, the journey has barely begun.

    Think Campaigns, Not Courses

    This is where I believe L&D can borrow one of marketing’s most valuable ideas.

    Stop thinking in courses.

    Start thinking in campaigns.

    Imagine a manager training program.

    The traditional approach might look like this:

    • Launch a 45-minute course.
    • Send two reminder emails.
    • Track completions.

    Done.

    A campaign approach looks different.

    Week one might include a teaser video and an attention-grabbing story.

    Week two introduces the core learning experience.

    Week three gives managers a practical challenge to complete with their teams.

    Week four shares success stories and reflection questions.

    A month later, learners receive another reminder tied to a real-world scenario.

    Same topic.

    Same learning goal.

    Completely different learning experience.

    Why This Approach Works

    One of the most replicated findings in learning science is the spacing effect.

    Research has repeatedly shown that people tend to retain information more effectively when learning is distributed over time rather than delivered in a single event. [cognitivep…hology.com], [files.eric.ed.gov]

    That finding should make every learning professional pause.

    Because much of corporate training is still built around one-and-done experiences.

    Complete the course.

    Check the box.

    Move on.

    Yet the evidence suggests that lasting retention and behavior change often require repeated exposure and reinforcement. [cognitivep…hology.com], [files.eric.ed.gov]

    In other words, the science supports the campaign mindset.

    The Metric We Should Care About

    Another lesson worth stealing from marketers is how they define success.

    Marketing teams don’t obsess over whether someone saw a piece of content.

    They care about whether something happened afterward.

    Did someone click?

    Did they subscribe?

    Did they buy?

    L&D should ask similar questions.

    Did someone apply the skill?

    Did behavior change?

    Did performance improve?

    Did risk decrease?

    Completion rates have value, but they’re not the outcome we ultimately care about.

    They’re simply a milestone along the way.

    In fact, the idea of borrowing attention, engagement, and reinforcement strategies from marketing is something I’ve become increasingly passionate about over the years. It’s also a theme I explore in much greater depth in my book, Think Like a Marketer, Train Like an L&D Pro, where I share practical ways learning professionals can apply proven marketing principles to drive engagement and behavior change. You can learn more at Train Like a Marketer.

    Final Thought

    The future of workplace learning isn’t about making everything shorter.

    It’s about making everything more intentional.

    A 30-minute course can absolutely outperform a 60-second video when it’s part of a broader ecosystem designed to capture attention, reinforce key ideas, and drive action over time.

    The organizations that succeed won’t be the ones that create the most content.

    They’ll be the ones that create the most meaningful touchpoints.

    Because learning doesn’t compete with learning anymore.

    It competes with attention.

    And attention is the first learning objective.

  • Five Studies That Shaped My Thinking About Training

    Five Studies That Shaped My Thinking About Training

    When training fails, our first instinct is often to add something.

    Another explanation. Another example. Another module. Maybe a quiz with three answers nobody could possibly get wrong.

    But what if content isn’t the problem?

    I recently reviewed 50 studies from psychology, behavioral science, and learning research. I chose them for their influence, historical value, and usefulness at work.

    Several of them highlight that training isn’t always the answer.

    Here are five that changed how I think about performance.

    Get the complete 50-study field guide

    Want the other 45? I compiled all 50 studies, along with their limitations, workplace applications, and practical ideas, into a free field guide.

    ​

    Download the 50 study field guide

    ​

    A layout problem can become a learning problem

    Picture a diagram on one side of a screen and its explanation on the other. Your eyes bounce back and forth. Meanwhile, your brain tries to hold one piece in memory while searching for the next. That mental juggling has a cost. Paul Chandler and John Sweller called it the split-attention effect. When people must combine related information from different places, the layout itself can interfere with learning.

    Split attention—and related attention problems—can appear when we:

    • Put a legend far from the visual it explains
    • Separate instructions from the task
    • Make employees switch between systems to assemble one answer
    • Show visuals that compete with the narration
    • Add decoration that steals attention

    The lesson is not “make everything shorter.” It is this:

    Make important relationships easier to see.

    Try this: Find one screen that separates information people must understand together. Put those pieces side by side—but don’t duplicate information that already makes sense on its own. ​

    Explore the split-attention research →​

    ​

    Remembering beats reviewing

    Rereading feels productive.

    The words look familiar. The page makes sense. We nod along and mistake recognition for mastery.

    Then we need the information three days later—and it has vanished.

    Henry Roediger and Jeffrey Karpicke compared repeated study with retrieval practice: asking people to recall what they had learned.

    Repeated study performed better after a very short delay. Retrieval produced stronger retention later.

    That difference matters.

    If people need knowledge on the job, they should practice retrieving it from memory—not merely looking it up again.

    A quiz should not be the inspection station at the end of a course. Used well, it can help create the learning.

    Try this: Before reviewing something you have already taught, ask learners to recall it without looking. Then provide the correct answer and useful feedback.

    Effort helps when success is possible and feedback follows. Blind frustration does not.

    Read the retrieval-practice study →​

    Impact becomes real when it has a face

    Organizational psychologist Adam Grant and his colleagues studied university fundraising callers.

    Some callers met a scholarship student whose education had been funded by their work.

    The script stayed the same. So did the pay.

    But the callers could now picture someone who benefited when they picked up the phone. After meeting the student, they worked longer and raised more money.

    Now compare that with the language we often use to introduce workplace training:

    • Maintain compliance
    • Reduce organizational risk
    • Support strategic priorities
    • Complete this requirement by Friday

    Technically accurate. Emotionally dead.

    Grant studied direct contact with a beneficiary, not whether a good story can rescue a dull compliance course. But the design question travels well:

    Can employees see the person their actions affect?

    Who stays safe? Whose work becomes easier?

    Try this: Replace “This training is important because…” with a real example of someone affected by the behavior.

    Don’t just explain the requirement. Give the consequence a face.

    See how beneficiary contact changed performance →

    Information cannot fix a friction problem

    Imagine you know exactly what to do.

    Then you meet the form.

    The instructions are vague. The button you need is buried three menus deep. One approval takes four days, and nobody knows who owns it.

    Would another course help?

    Eric Bettinger and his colleagues studied families applying for college financial aid. One group received personalized information. Another received the same information, plus hands-on help completing and submitting the application.

    The help changed behavior. Information alone did much less.

    The workplace parallel is hard to miss. We prescribe training when the real obstacle is:

    • A confusing form
    • A buried button
    • Too many approvals
    • An unclear process
    • A lack of time
    • A system designed like an escape room

    If people already know what to do, more content is unlikely to save the day.

    Try this: Before building training, watch someone perform the task. Count the clicks, decisions, delays, handoffs, and chances to give up.

    You may not have a knowledge problem. You may have a friction problem.

    Sometimes the right intervention is not better instruction. It is fixing the system.

    Read the study on information vs. assistance →

    A badly framed norm can make behavior worse

    In a well-known field experiment, households learned how their energy use compared with their neighbors’.

    The heavy users cut back.

    But some households that had used less energy began using more. Once they saw the average, they drifted toward it even though that meant doing worse.

    A small sign of approval, a smiley face, stopped that boomerang effect.

    Now consider this workplace message:

    Thirty percent of employees still click suspicious links.

    The intended message is clear:

    This behavior needs to change.

    But employees may hear something else:

    Plenty of people do it. I wouldn’t be unusual.

    Never make the behavior you want to stop sound normal.
    When you use a social norm, make it truthful and pair it with clear approval of the desired behavior.

    If your data support it, you might say:

    Most employees report suspicious messages when something doesn’t look right. Thank you for helping protect your coworkers.

    What if the desired behavior is not common yet?

    Don’t invent a statistic. State the standard:

    Reporting a suspicious message is exactly the response we want. Every report helps protect the people around you.

    Try this: Review one message about poor behavior. Does it make the problem sound normal—or the solution?

    Explore the social-norms experiment →

    The Bottom Line

    These studies examine different problems, but they point in the same direction:

    Performance depends on more than content.
    People must notice what matters. They must recall it when needed, see a reason to act, have a workable path, and believe the behavior belongs here.

    Training can help.

    But it cannot repair a cluttered design, bring an empty mandate to life, remove a broken process, or overpower a culture that rewards the wrong behavior.

    Sometimes the smartest move is to improve the training.

    Sometimes it is to fix what stands in the way.

    What workplace learning problem should I investigate next?

  • Stop Calling It Mandatory Training

    Stop Calling It Mandatory Training

    An email lands in your inbox with the subject line Mandatory Annual Compliance Training—Complete by Friday.

    Before you open it, you already know the plot: a slow march through policy slides, a quiz designed to prove you were technically present, and perhaps a cheerful stock photo of coworkers pointing at a laptop.

    The course may surprise you. But the label may already be working against it.

    “Mandatory” tells people why the organization needs the training completed. It does not tell them why the training matters, what problem it will help solve, or what they will be able to do afterward.

    That is an administrative fact, not a value proposition. And yet we keep putting it in the headline.

    The Requirement Is Fixed. The Framing Is Not.

    People do not approach training as blank slates. They make fast judgments before the first screen loads. Will this be useful or irrelevant? Worth my attention or safe to skim?

    The word mandatory makes that judgment easier. It places the training in the same mental bucket as expense reports, password resets, and forms that need three signatures. It answers the organization’s question—Do you have to do this?—while ignoring the learner’s question: What will this help me do, avoid, or understand?

    A mandate may secure participation. It does not guarantee the motivation needed to learn or apply anything.

    That is why required learning still needs positioning. Marketers rarely lead with the least appealing part of an offer. A gym does not advertise, “Pay us to sweat before work.” A financial planner does not promise, “Several forms and an uncomfortable discussion about death.” They lead with the outcome: more energy, less stress, a safer future.

    Learning teams should do the same. You do not need to hide the requirement. You simply need to demote it. Lead with the reason to care, then include the deadline and completion rules as supporting details.

    Change the Frame

    Compare these announcements:

    Before: Complete Cybersecurity Training
    After: Protect Your Team From the Attacks Employees See Most Often

    Before: Annual Code of Conduct Training
    After: Know What to Do When the Right Choice Is Not Obvious

    Before: Required Manager Training
    After: Handle Difficult Performance Conversations Without Making Them Worse

    Before: Mandatory Data Privacy Course
    After: Avoid the Small Data Mistakes That Create Big Problems

    Nothing about the requirement changed. The stronger versions simply make a more useful promise.

    They give people something concrete to care about: a threat, a decision, a mistake, or an action they will be better prepared to take. The message shifts from “the company wants this” to “this could help me.”

    Of course, the promise must also be true. Do not advertise the “most common attacks” if the course never identifies them. Positioning is not decoration. It is a clear and honest connection between what people need and what the learning experience delivers.

    Four Better Ways In

    When you write your next training announcement, try entering through one of these four doors. They overlap, and that is fine. Choose the one that offers the strongest truthful reason to care.

    Relevance: Where will people use this?
    Spot the phishing tactics appearing in employee inboxes.

    Risk: What costly mistake can they avoid?
    Learn the warning signs that can prevent one rushed click from exposing customer data.

    Benefit: What becomes easier, safer, or less painful?
    Prepare for performance conversations that are clearer and fairer for everyone.

    Action: What will people be able to do?
    Submit expenses correctly the first time and avoid delays or rejected reports.

    Notice that none of these promises fun. They promise usefulness, which is a much better bargain.

    Positioning cannot turn a bad course into a good one. Clever copy attached to a bloated click-through course is not marketing; it is camouflage. But poor positioning can make genuinely useful training feel worthless before it begins.

    The Five-Second Test

    Before you publish your next announcement, remove the words mandatory, required, compliance, and complete. Then read what remains.

    Can someone tell why the training matters? Can they picture a situation where it might help? Is there a clear risk, benefit, or useful action?

    Most importantly, would the message have any pull if nobody were tracking completion?

    If not, you have written an instruction rather than a reason to pay attention. You may still get a green completion dashboard, but do not confuse that with learning.

    Try This This Week

    Find one announcement that begins with Required, Mandatory, or Complete. Rewrite its headline around relevance, risk, benefit, or action.

    Keep the requirement in the message. Just stop asking it to do the work of the value proposition.

    The course may have to be mandatory. The boredom does not.

    Go Deeper

    The Evidence on Mandatory Training Is Thinner Than You Think
    This government review examines whether required workplace training in areas such as cybersecurity, privacy, ethics, and harassment actually changes performance.

    Why “You Must” Can Make People Push Back
    Explore the research on psychological reactance and why controlling language can create resistance to a message.

    A Better Way to Communicate Requirements
    See how meaningful explanations and autonomy-supportive language can preserve motivation even when a request is not optional.

    Want to Train Like a Marketer?
    If this kind of thinking hits home, you’ll probably enjoy our book Think Like a Marketer, Train Like an L&D. It’s packed with practical, real-world ideas to help you make your learning clearer, sharper, and a whole lot more effective.

  • Attention Isn’t Given. It’s Spent.

    Attention Isn’t Given. It’s Spent.

    Learners aren’t losing the ability to pay attention.

    They’re making rational decisions about where to spend it.

    That’s not a capacity problem. It’s a design problem.

    Attention Is a Resource. Not a Reflex.

    Herbert Simon saw it coming in 1971. A wealth of information creates a poverty of attention. His point was precise: information doesn’t just compete for your learners’ attention. It consumes it. And attention, like money, gets allocated — not lost.

    Daniel Kahneman confirmed the mechanism two years later. His research showed that people direct their focus based on perceived importance – not randomly, not based on elapsed time, but on whether the next moment seems worth it.

    This isn’t attention deficit. It’s rational economics.

    The Numbers Everyone Gets Wrong

    Two numbers often drive the attention conversation in L&D right now. One is fabricated. One is real and widely misread.

    The real one: 47 seconds. 
    Gloria Mark at UC Irvine tracked screen-switching behavior for two decades. In 2004, people averaged 2.5 minutes on a task before switching. Today: 47 seconds, replicated across five independent studies. Philip Guo analyzed 6.9 million edX video sessions and found engagement collapses at 6 minutes — videos over 12 minutes got roughly 3 minutes of actual watch time.

    The fabricated one: 8 seconds, shorter than a goldfish.
    A 2015 Microsoft report cited a nonexistent source. It spread because it felt true. It wasn’t.

    These numbers get called attention limits. They aren’t. They’re exit decisions.

    A learner who switches screens at 47 seconds has made a judgment: not worth it. The one who stops a video at 3 minutes has made the same call. Attention followed value until the value ran out. Then it went somewhere that offered more.

    That behavior has a name. L&D built a model around it in the 1980s. Then mostly forgot it.

    L&D Had the Answer. Forgot It Anyway.

    ​John Keller’s ARCS model — Attention, Relevance, Confidence, Satisfaction — was built directly on Expectancy-Value Theory. The equation is blunt: motivation equals expectancy times value. Either factor hits zero, engagement hits zero. Not a drift. A cliff.

    Research on relevance interventions confirms it.

    When learners understand why content matters to them personally, engagement jumps across every measure.

    Keller wasn’t describing good design. He was describing the entry fee.

    The field had this for forty years and reached for a 10-minute timer instead anyway.

    Marketers didn’t have that luxury. Their feedback loops — click rates, bounce rates, conversion data — made getting attention wrong immediately visible and immediately painful. That forced one rule above all others: earn attention before you spend it. L&D never faced that reckoning. So myths filled the void.

    The Harder Question

    Before you build anything, ask this:
    ​
    why would a learner believe the next 45 seconds is worth their attention?

    Not whether the content matters. You know it does. Whether they’ll feel it in the opening moment, before you’ve done anything to earn their focus.

    That’s your actual design problem. Not length. Not format. Not which authoring tool.

    For eLearning: lead with stakes, keep it short (ideally under 6 minutes, if possible), and cut anything that doesn’t earn its place. Your learner is running a live cost-benefit call. Win it early or don’t win it.

    For ILT and VILT, the research is consistent: attention in live learning tracks the instructor, not the clock. Relevance, interactivity, and stakes. Those are the levers. A timer is not.

    Worth Your Attention

    Interest Matters: The Importance of Promoting Interest in Education

    This research highlights that fostering learner interest is not a superficial “nice-to-have” but a core engine for deep cognitive processing, persistence, and performance.

    ​Discover the 4 Interventions Driving Real Engagement →

    Designing Organizations for an Information-Rich World

    Worth reading, not because it’s practically actionable, but because it reframes the whole problem: attention isn’t something that’s been damaged by technology. It was always scarce. Technology just made the competition for it visible. Short, readable, historically significant.

    ​Learn Why Attention Was Always Scarce →

    Keller’s ARCS Model of Motivational Design

    This outlines a systematic blueprint for shifting the instructional focus from purely environmental rewards to a learner’s active desire to learn.

    ​Explore the ARCS Model to Design for Motivation →

  • The Dual Coding Problem: Why Your Learners Keep Tuning You Out

    The Dual Coding Problem: Why Your Learners Keep Tuning You Out

    How Dual Coding Theory Explains (and Fixes) Your Biggest Learning Design Problem

    Your learners look glazed over. They’re reading ahead while you talk. Or they’ve checked out entirely.

    You know what they’re doing? Choosing.

    Read the slide or listen to you. Not both. They can’t. And it’s not their fault—it’s your design.

    Your Brain Processes Information Through Two Separate Systems

    In 1971, psychologist Allan Paivio proposed dual coding theory. The core insight: your brain has two distinct processing channels. One handles visual and spatial information—images, diagrams, animations. The other handles verbal information—language and words.

    The critical detail most people miss? It’s not about eyes versus ears.

    Text on a slide gets processed through the verbal channel, even though you see it with your eyes. Your brain recodes written words into linguistic form. So when you put bullet points on screen and read them aloud, you’re forcing the verbal channel to process identical information from two sources simultaneously.

    Think of two conveyor belts feeding into working memory. One carries visual representations. The other carries language. When each belt carries complementary information, learning compounds. You get multiple retrieval pathways, stronger encoding, better recall.

    But pack identical information on both belts? The system jams.

    Richard Mayer’s multimedia learning research confirmed this. People learn better from words and pictures together than from words alone—but only when those words and pictures complement rather than duplicate each other.

    The Redundancy Trap

    Most training designers make the same mistake: text on slide, narration reading that text.

    You’ve seen it. Probably done it.

    The problem isn’t repetition for emphasis. The problem is cognitive cost. Your verbal processing system has limited capacity. Force it to handle the same information from two sources—reading and listening—and you max out that capacity on redundant processing. No room left for understanding.

    This is the redundancy effect. Your learners have to choose: read or listen. They read faster than you speak, finish the slide, then wait. You’re talking to people who’ve mentally moved on.

    For novice learners especially, this matters. Experts can handle more complexity. But people learning something new need clean separation between visual and verbal channels.

    Dual Coding Done Right

    This BrightCarbon video demonstrates the difference perfectly.

    First, they show the problem: slides packed with text while narration repeats those exact words. Then they flip it. Visual sequences—diagrams, animations, process flows—paired with narration that explains rather than repeats.

    Watch how it works. The visuals aren’t decorative. They’re not self-explanatory either. You need both channels. What you see plus what you hear creates the complete understanding.

    That’s dual coding working as designed.

    Redesigning Your Materials

    Stop treating slides as a script. They’re a visual partner to your narration.

    Instead of bullet points repeating your words:
    Show diagrams illustrating relationships. Use process flowcharts while you walk through steps verbally. Display before/after comparisons while you explain what changed.

    Instead of definitions on screen:
    Show examples or illustrations while you speak the definition. Visual representation through the eyes, explanation through the ears.

    Instead of spelling out your message:
    Let visuals do visual work and narration do verbal work. Explaining why customer complaints spike in Q4? Show the graph of complaint volume over time. Don’t write “complaints spike in Q4” on the slide. Your words explain why. The visual shows what.

    The principle: related but distinct information through each channel. They support each other, don’t duplicate.

    Try This

    Find one slide where you’re reading on-screen text verbatim.

    Remove that text. Replace it with a visual—diagram, icon, image, data visualization. Practice delivering it with the visual carrying its message while your narration carries yours.

    You’ll know it works when the slide alone doesn’t tell the full story and your narration alone doesn’t paint the full picture. Together, they create understanding neither could achieve alone.

    That’s how your learners’ brains want to process information. Stop fighting it.

    References:

    • Paivio, A. (1971). Imagery and Verbal Processes. New York: Holt, Rinehart & Winston.
    • Paivio, A. (1986). Mental Representations: A Dual Coding Approach. Oxford University Press.
    • Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149-170.
    • Mayer, R. E. (2009). Multimedia Learning (2nd ed.). Cambridge University Press.
  • The Best L&D Books Are Not L&D Books

    The Best L&D Books Are Not L&D Books

    (A reading list that makes you useful, fast)

    When someone new to learning design asks a reasonable question:

    “What L&D books should I read?”

    They usually get a long list of L&D books.
    Models. Methods. Frameworks. More models.

    Helpful… up to a point.

    Because the real job of learning design isn’t “build a course.”

    It’s help people do something differently—inside messy systems, with limited time, and zero patience for fluff.

    That’s why the books that make you better at L&D often aren’t labeled “L&D.”

    This list is built for one thing:

    To help you stop building training that changes nothing—and start designing work that gets used.

    Learning sits downstream

    Learning doesn’t live on an island. It lives inside:

    • incentives
    • tools
    • culture
    • managers
    • time pressure
    • bad processes
    • and human laziness (the normal kind)

    So if you only read learning books, you’ll get good at packaging information.

    But behavior doesn’t move because information is well packaged.

    Behavior moves because the situation changes.

    Tier 1: Books that make you useful early

    These are the books I’d hand to a new learning designer because they prevent the classic rookie mistake:

    You build the thing you were asked for… instead of the thing that would help.

    Performance & diagnosis

    • Performance Consulting — Dana & Robinson
      When you’re handed “we need training,” this teaches you how to ask, “Do we?”
    • Good Strategy / Bad Strategy — Richard Rumelt
      When leaders give you goals and call it strategy, this shows you the difference.

    Behavior & action

    • Behavior Change — Susan Michie et al.
      When people “know better” and still don’t do it, this tells you why.
    • Designing for Behavior Change — Stephen Wendel
      When you need action, not awareness, this gives you levers you can actually pull.

    Learning (when learning really is the lever)

    • Design for How People Learn — Julie Dirksen
      When learning is part of the solution, this keeps it focused, practical, and humane.

    Communication & clarity

    • The Pyramid Principle — Barbara Minto
      When your content feels bloated, this teaches you how to think clearly first.
    • Made to Stick — Chip & Dan Heath
      When your message doesn’t land, this shows you what makes ideas memorable.

    Discovery & listening

    • The Mom Test — Rob Fitzpatrick
      When your “needs analysis” is basically polite guessing, this fixes it.

    Tier 1 outcome:
    You stop being an order-taker. You become a problem-solver.

    Bridge Books: translating insight into practice

    Some books aren’t foundations, and they aren’t slow-burn theory either.

    They’re translation layers. They help you change your posture before you change your deliverables.

    They move you from:

    • content-first → audience-first
    • delivery → demand
    • push learning → create pull

    A few that do this well:

    • Think Like a Marketer, Train Like an L&D Pro
      When you’re tired of “mandatory training” energy, this helps you earn attention and design for real humans.
      https://www.amazon.com/Think-Like-Marketer-Train-Pro/dp/1960231197
    • The Power of Moments — Chip & Dan Heath
      When learning feels forgettable, this shows how to design moments people remember—and use.

    Tier 2: Books that make you less naïve

    Tier 2 is what you read after you ship good work… and still watch behavior resist.

    These books teach you the limits.
    And that’s a gift.

    • Nudge — Thaler & Sunstein
      When environment beats explanation (which is often), this shows how to design the path of least resistance.
    • Noise — Kahneman, Sibony, Sunstein
      When inconsistency breaks trust and performance, this helps you see the hidden problem.
    • How Behavior Spreads — Damon Centola
      When adoption stalls, this shows why broadcasting information isn’t enough.
    • The Art of Gathering — Priya Parker
      When workshops and cohorts feel flat, this helps you design the social container.
    • Seeing Like a State — James C. Scott
      When an organization wants a grand rollout, this teaches humility before you scale.

    Tier 2 outcome:
    You stop over-promising what learning can do on its own.

    A note on the L&D classics

    None of this is new.

    Gilbert made environment and supports central to performance.
    Mager & Pipe warned us not to reach for training first.
    Clark & Mayer formalized how cognitive limits shape design.

    So why recommend newer, non-L&D books?

    Because most practitioners don’t struggle with theory.
    They struggle with translation, influence, and getting results in modern workplaces.

    These books often travel further because the language lands with more people.

    This isn’t a replacement of the classics.
    It’s an extension.

    This isn’t anti-L&D

    L&D books matter.

    But they work best as integrators—books that help you apply ideas from behavior, systems, and communication to learning.

    If you treat L&D as the whole world, you end up building content in a vacuum.

    If you treat learning as one lever inside a system, you get much better results.

    How to use this list (without turning it into homework)

    1. Start with Tier 1. Pick two and read with a project in mind.
    2. Add one bridge book. Use it to reshape how you pitch and package learning.
    3. Earn Tier 2. Read it after you’ve shipped work and want sharper judgment.

    Don’t binge. Sequence.

    Because the goal isn’t to be well-read.
    It’s to be effective.

  • The Signaling Principle

    The Signaling Principle

    A practical guide to designing learning people actually notice

    Learners don’t ignore content because they’re lazy.
    They ignore it because your design makes them guess what matters.

    That’s the signaling principle (cueing).

    Signaling means you guide attention—on purpose.
    You make the important parts easy to spot and the unimportant parts easy to ignore.

    Why it works is simple: attention is limited. Working memory is limited.
    When learners waste effort searching for relevance, they have less capacity left to understand, connect, and remember.

    If they have to hunt for meaning, you’ve already made learning harder.

    The one question every screen must answer

    Every slide, screen, or interaction triggers a silent question:

    “What should I focus on right now?”

    Good signaling answers that question instantly.
    Poor signaling makes learners answer it themselves—while also trying to learn.

    That’s a bad trade.

    What counts as a signal

    Signals come in three main forms. None need to be fancy.
    Use fewer than you want to. Make them earn their keep.

    1) Visual signals

    Use visuals to point, not decorate.

    Effective examples:

    • One arrow pointing to one element
    • A highlight on the exact part being discussed
    • A “spotlight” effect: dim everything except the target
    • Step-by-step reveals (one idea at a time)

    Ineffective examples:

    • Highlighting everything
    • Three arrows competing on one screen
    • Motion that means nothing

    Rule: One screen, one focal point.

    2) Textual signals

    Text is a quiet power tool.

    Effective uses:

    • Headings that state the takeaway (not the topic)
    • Bold for key terms or decisions (sparingly)
    • Bullets to group ideas, numbers to show sequence
    • Preview lines: “There are three things to watch for…”

    Avoid:

    • Paragraph walls
    • Bold-as-decoration
    • Vague headings like Overview

    If everything is emphasized, nothing is.

    3) Verbal signals

    Your voice is part of the design.

    Signals include:

    • A pause before the key point
    • A slower pace on the “why”
    • Phrases like “Here’s the part people miss…”
    • One clean repetition

    Monotone narration forces learners to guess importance.
    If something matters, it should sound like it matters.

    Where signaling matters most

    Signaling helps almost everywhere, but it matters most when:

    • The material is complex or dense
    • The medium is fast or transient (video, animation, demos)
    • Learners are novices
    • Visuals have many parts or relationships

    Novice learners don’t yet know what to pay attention to.
    That’s not their flaw. That’s the assignment.

    When signaling helps less (and can backfire)

    Be cautious when:

    • Learners already know the domain well
    • The content is simple or familiar
    • Signals pile up and create clutter

    Here are red flags:

    • If your slide has five highlights, it has zero.
    • If the cue stays after the moment passed, it becomes noise.
    • If learners ask “which one?” your cues are competing.

    More cues doesn’t mean more learning.
    It usually means more distraction.

    Quick diagnostics before you ship

    Use these tests before finalizing any learning artifact.

    The 5-second test

    Show the screen for five seconds.
    Can a learner say what matters? If not, redesign.

    The pointer test

    When the narration references something, could a learner point to it instantly?
    If not, add or improve the cue.

    The squint test

    Squint at the screen.
    Does the focal element still pop? If not, simplify and re-signal.

    The timing test

    Does the cue appear exactly when the narration references it?
    If it shows up early or late, learners still have to search.

    🔍 Sidebar: What signals really do

    Most people think signaling just means “point here.”
    It does more than that.

    Signals perform three jobs:

    1) Guide attention
    They reduce search. Search is wasted effort.

    2) Show structure
    They tell learners how ideas are organized: steps, parts, priorities.
    Headings, previews, and numbering aren’t decoration. They’re a map.

    3) Help integration
    They make relationships obvious.
    Words match visuals. Narration matches action. Cause links to effect.

    Good signaling doesn’t just say look here.
    It also says this belongs with that.

    Common design mistakes

    • Highlighting entire screens
    • Using color or motion without meaning
    • Assuming learners will “figure it out”
    • Confusing engagement with clarity

    Learning often fails quietly.
    Confusion looks like compliance.

    The mindset shift that matters

    Stop thinking like a content expert.
    Start thinking like a guide with a flashlight.

    Your job isn’t to show everything.
    It’s to guide attention, moment by moment, so learners can use their limited cognitive resources for understanding and integration.

    Bottom line (and a challenge)

    Design is a series of attention decisions.
    Signaling makes those decisions visible.

    Now do this: take one messy slide you’ve made before.
    Redesign it using two signals max.

    If it gets clearer, you’re doing it right.

  • Most Instructional Designers Put This at the Start. Here’s Why YOU Shouldn’t!

    Most Instructional Designers Put This at the Start. Here’s Why YOU Shouldn’t!

    You spent three weeks on that compliance module. Got the objectives perfectly clear. Slide two, exactly where Gagne said they should be. Your instructional design was textbook.

    Then you watched what happened: Learners skimmed the objectives and jumped ahead. Exit interviews revealed the pattern: “I knew what I was supposed to learn, so I didn’t pay attention to how.” Poor retention. Minimal application on the job.

    Meanwhile, your colleague’s module—the one that starts with no objectives, just a failure scenario—had learners leaning forward, engaged, completing it in one sitting. Quiz performance was stronger. Six months later, managers reported better application.

    It breaks every rule you were taught.

    It works anyway.

    Why?

    The answer isn’t about engagement tactics or making training “fun.” It’s about something most instructional designers fundamentally misunderstand about how curiosity affects learning.

    We think curiosity is the appetizer. It’s actually the main course.

    The Conventional Wisdom Makes Perfect Sense

    Robert Gagne’s Nine Events of Instruction have anchored systematic design for 40 years. For good reason. Event #2—”State the Objectives”—makes logical, ethical sense:

    Learners deserve to know what’s expected. Objectives reduce anxiety. Clear expectations help learners organize information. Transparency respects their time.

    Every instructional design certification teaches this. Every ADDIE process includes it. Lead with learning objectives? You’re being a responsible designer.

    You’re also sabotaging the learning.

    Not for the reason you think.

    What We Got Wrong About Curiosity

    Most of us carry the same assumption: Curiosity is an engagement tactic. Gets attention. Makes training feel less boring. It’s the motivational wrapper around the real content.

    Curiosity = appetizer.
    Learning = main course.

    That’s backwards.

    What if curiosity isn’t decoration for learning—what if curiosity is how learning happens?

    What Loewenstein Actually Discovered

    In 1994, Carnegie Mellon psychologist George Loewenstein published research that should have changed everything.

    Most people know the sound bite: curiosity is a “cognitive itch.”

    They miss why that matters.

    Loewenstein showed that curiosity isn’t a personality trait. Not a motivational boost. It’s a specific cognitive state triggered by a knowledge gap—the uncomfortable distance between what you know and what you realize you must know.

    The itch only happens when you perceive something’s missing.

    The critical insight: Curiosity requires just enough knowledge to recognize that the gap exists.

    Too little knowledge? Confusion.
    Too much knowledge? No gap.
    Curiosity lives in between.

    Most training never enters that zone.

    Lead with learning objectives? You fill the hole before learners know there’s a hole to fill. You answer questions they haven’t asked yet. You solve problems they don’t feel.

    The gap closes before it opens.

    The Thing We’ve Been Getting Wrong

    The conventional wisdom: Curiosity captures attention, then objectives structure the learning.

    Curiosity = motivation.
    Objectives = learning architecture.

    The research tells a different story.

    Twenty years after Loewenstein’s psychological framework, neuroscientists at UC Davis finally measured what curiosity does to memory. Matthias Gruber, Bernard Gelman, and Charan Ranganath put people in a brain scanner and had them learn trivia facts—some they were intensely curious about, others they didn’t care about at all.

    The results were clear.

    When people were curious, their brains showed two things: First, the same reward circuits that activate for food or money lit up for information. Curiosity isn’t just mental—it’s rewarding at a neurological level.

    Second, and more importantly, the brain regions involved in memory formation showed dramatically enhanced activity.

    Curious brains form stronger memories.

    But here’s the finding that changes everything:

    The memory enhancement wasn’t limited to the information they were curious about. When learners were in a curious state, they formed stronger memories for related information too—including details they weren’t specifically trying to learn.

    Your compliance module just changed.

    When you open with a knowledge gap about the $2M invoice error—before you explain the approval process—you’re potentially creating the same memory-enhancing state Gruber observed in the lab. While the research tested trivia, the underlying mechanism appears domain-general: the brain doesn’t distinguish between “curiosity about Beatles trivia” and “curiosity about approval checkpoints.” It responds to the knowledge gap itself.

    The evidence suggests that curiosity-driven learning creates conditions where information sticks more effectively—not just the answer to the question, but the surrounding procedures, policies, and protocols you teach while learners are actively working to close the gap.

    Loewenstein predicted this in 1994. Psychology.
    Neuroscience confirmed it in 2014. Biology.
    Most instructional designers still haven’t caught up.

    Watch what happens in real time:

    The learner sees three URLs. Almost identical. One cost $2M.

    I know URLs. I know phishing. But I don’t know WHICH character matters.

    Prior knowledge: activated.
    Gap: perceived.

    They lean forward. Character by character. Domain by domain. What am I missing?

    Questions generated.
    Attention locked.

    When they spot it—that tiny subdomain difference—resolution floods in.

    There.

    That’s when memory formation happens most effectively. Not when they read the objectives slide listing “three types of phishing patterns to identify.” When they discover the pattern themselves while actively hunting for it.

    That’s not engagement theater.

    That’s learning.

    The curiosity gap creates the conditions for deep processing—the productive mental work of building understanding that lasts.

    Lead with objectives? Learners passively receive information.
    Lead with gaps? They actively construct meaning.

    This is why the “wrong” design wins.

    Not because it’s more engaging. Because the design creates the cognitive conditions where memory formation is most effective.

    The Gagne Question: Is This a Contradiction?

    If you’re a Gagne loyalist—and if you’ve been trained in systematic instructional design—this sounds like throwing out proven frameworks for trendy engagement tactics.

    It’s not.

    Loewenstein didn’t disprove Gagne. He updated him with 30 years of neuroscience Gagne didn’t have in 1985.

    Gagne was right that learners need objectives. Right that systematic design matters. Right that structure reduces cognitive load.

    He just put Event #2 in the wrong spot.

    Gagne assumed learners needed the destination before they’d take the first step. Loewenstein proved they need to want the destination first.

    The objectives aren’t wrong.
    The timing is.

    The synthesis instructional design has been missing:

    Keep Gagne’s structure. Create Loewenstein’s gap. Flip the sequence.

    Traditional approach:

    1. State objectives (Event #2)
    2. Present content
    3. Practice
    4. Assess

    Updated approach:

    1. Activate schema (give just enough context to perceive the gap)
    2. Open the gap (present the failure, anomaly, or question)
    3. Sustain the gap (reveal information gradually as they work to close it)
    4. Close the gap (NOW state the objectives—as the earned answer)
    5. Transfer the gap (create a new scenario where they apply the pattern)

    The objectives still exist. Still provide structure and clarity.

    They’re just repositioned as the answer to a question the learner is already asking.

    The Implementation Problem: Why Most Designers Get This Wrong

    Some designers try this once and fail.

    What usually goes wrong? They confuse mystery with confusion—threw learners into a vague scenario where nobody knew what domain they were in, what to pay attention to, or whether they could even solve the problem, and working memory collapsed under the weight of too many unknowns competing for too few cognitive resources.

    The training didn’t just fail to engage. It actively overwhelmed.

    Then they concluded: “Curiosity gaps don’t work. Gagne was right all along.”

    But that’s like saying vegetables don’t work because you served raw, unwashed potatoes.

    The implementation was wrong. Not the principle.

    Loewenstein’s research was clear. Curiosity only works when the gap is:

    • Perceivable (learners can tell what they don’t know)
    • Specific (the missing information is bounded, not infinite)
    • Closeable (learners believe they can find the answer)

    You can’t just throw learners into ambiguity and hope curiosity emerges. You need just enough frame to direct attention without spoiling the answer.

    The frame prevents confusion. The gap triggers curiosity.

    Compare these:

    Bad gap (creates confusion):

    “Here’s a $2M invoice error. Figure out what went wrong.”

    What domain? Accounting? Data entry? Fraud?
    What am I looking for? Everything? One thing?
    Can I even solve this with what I know?

    Anxiety spikes. Working memory overloads.

    Good gap (creates curiosity):

    “Three people approved this invoice before it went out. The amount was wrong from the first click. Your job: find which approval checkpoint failed—and why everyone missed it.”

    Domain: clear (approval process)
    Task: specific (identify the failed checkpoint)
    Scope: bounded (it’s about process, not everything)
    Gap: present (I don’t know which checkpoint or why it failed)

    The second version gives learners orientation without answers. They know what to pay attention to. They don’t know what they’re going to discover.

    That’s where curiosity lives.

    Why Your SME Will Hate This (And How to Win Them Over)

    The SME conversation usually goes like this:

    “Why waste time on mystery? Just tell them what they need to know.”

    Fair objection.

    Here’s the reframe:

    When you lead with answers, you’re adding to the pile of 2,000 things they’re supposed to remember. Most of it disappears within a week—you’ve seen the post-training assessment data.

    When you lead with the problem, something different happens. The information you give them isn’t competing with 2,000 other facts. It’s the answer to a question they’re actively trying to solve. Research shows memory formation is more effective when learners encounter information in a curious state—when they’re working to close a knowledge gap.

    The SME’s content doesn’t change. The sequence does.

    You’re still giving them all five vulnerability types. Still covering every approval checkpoint. Still delivering the same expertise.

    We’re just repositioning it from “information to memorize” to “answer to a problem they’re trying to solve.”

    And the sequence determines whether their expertise becomes information that fades or knowledge that sticks.

    The Working Memory Question You Can’t Ignore

    Picture your learner staring at that objectives slide.

    Their brain is working. But on what?

    Reading the bullet points. Trying to figure out why this matters. Wondering when the real content starts. Checking Slack.

    That’s mental effort. But it’s not productive mental effort. It’s not building understanding. It’s just… busy work for the brain.

    Cognitive load theory has a name for this: extraneous load. Mental effort wasted on poor design instead of directed toward learning.

    Now picture the same learner encountering the $2M invoice error scenario.

    Their brain is still working. But now it’s working on: What checkpoint failed? Why did everyone miss it? What pattern am I not seeing?

    That’s also mental effort. But this time, the effort IS the learning.

    Current cognitive load theory distinguishes between two types of load:

    Intrinsic load (inherent complexity of the material)—UNAVOIDABLE
    Extraneous load (wasted mental effort from poor design)—ELIMINATE THIS

    The goal isn’t to reduce all cognitive load. It’s to eliminate wasteful load so learners can direct their limited working memory toward productive cognitive processing.

    Create a boring objectives slide? Extraneous load.

    Create a confusing, unbounded mystery? Also extraneous load.

    Create a bounded, directed knowledge gap? You minimize extraneous load while creating optimal conditions for learning.

    The critical factor: Working memory capacity is limited. Research suggests we can hold about four chunks of information at once. During a curiosity gap, learners are holding:

    1. The question or problem
    2. Their activated prior knowledge
    3. Attention to relevant cues
    4. Meta-cognitive monitoring

    Manageable—if you’ve provided the frame.

    Without the frame? Add uncertainty about domain, confusion about task, anxiety about solvability. Working memory exceeds capacity. Extraneous load spikes. Confusion replaces curiosity.

    The design principle:

    Bounded + directed + solvable = optimal conditions for learning.

    From Theory to Monday Morning: Redesigning Your Next Module

    You probably have a module you’re working on right now.

    Compliance training. Technical onboarding. Soft skills development.

    Right now, it probably starts with objectives. A dutiful list of what learners will know and be able to do.

    Your redesign framework:

    Phase 1: Don’t Delete the Objectives—Delay Them

    Your objectives aren’t wrong. Just premature. Move them from slide 2 to the moment after learners have encountered the problem the objectives solve.

    Phase 2: Replace Event #2 with “Frame the Gap”

    Open with three elements:

    1. Domain specification (what realm are we in?)
    2. Specific question (what don’t I know?)
    3. Bounded scope (how big is this problem?)

    Traditional objectives: “By the end of this module, you will identify three types of phishing URLs and explain how each exploits user behavior.”

    Framed gap: “Three employees clicked three different links last month. One lost their password. One leaked quarterly forecasts. One triggered a $2M breach. The URLs looked almost identical. Your job: find what the security team missed.”

    Same learning outcome. Different entry point.

    One spoils the answer. One creates the itch to know.

    Phase 3: Reveal Information Gradually

    Don’t dump the content. Unfold it.

    Let learners predict before you explain. Show examples before you give the pattern. Make the resolution earned rather than delivered.

    Phase 4: State Objectives as Resolution

    NOW—after they’ve wrestled with the problem—tell them what they just learned:

    “Those are the three phishing patterns most employees miss: subdomain spoofing, homograph attacks, and URL parameter injection. You just learned to identify all three in context. Now let’s practice on new examples.”

    The objectives become the answer, not the assignment.

    The One Thing to Audit Right Now

    You don’t need to redesign your entire curriculum today.

    Start with one diagnostic question:

    In your next module, are you answering questions learners haven’t asked yet?

    If yes, you’ve found your gap opportunity.

    The fix isn’t complicated. It’s sequential. You’re not removing information. You’re reordering when it appears.

    I’ve built a tool that walks you through exactly how to audit your modules for curiosity gaps—and how to redesign them without starting from scratch.

    Try the Curiosity Audit:

    curiosity-audit.lovable.app

    It’ll show you where you’re spoiling answers, where you’re creating confusion instead of curiosity, and where you’re missing opportunities to turn passive content into active problem-solving.

    The Bigger Picture

    This isn’t about making training “fun.”

    Not about engagement metrics or completion rates.

    It’s about designing for how memory actually works.

    The research is clear: Curiosity creates conditions where memory formation is enhanced. When you create the gap first, you’re not just capturing attention—you’re creating the conditions where the information you worked so hard to design actually sticks.

    The question isn’t whether to use systematic design or curiosity-first design.

    It’s how to synthesize both.

    Structure that creates productive cognitive processing instead of passive information consumption.

    If you aren’t digging holes before you fill them, you aren’t teaching. You’re just reciting.

    Your next module starts Monday.

    Slide two is waiting.

    What will you do with it?

    References

    Gagné, R. M., Briggs, L. J., & Wager, W. W. (1992). Principles of instructional design (4th ed.). Fort Worth, TX: Harcourt Brace Jovanovich College Publishers.

    Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486-496. https://doi.org/10.1016/j.neuron.2014.08.060

    Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75-98. https://doi.org/10.1037/0033-2909.116.1.75

    Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261-292. https://doi.org/10.1007/s10648-019-09465-5


    Want more like this? The thinking in this post comes from Think Like a Marketer, Train Like an L&D Pro—a deeper dive into applying behavioral science and marketing psychology to learning design.

    #TrainLikeAMarkter #InstructionalDesign #LearningScience #CuriosityGap #CognitivePsychology

  • The Testing Effect: Why Retrieval Practice is Your Most Powerful Learning Tool

    The Testing Effect: Why Retrieval Practice is Your Most Powerful Learning Tool

    The Lie We Tell Ourselves

    Sarah’s quarterly review went well. Her compliance training program had a 94% completion rate. Everyone passed the quiz. Leadership was pleased.

    Three months later, a manager made a comment that clearly crossed the line. Nobody reported it.

    When HR investigated, five employees gave the same answer: “I didn’t know that counted as harassment.” They’d all completed Sarah’s training. Every single one passed the final quiz. They remembered nothing.

    Sarah knew the problem wasn’t her slides. The content was clear. The examples were relevant. The quiz tested the right material.

    The problem was deeper: her learners never actually learned.

    They recognized information during training. They forgot it afterward. And Sarah had no way to tell the difference until it was too late.

    This is the central paradox of workplace training. We measure completion. We get forgetting. We design for immediate performance. We need lasting retention. We think we’re building knowledge. We’re building illusions.

    The solution isn’t better videos or clearer slides. The solution is counterintuitive. Less presenting. More testing.

    How This Fits: Building on What You Know

    If you’ve read earlier articles in this series, you know effective learning often requires productive struggle. We explored how worked examples reduce cognitive load for novices, making complex skills more learnable. We examined how desirable difficulties enhance retention when applied correctly.

    Testing—specifically, retrieval practice—belongs in the desirable difficulties family. It’s harder than reviewing notes. Less comfortable than rereading. And dramatically more effective than both.

    Here’s the key insight: retrieval practice and worked examples aren’t contradictory. They’re complementary partners in a learning sequence.

    Use worked examples when learners face genuinely novel material. Reduce extraneous load. Build initial schemas. Then—and this is critical—use retrieval practice to cement those schemas into long-term memory.

    Think of it as two-stage construction. First, reduce unnecessary difficulty during initial learning. Then introduce productive difficulty during practice. The worked example teaches the concept. The retrieval practice makes it stick.

    Timing matters. Sequence matters. Understanding when to scaffold and when to challenge separates effective training from theater.

    Now let’s examine why retrieval works so powerfully—and why most organizations still aren’t using it.

    The Evidence That Should Have Changed Everything

    In 2006, Henry Roediger and Jeffrey Karpicke ran an experiment that should have revolutionized workplace training.

    It didn’t. At least not yet.

    They had college students read passages about sea otters and the sun. After reading, students were randomly assigned to one of two groups:

    Group 1: Study the passage four times  

    Group 2: Study once, then take three practice tests (no feedback)

    Five minutes later, Group 1 crushed it. They recalled more information. This makes intuitive sense—more exposure, more learning, right?

    One week later, both groups returned for a surprise test.

    The results flipped.

    Students who took practice tests remembered 50% more than students who studied repeatedly (Roediger & Karpicke, 2006). Testing once produced better retention than studying four times.

    Let that sink in. The students who studied less remembered more.

    This wasn’t a fluke. The testing effect—also called retrieval practice—has been replicated hundreds of times across different ages, materials, and contexts. The meta-analyses are overwhelming:

    Rowland (2014) analyzed dozens of studies comparing testing to restudying. Medium-to-large effect size (g = 0.50). Adesope et al. (2017) examined 217 studies spanning lab and classroom settings. Testing won consistently. Agarwal et al. (2021) conducted a systematic review focusing specifically on applied educational contexts—real classrooms, not just labs. The effect held.

    The evidence is bulletproof. When you force learners to retrieve information from memory—to actively recall without looking—you create stronger, more durable learning than any passive review strategy.

    But why? What happens during retrieval that makes it so powerful?

    What Retrieval Actually Does to Your Brain

    Most people think of tests as measurement tools. Ways to assess what learners know. But testing is a learning event. The act of retrieval physically changes the brain.

    Let’s experience this directly.

    Try this right now: Without looking back, write down the three groups from the Roediger and Karpicke study I just described. What did each group do?

    Go ahead. Try it.

    Notice what just happened in your mind. You had to search for the information. Maybe you pictured the paragraph where I described it. Maybe you remembered “Group 1 studied more” but couldn’t recall the exact number. Maybe you struggled.

    That struggle? That’s retrieval. And it’s completely different from what happens when you reread.

    Now scroll up and check your answer.

    You just experienced three phenomena that make retrieval practice work:

    The Effort Changes Everything

    When you reread material, your brain takes a shortcut. You see information and think, “Yes, I know this.” You’re using recognition pathways. Recognition is passive. Easy. Comfortable.

    And almost useless for long-term retention.

    Retrieval is different. You must reconstruct the memory from scratch. No cues. No hints. Just your brain searching through networks of associations trying to locate and rebuild the information.

    This reconstruction requires effort. Real cognitive work. And that effort is what strengthens the neural pathway. Each successful retrieval makes the next one easier. The memory becomes more accessible (Bjork, 1994).

    Think of it like a trail through woods. Walking the same cleared path repeatedly keeps it maintained. But bushwhacking through overgrowth to find the trailhead? That’s retrieval. It’s harder. You might get lost. But when you succeed, you’ve created a clearer, more distinct path.

    The difficulty is the mechanism. No struggle, no strengthening.

    You Activate More Than You Retrieve

    Here’s where it gets fascinating.

    When you struggle to retrieve information, you don’t just strengthen the direct memory. You activate related information through spreading activation in semantic memory (Carpenter, 2009, 2011).

    Imagine you’re trying to remember your company’s incident reporting procedure. As you struggle to recall “Document the incident immediately,” your brain activates related concepts: “Notify supervisor,” “Preserve evidence,” “Photos if possible,” “Follow up within 24 hours.”

    These related concepts become linked to your target memory. They create multiple routes to the same destination.

    Carpenter (2011) demonstrated this with word pairs. When learners retrieved weakly associated pairs like “basket” and “bread,” they spontaneously activated semantic mediators—related concepts like “grocery” or “bakery.” These mediators later served as additional retrieval cues. Restudying didn’t produce this effect.

    For workplace application, this is critical. Real problems don’t arrive with helpful labels. Your employees won’t see a notification saying “Time to use your de-escalation training!” They need to access that knowledge spontaneously when a customer starts yelling.

    Multiple access routes make spontaneous recall possible.

    You Learn What You Actually Know

    When Sarah’s employees restudied her harassment prevention slides, everything looked familiar. They thought they knew it. Familiarity felt like learning.

    It wasn’t.

    Retrieval reveals the truth. It exposes the gap between what you think you know and what you can actually recall. This is a metacognitive benefit—your ability to judge your own knowledge (Bjork, 1994).

    Karpicke et al. (2009) studied how college students prepare for exams. The results were striking:

    – 84% relied primarily on rereading

    – Only 1% used self-testing as their main strategy

    When asked why, students said rereading “felt more productive.” But their predictions about their own learning were systematically wrong. Students thought repeated studying would help. Testing actually helped more.

    This is the illusion that sabotages most training. Rereading feels smooth and confident. Testing feels choppy and uncertain. But feelings deceive. The uncertainty during retrieval is a signal of learning, not failure.

    Sarah’s employees felt confident after reviewing her slides. They were wrong. The quiz after training measured recognition—can you identify the right answer when you see it?—not retrieval. Recognition and retrieval are different animals. One works on the job. One doesn’t.

    The Resistance You’ll Face (And Why It Matters)

    Here’s the problem Sarah discovered when she redesigned her training: retrieval practice feels wrong to learners.

    She sent her first spaced quiz one week after training. Simple questions. No tricks. Just “From memory, list three behaviors that constitute reportable harassment under company policy.”

    The responses she got weren’t quiz answers. They were complaints.

    “I don’t remember this. Can you send the slides again?”  

    “This feels like a test. I thought we already passed the training.”  

    “Why are you testing us? Don’t you trust us?”

    Sarah had anticipated resistance. She hadn’t anticipated how visceral it would be.

    But the research predicted this reaction perfectly. When Karpicke et al. (2009) surveyed students about study strategies, they found learners overwhelmingly prefer rereading because it feels productive. It’s smooth. Information flows easily. Learners mistake this fluency for learning.

    Testing feels terrible. You get stuck. You blank on answers you “knew” five minutes ago. You feel uncertain. Learners interpret this difficulty as failure.

    But that difficulty is the entire point.

    Bjork (1994) calls this “desirable difficulty”—obstacles that impair performance during practice but enhance long-term retention. The struggle to retrieve is what makes retrieval practice work.

    This creates a massive challenge for L&D professionals. Your learners will actively resist the strategy that helps them most. They’ll gravitate toward strategies that feel productive but accomplish little.

    Sarah learned to reframe retrieval practice. Not as testing. As a workout for memory. Nobody expects exercise to feel effortless. The challenge is the mechanism of growth.

    She told her team: “If this feels hard, that’s good. If you’re struggling to remember, that’s the point. Your brain is working. That work is what makes knowledge stick.”

    Some grumbled. But after two months, something shifted. Employees started noticing they could answer questions without looking things up. The knowledge was just… there.

    One employee told Sarah: “I hated those pop quizzes at first. But now, when a situation comes up, I actually remember the policy. I don’t have to search for it.”

    That’s when Sarah knew it was working.

    How to Actually Do This: Two Detailed Applications

    Enough theory. Let’s walk through exactly how Sarah implemented retrieval practice—first in her compliance training, then in her technical skills program.

    Application 1: Compliance Training That Actually Sticks

    What Sarah Changed

    Her old approach was typical: 45-minute module, information-dense slides, final quiz with answers visible on the screen above the questions. Pass rate: 95%. Retention three months later: maybe 30%.

    Her new approach eliminated the end-of-training quiz entirely. Instead:

    Day 1 (during training session, last 15 minutes):

    Sarah presented core policy information in 30 minutes instead of 45. Then she closed the slides.

    “Put away your notes. Close the handout. For the next 10 minutes, write everything you remember about what constitutes reportable harassment under our policy.”

    Silence. Discomfort. People looked around nervously.

    “No looking back,” Sarah said. “This isn’t graded. Just write what you remember.”

    People struggled. They wrote a few things, crossed them out, wrote more. After 10 minutes, Sarah gave them a one-page reference sheet showing the actual policy elements.

    “Compare what you wrote to this. Notice what you got right. Notice what you missed. That’s your learning.”

    Week 2 (5 minutes via email):

    Sarah sent a scenario:

    “You witness a colleague make an ambiguous comment to another employee. The recipient looks uncomfortable but doesn’t say anything. Is this reportable under company policy? What’s your next step? Reply with your answer.”

    After people responded, she sent the correct answer with a brief explanation. No scoring. No judgment. Just feedback.

    Month 2 (10 minutes in team meeting):

    Sarah presented a realistic case study. Pairs worked together: one person explained the policy, the other identified gaps or errors, then they switched roles.

    Month 3 (5 minutes via Slack):

    “Real situation from this week: [describes actual ambiguous incident]. How does this relate to our harassment policy?”

    Sarah’s Results

    Three months after training, she ran an unannounced quiz using realistic scenarios. Retention: 78%. Nearly three times better than her previous approach.

    More importantly: when actual incidents occurred, employees actually reported them. They recognized the behavior. They knew the procedure. The knowledge was accessible when they needed it.

    Implementation Details Sarah Learned:

    The free-recall brain dump (Day 1) was crucial. It revealed gaps immediately while the information was fresh enough to correct. But it required Sarah to create a clear reference sheet for self-comparison. Without that, learners felt lost.

    The spaced quizzes (Week 2, Month 2, Month 3) worked best when they pulled from all previous content, not just recent material. This prevented learners from forgetting early content while learning later content.

    Email delivery worked fine for simple questions. For complex scenarios, team meetings created better engagement because people could discuss answers.

    The biggest challenge was getting managers to protect the 5-10 minutes required for retrieval activities. Sarah had to show retention data to prove the time was worth it.

    Application 2: Technical Skills Training (CRM System)

    What Sarah’s Colleague Marcus Changed

    Marcus ran software training for their sales team. His old approach: a full-day session where he demonstrated features while learners followed along. “Sandbox environment available for practice.” About 20% actually used it. Within the first month, 60% called the helpdesk for procedures they should have remembered.

    His new approach split the training across two weeks with retrieval built in:

    Session 1 (2 hours):

    Marcus demonstrated core workflows:

    – Creating a contact (10 minutes demo)

    – Logging a call (10 minutes demo)

    – Creating an opportunity (10 minutes demo)

    Then he closed his screen. “Now you try—from memory. Create a contact using your own information. I’m not showing you the steps. Pull from what you just saw.”

    People fumbled. Clicked the wrong buttons. Got lost. Marcus circulated to help but didn’t re-demonstrate. He prompted: “What did you see me do? What was the first step?”

    This immediate retrieval after each demo took 30 minutes total. Messy. Inefficient. Effective.

    Week 1 (5 minutes each morning):

    Marcus sent daily retrieval prompts:

    Monday: “Don’t open the CRM yet. Write down the steps to create a contact.”  

    Tuesday: “Write the steps to log a call.”  

    Wednesday: “Write the steps to create an opportunity.”  

    Thursday: “Now combine them: Write the full workflow from new contact to logged opportunity.”  

    Friday: “Scenario: Client calls with interest in Product X. Write your complete CRM workflow.”

    After each prompt, people opened the CRM and tried the procedure. They compared what they wrote to what they actually had to do.

    Week 2 (Session 2, 90 minutes):

    Marcus presented progressively harder scenarios. Each one required retrieving earlier knowledge:

    “Client wants Product X but has budget concerns. Create the contact, log this call, create an opportunity with notes about the budget issue.”

    People worked from memory. Real client data. Real consequences for errors. Marcus provided help only for genuine system bugs, not forgotten procedures.

    Marcus’s Results

    Within two weeks, 80% of the sales team was independently proficient. Previously this took two months. Helpdesk calls for “how do I…” questions dropped by 70%.

    Sales reps could execute workflows without references. When weird edge cases appeared, they could figure them out because they understood the logic, not just the steps.

    Implementation Details Marcus Learned:

    The immediate retrieval after each demo (Session 1) was the most important intervention. It revealed mistakes while Marcus was there to help. Waiting until later would have let errors solidify.

    The daily prompts required only 5 minutes but had to be consistent. Missing a day broke the spacing pattern. Marcus automated them using email scheduling.

    The hardest part was resisting the urge to re-demonstrate when people struggled. His instinct was to jump in and show them again. But making them figure it out—with prompts, not answers—was what made knowledge stick.

    Marcus learned to use two-part questions: “Write the steps” (retrieval) then “Now try it” (practice). The retrieval before practice was what made practice effective.

    The Other Applications: Quick Hits

    Product knowledge for customer service: Daily “customer question of the day” sent via Slack. Reps answer from memory. Correct answer shared after responses come in. Three months: customer satisfaction scores up 12% because reps can answer questions without putting customers on hold.

    New hire onboarding: Weekly “teach-back” sessions where new hires explain company procedures to each other. Manager observes, corrects errors. Time to productivity: down 18 days compared to traditional onboarding.

    The pattern is consistent. Replace review with retrieval. Space it out. Make it effortful. Measure retention, not completion.

    The Bottom Line: What Changes in Your Training

    Most organizations build training like this:

    1. Deliver information

    2. Check understanding with a quiz

    3. Issue certificate

    4. Hope learners remember

    Sarah learned to flip this completely:

    1. Deliver information (briefly)

    2. Force immediate retrieval

    3. Space retrieval over weeks

    4. Measure actual retention weeks later

    The shift is from “Did they complete it?” to “Can they recall it without help?”

    This requires three fundamental changes in how you design training:

    First, measure retention, not completion.

    Stop celebrating 95% completion rates. Start measuring what learners remember two weeks, four weeks, and three months after training. Use unannounced quizzes with realistic scenarios. If completion is high but retention is low, your training failed.

    Sarah now tracks two metrics: immediate quiz scores (which she expects to be high) and delayed retention scores (which reveal actual learning). Only the second metric matters for program success.

    Second, replace every review session with retrieval.

    Every time you’re tempted to “review the key points,” stop. Instead, ask learners to retrieve the key points from memory. No slides. No handouts. Just retrieval. Then show them what they missed.

    Sarah eliminated all her “refresher” training. She replaced it with spaced quizzes that force retrieval of previous content. The time commitment is 20% of what refreshers required. The retention is 3x better.

    Third, embrace forgetting as a feature.

    The forgetting curve is your friend. Let learners forget a little between retrieval sessions. When they struggle to remember, that difficulty strengthens the memory.

    Sarah spaces her quizzes strategically: 1 day, 1 week, 1 month, 3 months. Each retrieval resets the forgetting curve at a higher baseline. After four retrievals, knowledge sticks for 6+ months without additional intervention.

    Your Checklist for the Next Training Program

    You’re designing new training. Here’s what changes:

    During Design:

    – Cut your presentation time by 30%. Use that time for retrieval instead.

    – Write 20-30 retrieval questions spanning simple recall to complex application.

    – Schedule retrieval sessions at 1 day, 1 week, 1 month, 3 months.

    – Choose question formats: free recall for complex concepts, short answer for procedures, multiple choice only when you need to scale beyond what you can manually review.

    – Plan feedback: immediate for high-stakes content (safety, compliance), delayed by one day for everything else.

    During Rollout:

    – Tell learners why you’re doing this: “This will feel hard. That means it’s working. The struggle is what makes knowledge stick.”

    – Make first retrieval happen immediately after initial training, before learners leave.

    – Monitor completion of spaced retrieval, not just initial training. If people skip the spaced quizzes, they’ll forget everything.

    – Watch success rates. If below 40%, the questions are too hard. If above 90%, they’re too easy. Target 60-75% success.

    During Measurement:

    – Test retention at 2-4 weeks with unannounced quiz.

    – Compare to previous version of same training (or to control group).

    – Measure on-the-job application through manager observation or real performance data.

    – Calculate time-to-proficiency versus your baseline.

    During Iteration:

    – Identify which specific content is forgotten most often.

    – Add extra retrieval practice for those trouble spots.

    – Experiment with spacing intervals (some content needs more frequent retrieval).

    – Test different question formats to find what works for different content types.

    Sarah now designs all training this way. Her programs take less time to deliver. They cost less to maintain. And they produce dramatically better retention.

    Her secret? She makes learning uncomfortable. And she’s stopped apologizing for it.

    The Uncomfortable Truth

    Retrieval practice works. The evidence is overwhelming. Hundreds of studies. Multiple meta-analyses. Consistent effects across ages, materials, and contexts.

    But it requires you to do something that feels wrong: make learning harder.

    Your instinct is to make training smooth, easy, comfortable. Clear slides. Engaging videos. Lots of review. Retrieval practice is choppy, difficult, frustrating. Learners struggle. They forget. They complain.

    And that’s exactly why it works.

    When Sarah’s employees said “This is hard,” she learned to hear “This is working.” When they said “I don’t remember this,” she learned to hear “My brain is building stronger pathways.” When they complained about “too much testing,” she learned to hear “I’m experiencing desirable difficulty.”

    The struggle is the signal.

    Your job isn’t to make learning feel easy. Your job is to make learning last.

    Sarah learned this when she stopped measuring completion rates and started measuring retention. When she stopped asking “Did they like the training?” and started asking “Can they actually do their jobs better?”

    The answer surprised her. Her new training was less popular in post-training surveys. People complained about the quizzes. They wanted more slides, more review, more support.

    But six months later, when she measured performance, her new approach won decisively. People retained more. They performed better. They needed less remedial training.

    The training that felt worse worked better.

    Sarah now tells new L&D professionals: “If learners say your training is too easy, you’re failing them. If they say it’s too hard, you might be failing them. But if they say it’s challenging but they can feel themselves getting better, you’re doing it right.”

    Retrieval practice is how you do that. It’s how you build knowledge that lasts beyond the training room.

    It’s how you turn completion into competence.

    —

    References

    Adesope, O. O., Trevisan, D. A., & Sundararajan, N. (2017). Rethinking the use of tests: A meta-analysis of practice testing. *Review of Educational Research, 87*(3), 659–701.

    Agarwal, P. K., Nunes, L. D., & Blunt, J. R. (2021). Retrieval practice consistently benefits student learning: A systematic review of applied research in schools and classrooms. *Educational Psychology Review, 33*(4), 1409–1453.

    Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), *Metacognition: Knowing about knowing* (pp. 185–205). MIT Press.

    Carpenter, S. K. (2009). Cue strength as a moderator of the testing effect: The benefits of elaborative retrieval. *Journal of Experimental Psychology: Learning, Memory, and Cognition, 35*(6), 1563–1569.

    Carpenter, S. K. (2011). Semantic information activated during retrieval contributes to later retention: Support for the mediator effectiveness hypothesis of the testing effect. *Journal of Experimental Psychology: Learning, Memory, and Cognition, 37*(6), 1547–1552.

    Karpicke, J. D., Butler, A. C., & Roediger, H. L. (2009). Metacognitive strategies in student learning: Do students practise retrieval when they study on their own? *Memory, 17*(4), 471–479.

    Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. *Psychological Science, 17*(3), 249–255.

    Rowland, C. A. (2014). The effect of testing versus restudy on retention: A meta-analytic review of the testing effect. *Psychological Bulletin, 140*(6), 1432–1463.