Capítulo 1
The Bridge to Better Learning: When Science Meets Practice
In 1831, soldiers of the 60th Rifle Corps marched across one of Europe's first suspension bridges near Manchester. Their synchronized steps created a resonant frequency that matched the bridge's natural vibration, causing it to collapse and injuring twenty men. The British Army responded by investigating the incident and issuing a standing order for soldiers to "break step" when crossing bridges-a practical application of evidence that saved lives. This incident serves as a perfect metaphor for the learning profession today, where practitioners often ignore scientific evidence in favor of intuition, fads, and personal beliefs. "Evidence-Informed Learning Design" by Mirjam Neelen and Paul Kirschner has become a cornerstone text for learning professionals seeking to build their practice on solid foundations rather than shifting sands. Since its publication, it has influenced countless training programs and sparked important conversations about how we design learning experiences in workplaces worldwide.
Capítulo 2
Building on Scientific Foundations: The Learning Sciences
The learning sciences form the backbone of evidence-informed learning design-an interdisciplinary field focused on advancing scientific understanding of learning while improving instructional methodologies. After 50 years of research, learning scientists have reached consensus on several key principles: learning should focus on deeper conceptual understanding rather than just facts, exemplified by teaching problem-solving strategies instead of mere formulas; quality instruction must be balanced with active learner participation through methods like guided discovery and collaborative projects; effective learning environments have identifiable features including clear goals, appropriate challenge levels, and meaningful feedback loops; learning builds on prior knowledge by connecting new information to existing mental models; reflection enhances learning through deliberate practice and self-assessment; and transfer of learning to new situations matters more than mere comprehension, demonstrated when learners can apply concepts across different contexts.
These principles draw from multiple influential theories including behaviorism (learning as observable performance changes through stimulus-response associations, like programmed instruction and reinforcement schedules), cognitive science (examining mental processes like perception, memory and reasoning, including cognitive load theory and information processing models), and constructivism (individuals imposing meaning on the world based on previous experiences, as seen in inquiry-based learning and social learning theory). Newer influences include neuroscience, revealing how brain plasticity affects learning, and artificial intelligence, which offers insights into pattern recognition and adaptive learning, though their practical applications for learning design remain limited due to the complexity of translating laboratory findings to real-world settings.
Despite this rich scientific foundation, many learning professionals make decisions based on intuition or trends rather than evidence. This creates what the authors call the "Photoshop explanation" phenomenon-a situation where self-proclaimed experts ("quacksperts") spread unverified information, making it difficult to distinguish fact from fiction. Common examples include learning styles theory, right/left brain learning, and brain training games, which persist despite lack of scientific support. In this environment, opinions often trump empirical evidence, resulting in ineffective methods being embraced while proven approaches like spaced practice and retrieval practice are ignored.
The solution? Learning professionals must adopt Daniel Willingham's four-step framework for evaluating claims: 1) Strip it and flip it (examine language for vagueness or manipulation, particularly watching for buzzwords and oversimplified promises); 2) Trace it (investigate evidence sources, looking for peer-reviewed research and replication studies); 3) Analyze it (apply critical thinking to evaluate claims, considering alternative explanations and potential biases); and 4) Decide whether to implement the approach based on contextual factors and available resources. By following respected researchers who do quality research-to-practice work, such as those published in peer-reviewed journals and established educational research institutions, learning professionals can build their practice on solid evidence rather than shifting trends. This approach ensures that learning design decisions are grounded in scientific principles rather than popular but unproven methodologies.
Capítulo 3
Designing Learning Experiences: Beyond Content Delivery
What exactly are "learning experiences" and how do we design them effectively? The term "learning experience design" acknowledges that while we can't design learning itself-which happens in an individual's brain-we can design experiences that facilitate effective, efficient, and enjoyable learning. This approach embraces learning's contextual nature, occurring across various settings from classrooms to workplaces, online platforms, and informal learning environments.
Unfortunately, many workplace learning solutions suffer from atomistic design, which creates three key problems: artificially dividing knowledge from skills acquisition, fragmenting complex goals into isolated parts with separate learning activities for each objective, and failing to support learning transfer to real-world contexts. For example, a sales training program might separately teach product knowledge, communication skills, and negotiation techniques without integrating these elements into realistic scenarios that reflect actual sales situations.
Designing holistic learning experiences requires starting with a strong foundation: identifying actual performance problems through observation rather than assumptions; determining if learning can truly address the issue; clarifying measurable success criteria; and understanding learners' needs. This involves conducting thorough needs assessments, shadowing top performers, and gathering data about common challenges and mistakes. All steps should focus on authentic learning tasks that mirror real-life situations across five dimensions: the task itself, physical context, social context, assessment results, and performance criteria. For instance, medical training might incorporate simulated emergency scenarios in mock hospital rooms with team dynamics and time pressures that match actual workplace conditions.
The authors introduce the concept of "three-star learning experiences" that embody two "holy trinities": 1) effective, efficient, and enjoyable learning, and 2) techniques, tools, and ingredients to achieve learning. Effective learning means learners acquire what they need in the allotted time, demonstrating measurable improvement in performance. Efficient learning requires less time or mental effort while maintaining quality, often achieved through careful sequencing and scaffolding of learning activities. Enjoyable doesn't mean "fun" but rather creating experiences where learners feel accomplishment and increased self-efficacy, such as through progressive challenges that build confidence.
These three elements must be balanced-improving one should never harm the others. For example, making learning more "enjoyable" by adding game elements shouldn't reduce effectiveness by distracting from core learning objectives. Similarly, efforts to increase efficiency shouldn't compromise the depth of learning or learner engagement. Success requires careful consideration of cognitive load, motivation theory, and performance support tools that can enhance learning without creating unnecessary complexity.
The design process should incorporate regular feedback loops and iterative improvements based on learner performance and responses. This might include pilot testing, gathering user feedback, measuring learning outcomes, and adjusting design elements accordingly. Effective learning experiences also consider the broader organizational context, ensuring alignment with business goals and integration with existing systems and processes.
Capítulo 4
Eyes Wide Open: Confronting Professional Blind Spots
Despite significant advances in learning sciences over the past decades, many practitioners continue to operate without incorporating crucial research findings into their design decisions. The learning profession often operates with "eyes closed" by systematically ignoring or dismissing evidence that contradicts established practices or deeply held beliefs. This troubling pattern reveals an immature profession struggling to build a solid scientific foundation, where practitioners frequently lack awareness of learning sciences or actively resist applying evidence-based approaches in workplace contexts.
Several interconnected factors contribute to this professional blindness: learning professionals come from diverse educational and career backgrounds with varying levels of scientific literacy; many practitioners struggle to critically evaluate and interpret research data; and some incorrectly assume that research conducted on students in educational settings cannot be meaningfully applied to adult workers in corporate environments. The challenge is compounded when stakeholders view learning professionals merely as service providers rather than strategic partners and subject matter experts, often forcing practitioners to implement stakeholders' intuition-based approaches instead of evidence-backed solutions.
To overcome these challenges, learning professionals must develop robust skills for evaluating research trustworthiness and validity. This includes carefully examining research methodology and design (with randomized controlled trials representing the highest standard of evidence), sample size and scale (larger participant numbers typically produce more reliable and generalizable results), participant dropout rates and their potential impact on findings, data quality measures (including both reliability and validity metrics), and any potential conflicts of interest that might influence the results. Gorard's "sieve" framework provides a systematic approach through six essential categories: design, scale, dropout, outcomes, fidelity, and validity. This framework helps practitioners rate a study's trustworthiness on a scale from 0 to 4 stars, offering a practical tool for evidence evaluation.
Learning professionals should exercise particular caution when encountering commercially driven research, especially when companies investigate their own products or services without proper scientific controls or peer review. Critical analysis should focus on several key aspects: identifying who conducted and funded the research, examining the robustness of the study design and methodology, tracking primary source references rather than relying on secondary citations, and carefully scrutinizing how outcomes were measured and reported. By developing these analytical skills, practitioners can better distinguish between legitimate scientific evidence and marketing materials disguised as research.
The path forward requires learning professionals to embrace their role as evidence-based practitioners while actively working to expand their scientific literacy. This includes staying current with peer-reviewed research, participating in professional development focused on research methodology, and building networks with academic researchers in the learning sciences. Only by keeping our eyes wide open and maintaining high standards for evidence can the profession mature and deliver consistently effective learning solutions.
Capítulo 5
Facing Fallacies and Myths: Zombie Ideas That Won't Die
Learning myths persist like zombies that refuse to die, causing significant damage to the field. These myths waste money, resources, time, and effort while distracting professionals from evidence-based approaches. Four main reasons make these myths indestructible: 1) The complexity of scientific evidence makes it hard for practitioners to grasp; 2) Myths strengthen our sense of belonging to groups and provide cultural guidance; 3) The overwhelming availability of information means much unreliable content spreads rapidly; 4) The "Photoshop effect" where, since anything can be manipulated, no experts can be trusted.
Several persistent myths deserve debunking:
Myth 1: Neuroquatsch (Neuro-nonsense)
Brain science has spawned stubborn neuromyths in workplace learning. The most persistent myth is that anything with "neuro" or "brain" in its name is automatically beneficial. While neuroscience can inform learning theory, it faces several problems when applied directly to learning: the "goals problem" (neuroscience can't inform learning design decisions), the "vertical problem" (studying isolated cognitive functions is too simplistic for learning), and the "horizontal problem" (difficulty marrying neuroscientific and learning theories).
Despite neuroscientific evidence disproving learning styles, workplace professionals continue pigeonholing people into categories. Cognitive differences between people exist on continuums rather than in distinct categories; self-report measures used to determine learning styles are unreliable; and with at least 71 different learning style models, there are over 2 trillion possible combinations, making practical application impossible.
Myth 2: What learners say they prefer is good for them
The belief that learners' expressed preferences should guide instructional design is misguided. While learners can provide valuable feedback on certain aspects (like application relevance), their suggestions about how they learn best often don't align with evidence-based approaches. Research typically shows that effective instructional methods benefit all learners regardless of their preferences. Richard Clark found that learner preferences were typically uncorrelated or negatively correlated to learning outcomes-learners often don't benefit from their preferred approaches.
Myth 3: Google can replace human knowledge
Many believe we no longer need to learn facts since Google provides instant access to information. This overlooks a crucial point: we need knowledge to interpret the information Google provides. Knowledge differs from data and information-data are raw numbers, information is processed data, and knowledge is how we interpret these in our heads. While Google can enhance our memory by helping us find information we partially recall or look up procedures for routine tasks, it becomes nearly useless for complex, non-recurrent skills and ill-structured problems.
Myth 4: 21st-century skills are more important than domain-specific knowledge
Despite widespread popularity, "21st-century skills" lack clear definition. They're broadly described as transferable competencies like problem-solving, decision-making, communication and collaboration that supposedly transcend specific domains. However, our cognitive architecture relies on long-term memory (LTM), which distinguishes humans from other species by its virtually limitless capacity. What we think of and even how we think is overwhelmingly determined by our knowledge held in LTM.
If problem-solving were truly a generic skill, people with no domain knowledge should theoretically outperform those with domain expertise when applying "strategic" problem-solving skills. Research comparing experts and novices contradicts this. Experts possess schemas for encoding multiple elements as single units, acquire skills without needing to recall rules, have automated many complex processes, and use strong domain-specific approaches. The relationship between knowledge and "21st-century skills" is actually the inverse of what's commonly claimed-domain-specific knowledge in our long-term memory actually supports and enables all so-called 21st-century skills.
Capítulo 6
Finding Focus: Evidence-Based Tools and Techniques
After debunking myths, the authors turn to evidence-based approaches for designing effective learning experiences. They present several key tools and techniques:
Complex Skills Design
Complex skills require an integrated approach rather than teaching fragmented pieces. The first critical step is designing learning tasks based on the hierarchy of constituent skills, using real-life tasks organized from simple (yet authentic) to complex categories. Each task class should provide appropriate guidance that fades as learners progress, with two main categories of support: supportive information (helping with non-recurrent aspects through conceptual models) and procedural information (enabling recurrent aspects through just-in-time displays and feedback).
Effective Tools
Common computer programs serve as both productivity tools and "mindtools" that enhance thinking. Spreadsheets help develop critical thinking about variables and relationships. Search engines build complex information problem-solving skills. Word processors improve writing skills through features like Outline view.
Research consistently shows handwritten notes produce better learning outcomes than digital note-taking. When writing by hand, we process information more deeply because we must paraphrase, analyze and synthesize content due to writing's slower pace.
While multimedia can be effective for learning, it's merely a vehicle-the instructional methodology remains the critical factor. Common assumptions about multimedia learning (that it automatically leads to more learning, is more motivating, or adapts to learning styles) lack convincing evidence.
Direct Instruction
Despite misconceptions that direct instruction is boring or outdated, it's one of the most effective techniques for teaching novice learners. Direct instruction consists of six steps: reviewing previous material, stating objectives, presenting new material, guiding practice with feedback, assigning independent practice with feedback, and periodic review. Research consistently shows positive effects on learning outcomes across hundreds of studies spanning 50 years.
Feedback
Feedback is perhaps the most powerful technique for supporting learning, with John Hattie finding an effect size of 0.79 (twice the average of other educational interventions). Effective feedback aims to reduce the gap between current understanding and desired goals through three key focuses: feed up (Where am I going?), feed back (How am I doing?), and feed forward (Where to next?).
Three key types of feedback serve different purposes: corrective feedback (single-loop) simply indicates right/wrong answers; directive feedback (double-loop) shows better ways to complete tasks; and epistemic feedback (triple-loop) prompts learners to think about why and how to improve through guiding questions. The effectiveness of feedback depends on clear learning objectives, making feedback a habit that learners actively use, proper timing, and consideration of the learner's skill level and prior knowledge.
Capítulo 7
Evidence-Based Learning Ingredients
The authors introduce five fundamental "ingredients" that form the foundation of effective learning design:
1. Worked Examples
Worked examples demonstrate both the product (beginning state, goal state and solution) and process (problem-solving steps with rationale) of solving problems. They're particularly effective for novices who haven't yet developed problem-solving schemas because they reduce extraneous cognitive load, allowing learners to use their limited cognitive resources more effectively for schema construction and automation.
2. Spaced Learning
Spaced learning involves tackling learning in multiple short sessions rather than cramming the same material into one long session. This approach strengthens memory traces through repeated retrieval from long-term memory. Each review session not only returns retention to 100% but also makes the forgetting curve progressively less steep. Learning across different settings enhances retention because learners must mentally reinstate prior learning contexts differently each time, strengthening memory through context updating and integration.
3. Retrieval Practice
Retrieval practice involves actively remembering previously learned information. Unlike passive rereading, retrieval practice forces learners to pull information from long-term memory, significantly improving both storage strength and retrieval strength. Meta-analysis found retrieval practice outperforms all other learning strategies, including rereading (the most common approach) and restudying, with a high effect size of 0.61.
4. Interleaving
Interleaving involves varying the types of exercises and alternating between related topics when practicing. Unlike blocked practice where learners complete similar problems in sequence, interleaving mixes up problem types so learners must identify which strategy to apply. This approach costs nothing extra but yields significant benefits: learners must decide which approach to use, recognize distinguishing elements between problems, make fewer errors long-term, and achieve better transfer of knowledge to novel situations.
5. Double-Barrelled Learning
Double-barrelled learning leverages our dual cognitive processing systems to enhance learning through appropriate combinations of words and images. Based on Paivio's dual coding theory, this approach recognizes that humans process verbal and visual information through separate yet connected cognitive subsystems. When properly combined, words and images create stronger memory traces because images get coded twice (visually and verbally), while utilizing both visual and verbal working memory capacity.
Capítulo 8
The Learner in the Driver's Seat
The final section shifts focus to empowering learners to take control of their own development. Workers face multiple forms of professional obsolescence in today's rapidly changing organizations. Continuous learning through self-directed and self-regulated approaches is especially crucial in knowledge-intensive workplaces where formal learning experiences aren't always effective for solving complex, novel problems.
Self-Directed Learning (SDL) and Self-Regulated Learning (SRL)
SDL and SRL are often used interchangeably but have fundamental differences. SDL functions at the macro-level (overall learning process/trajectory), while SRL operates at the micro-level (specific task-focused learning). Both are higher-order metacognitive skills that must be trained within particular domains to transfer effectively.
Our faulty mental models about learning create significant barriers to effective SDL/SRL. We often misjudge the quality of our learning outcomes, incorrectly believe errors should be avoided during learning, and tend to overvalue innate differences like talent or intelligence while undervaluing practice and effort.
Learning professionals can help learners overcome these faulty mental models both explicitly (teaching "tips and tricks" about learning, explaining domain knowledge's role in SDL/SRL) and implicitly (enabling learners to compare self-assessments with external evaluations). They can provide guidance materials like structured conversation frameworks and reflection prompts to help learners establish goals, monitor progress, and evaluate their learning approaches.
Personal Learning Networks (PLNs)
A personal learning network is a trusted network of connections that supports continuous professional learning. Effective PLN engagement involves four key behaviors: consuming (discovering information), creating (authoring resources), connecting (with people and information sources), and contributing (sharing knowledge back to the network). When building a PLN, seek people who offer different perspectives, share values you appreciate, demonstrate passion and expertise in relevant areas, inspire you, can be trusted, provide fresh thinking, and offer reality checks on your ideas.
Organizations must move beyond supporting individual learning to become true "learning organizations" that constantly learn collectively and transform themselves to better use knowledge. They can foster this symbiosis by developing shared vision, encouraging continuous learning, promoting teamwork, providing necessary freedom and trust, and creating technology-enabled environments.
Capítulo 9
Building Bridges That Won't Collapse
The authors use the dramatic collapse of the Broughton suspension bridge in 1831 as a powerful metaphor for the current state of learning design. Just as the bridge failed because its engineers didn't understand the principles of mechanical resonance, many learning initiatives fail because practitioners ignore established scientific evidence in favor of popular but unproven methods. This historical example serves as a stark reminder that good intentions without solid scientific backing can lead to spectacular failures.
Learning professionals must break free from three common pitfalls: relying on personal beliefs rather than evidence, disregarding research that contradicts their preferred approaches, and chasing every new trend that promises revolutionary results. These behaviors mirror the pre-scientific era of engineering, when bridge builders relied more on tradition and intuition than mathematical principles and physical laws.
The concept of "idle innovation" is introduced as a crucial counterbalance to the industry's constant push for novelty. This approach advocates for thoughtful pauses in the pursuit of new methods, allowing time to critically evaluate what truly works. Just as modern bridge engineers carefully study structural principles before innovation, learning designers should thoroughly understand cognitive science and learning research before implementing new approaches.
The authors emphasize a whole-task design approach, which considers all elements of the learning experience in context, similar to how engineers must consider all forces acting on a bridge. This includes understanding cognitive load, information processing, motivation, and transfer of learning. Successful learning experiences, like successful bridges, must balance multiple factors and constraints.
By learning from past failures in both education and engineering, practitioners can build more resilient learning solutions. The authors point to specific examples where evidence-based practices have produced superior results: spaced practice over massed practice, retrieval practice over passive review, and worked examples for novice learners. These proven techniques, like the principles of modern bridge engineering, have emerged from rigorous testing and systematic observation rather than intuition or popular opinion.
The ultimate goal is to create learning experiences that are not only effective and efficient but also enjoyable and sustainable - structures that, like well-designed bridges, can reliably serve their purpose for years to come. This requires a commitment to evidence-based practice, continuous evaluation of outcomes, and the courage to abandon popular but ineffective methods in favor of scientifically-supported approaches.
