Design For How People Learn: Key Insights & Takeaways

Why do most training programs fail to change behavior? Julie Dirksen's Design For How People Learn answers this question by bridging the gap between learning science and practical instructional design. The uncomfortable truth is that if learners leave your training knowing more but doing the same things, you've delivered content—but you haven't created learning.

This guide breaks down Dirksen's complete framework for designing learning experiences that account for how memory actually works, what genuinely motivates learners, and how to overcome the obstacles that prevent new skills from transferring to real-world performance. Whether you're building corporate training, educational curricula, or simply trying to learn more effectively yourself, these principles will transform how you think about the gap between information and behavior change.

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What's the difference between information and learning?

The difference between information and learning is behavior change. If someone can recite facts but their actions remain unchanged, information was delivered but learning didn't occur. This distinction challenges the widespread assumption that exposure equals learning—that if we tell people something clearly enough, they'll absorb it and apply it.

Consider teaching someone to ride a bicycle. You could explain the physics of balance, describe the pedaling motion, and detail how to steer. They might understand every concept perfectly and still fall over the moment they mount the bike. The gap between knowing and doing is where most training fails. True learning design must bridge this gap, ensuring that understanding concepts translates into changed outcomes.

This is precisely why Loxie focuses on active recall rather than passive review. Reading about learning principles isn't the same as being able to apply them when designing your next training program. Loxie's spaced repetition system forces you to practice retrieving these concepts, building the neural pathways that make knowledge accessible when you need it.

How do you diagnose why learners aren't performing?

Effective learning design begins with diagnosing whether learners face knowledge gaps (they don't know), skill gaps (they can't do), motivation gaps (they won't do), or environment gaps (they can't apply). Each gap type requires fundamentally different interventions, and misdiagnosis leads to wasted resources and unchanged behavior.

The Will vs. Skill Diagnostic

A powerful diagnostic question cuts through confusion: Could this person perform the task correctly if their life depended on it? If someone could execute the behavior with a gun to their head, it's not a skill gap—it's a motivation or environment problem. Training cannot fix unclear expectations, lack of consequences, missing resources, or competing priorities.

This explains why so much corporate training fails. Organizations default to training as the solution for every performance problem, when the real issues are often systemic. Teaching time management to someone with no control over their calendar, or customer service skills when metrics only reward speed, creates frustration rather than performance improvement.

The Four Gap Types

Knowledge gaps mean learners genuinely don't know what to do or why. These respond to information delivery—explanations, examples, and conceptual frameworks. Skill gaps mean learners understand but can't execute. These require practice with feedback, not more information. Telling someone how to ride a bike won't make them able to ride.

Motivation gaps mean learners could perform but choose not to. These require addressing beliefs, consequences, or emotional barriers. Knowing the benefits of exercise doesn't overcome the motivational barriers to actually going to the gym. Environment gaps mean external factors prevent application. Even well-trained employees fail when systems, tools, or organizational culture actively block new behaviors.

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How does memory actually work?

Information enters through sensory memory (lasting 0.5-3 seconds), gets filtered by attention into working memory (lasting about 20 seconds), and only transfers to long-term memory through active processing and rehearsal. This three-stage model explains why passive listening fails—without active engagement to move information through each stage, it never reaches long-term storage and is lost within seconds.

Working Memory's Severe Limitations

The brain's working memory can only hold 7±2 items at once. This cognitive bottleneck explains why learners become overwhelmed when presented with too much new information simultaneously. Learning designs must chunk information into meaningful patterns and provide scaffolding that gradually reduces as expertise develops.

This limitation also explains the dramatic difference between novice and expert performance. Experts aren't holding more items in working memory—they've developed sophisticated chunking patterns that allow them to process larger amounts of information as single units. A chess master sees board positions, not individual pieces. A skilled programmer sees patterns, not lines of code.

The Forgetting Curve's Brutal Reality

We lose approximately 90% of new information within a week unless we actively use it. This rapid decay explains why intensive training workshops often fail to create lasting change—without immediate application and spaced repetition, even well-understood concepts quickly fade from memory. The forgetting curve isn't a bug; it's a feature of a brain that prioritizes frequently-used information.

This is exactly why Loxie exists. Understanding memory science intellectually is one thing; actually retaining it is another. Loxie's spaced repetition algorithm resurfaces concepts right before you'd naturally forget them, turning the forgetting curve from an enemy into an ally.

Why does retrieval practice beat re-reading?

Retrieval practice strengthens memory more than repeated study. The act of forcing yourself to pull information from memory—through quizzes, recall exercises, or self-testing—creates stronger neural pathways than re-reading the same material multiple times. This testing effect shows that the effort of retrieval itself modifies memory storage.

This finding has profound implications for learning design. Most people's study strategy involves highlighting, re-reading, and passive review. These feel productive because the information seems familiar. But familiarity isn't the same as retrieval ability. You might recognize a concept when you see it while being completely unable to recall it when you need it.

Effective learning designs incorporate frequent low-stakes retrieval opportunities. Rather than presenting information and moving on, they ask learners to recall, apply, and reconstruct knowledge repeatedly. Each retrieval attempt strengthens the memory trace, making the information more accessible for future use.

Reading about retrieval practice won't help you remember it.
Loxie applies these exact principles to help you retain what you learn. Instead of re-reading this guide, practice retrieving these concepts with spaced repetition.

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Why does spacing learning over time work so much better?

Spacing learning over time (distributed practice) creates approximately 200% better retention than massed practice. Four 1-hour sessions beat one 4-hour session, even with identical content. The spacing effect occurs because forgetting between sessions forces effortful retrieval, strengthening memory consolidation in ways that continuous study cannot achieve.

This contradicts how most training is delivered. Organizations schedule intensive workshops, boot camps, and all-day sessions because they're logistically convenient. But the brain doesn't care about convenience. It consolidates memories during sleep and the intervals between learning sessions. Cramming everything into concentrated blocks works against the brain's natural learning rhythms.

The counterintuitive insight is that some forgetting is actually beneficial. When you return to material after a gap, the effort required to reactivate that knowledge strengthens the memory trace. Learning that feels effortless during the session often fails to produce lasting change, while learning that requires struggle creates durable retention.

What is cognitive load and why does it matter for learning design?

Cognitive load has three types—intrinsic (the inherent complexity of material), extraneous (load created by poor design), and germane (beneficial processing that builds understanding). Learning fails when total load exceeds working memory capacity. Effective design minimizes extraneous load through clear organization and maximizes germane load through meaningful practice.

Reducing Extraneous Load

Extraneous load comes from confusing layouts, separated information that must be mentally integrated, irrelevant details, and unclear instructions. The split-attention effect occurs when learners must mentally integrate separated elements—like diagrams and text on different pages—doubling cognitive load compared to integrated presentations. Labels directly on diagrams work better than separate legends.

Every moment spent deciphering poor design is a moment not available for understanding content. Clear visual hierarchies, logical information flow, and eliminating unnecessary complexity preserve precious working memory capacity for actual learning.

Maximizing Germane Load

Germane load is the productive mental effort that builds understanding—connecting new information to existing knowledge, constructing mental models, and practicing retrieval. Unlike extraneous load, germane load should be maximized. The goal isn't to make learning effortless but to ensure that effort is directed toward meaningful processing rather than fighting with poor design.

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Why do experts and novices need different learning designs?

Expert learners need different designs than novices—experts benefit from problem-based learning and minimal guidance, while novices need structured instruction and worked examples. This expertise reversal effect means that detailed step-by-step instructions that help beginners actually hinder experts by imposing unnecessary cognitive load.

Worked Examples for Novices

Novices trying to solve problems while simultaneously learning procedures experience cognitive overload. They lack the mental models to know what to pay attention to or how to approach the problem. Worked examples—showing step-by-step solutions with explanations—help learners build mental models before attempting independent practice. The learner can focus on understanding the approach rather than struggling with both learning and performing simultaneously.

Problem-Based Learning for Experts

Experts have sophisticated mental models that allow them to chunk information effectively and approach problems strategically. Forcing them through detailed step-by-step instructions wastes their time and prevents them from applying their knowledge. They benefit from complex scenarios, minimal scaffolding, and opportunities to exercise their expertise.

The curse of knowledge makes subject matter experts particularly poor at designing for novices. They can't unknow what they know, systematically underestimating learning challenges. Concepts obvious to them require significant mental effort for beginners. This explains why subject matter experts often create overwhelming training materials—they've forgotten what it's like not to understand.

What is deliberate practice and why is it essential for skill development?

Deliberate practice requires four elements: well-defined specific goals, immediate feedback, repetition at the edge of ability, and focused attention. Missing any element dramatically reduces skill development. This explains why years of experience doesn't equal expertise—without deliberate practice targeting weaknesses with feedback, people plateau and simply repeat what they already know.

Consider a surgeon with 20 years of experience versus one with 5 years. If the veteran surgeon has been performing the same procedures the same way without feedback or challenge, they may be less skilled than the junior surgeon who actively sought difficult cases and feedback. Experience without deliberate practice is just repetition.

The key distinction is that deliberate practice is uncomfortable. It requires working at the edge of your ability, where you're making mistakes and receiving correction. Comfortable practice feels better but produces less learning. This is why effective learning design must push learners beyond their comfort zone while providing the support needed to recover from errors.

How should scaffolding change as learners develop competence?

Scaffolding should fade as competence grows. Provide maximum support for novices through templates, checklists, and worked examples, then systematically remove aids to build independent performance. Like training wheels that must eventually come off, permanent scaffolding creates learned helplessness where learners remain dependent rather than developing autonomous skill.

The mistake many designers make is keeping scaffolding in place indefinitely. This feels supportive but prevents learners from developing independent capability. The goal isn't to make tasks easy forever but to gradually transfer responsibility to the learner as their competence grows.

Effective fading requires monitoring performance and adjusting support accordingly. As learners demonstrate proficiency with scaffolded tasks, remove one support at a time while watching for breakdown. If performance degrades, restore support temporarily and fade more gradually.

Why does learning context matter so much?

Learning experiences should match the context where skills will be used. Practicing customer service in a quiet classroom won't prepare someone for a noisy call center with upset customers and time pressure. Context-dependent memory means we recall information better in environments similar to where we learned it.

This has profound implications for training design. The gap between classroom learning and real-world application isn't just a transfer problem—it's a memory problem. Knowledge encoded in one context may simply be unavailable in a different context, even when the learner "knows" the material.

Authentic practice environments incorporate the noise, pressure, distractions, and emotional dynamics of real performance. Simulation, role-play, and on-the-job practice bridge the context gap that classroom training cannot cross. Variable practice conditions—changing contexts, problems, and constraints—create more robust skills than blocked practice, even though performance during learning appears worse.

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What drives intrinsic motivation in learners?

Intrinsic motivation comes from autonomy (choice), mastery (progress), and purpose (meaning). Designs that offer learner control, visible skill progression, and clear relevance sustain engagement without external rewards. These three psychological needs explain why gamification often fails when it only adds points and badges without addressing underlying desires.

The WIIFM Factor

Understanding your learners' WIIFM (What's In It For Me) is crucial. Adult learners engage when they see immediate relevance to their goals, problems, or pain points. They are problem-centered rather than subject-centered, needing to understand how new knowledge solves their specific challenges before they'll invest cognitive effort.

This means starting with the "why" before the "how." Learners who understand the relevance of material are more likely to engage deeply, persist through difficulty, and apply what they learn. Opening with abstract concepts before establishing relevance is fighting against adult learning psychology.

Creating Flow States

Flow state—the optimal challenge zone where skills match task difficulty—creates intrinsic motivation. Tasks that are too easy create boredom; tasks that are too hard create anxiety. Adaptive learning designs that adjust difficulty based on performance keep learners in this engagement sweet spot.

Curiosity gaps also drive engagement. Revealing what learners don't know but want to know—through questions, mysteries, or partial information—creates intrinsic motivation to pay attention. The brain pays attention to incomplete patterns and unresolved questions.

Why do environmental barriers often matter more than individual skills?

Environmental barriers often matter more than individual skills. Even well-trained employees fail when systems, tools, or organizational culture actively prevent application of new knowledge. This systems perspective reveals why individual training often fails: the problem isn't the person, it's the context they're operating in.

Consider teaching someone customer service excellence when metrics only reward call speed. Or time management skills to someone who has no control over their calendar. Or safety procedures when production pressure makes shortcuts necessary. Training individuals while ignoring systemic barriers creates frustration, not performance improvement.

Performance support tools—job aids, checklists, and reference materials available at the point of need—often solve problems more effectively than training. Rather than training pilots to memorize every emergency procedure, providing clear checklists ensures correct performance under stress. Sometimes the answer isn't to train harder but to design better environments.

The real challenge with Design For How People Learn

Here's the irony: you now understand more about effective learning design than most trainers and educators. But understanding these principles intellectually and being able to apply them are two different things. The forgetting curve applies to this content too—within a week, you'll lose most of these insights unless you actively use them.

How many professional development books have you read that felt transformative in the moment but left no lasting trace on your practice? How many training sessions have you sat through that evaporated from memory within days? The problem isn't that this information isn't valuable. The problem is that information delivery alone isn't learning.

How Loxie helps you actually remember what you learn

Loxie applies the exact principles from this book to help you retain what you learn. Instead of reading once and hoping something sticks, Loxie uses spaced repetition to resurface concepts right before you'd naturally forget them. The questions force retrieval practice—pulling information from your memory rather than just recognizing it—which builds the strong neural pathways that make knowledge accessible when you need it.

The entire process takes about 2 minutes a day. And the free version includes this book's concepts in its full topic library, so you can start reinforcing these learning design principles immediately. Because the real test isn't whether you can recognize these ideas when you see them again—it's whether you can recall and apply them when you're designing your next learning experience.

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