Summary: Learning Science for Instructional Designers by Clark N. Quinn

From Cognition to Application. Embark on a transformative journey with “Learning Science for Instructional Designers” and unlock the secrets to effective and engaging instructional design. This insightful guide by Clark N. Quinn is a treasure trove of strategies that promise to elevate your educational content to new heights of excellence.

Dive deeper into the world of instructional design and discover how to apply learning science to create impactful learning experiences. Keep reading to master the art of educational innovation.

Genres

Educational Technology, Cognitive Science, Instructional Design, E-Learning, Professional Development, Curriculum Planning, Pedagogy, Adult Education, Corporate Training, Human Resource Development

“Learning Science for Instructional Designers” by Clark N. Quinn distills the essence of learning science into an accessible format, emphasizing the importance of aligning instructional design with how people learn. The book covers key topics such as cognitive load, motivation, spaced practice, and the role of humor in learning. Quinn provides practical insights into creating learning experiences that are not only memorable but also facilitate long-term retention and transfer to real-world situations.

Review

Clark N. Quinn’s “Learning Science for Instructional Designers” is a vital resource for anyone in the field of education or training. The book is praised for its clear and informative presentation of cognitive science principles and their application to instructional design. It is particularly lauded for its actionable tips and the inclusion of prompts at the end of each chapter, which encourage readers to reflect on and apply the concepts to their work1. The consensus among readers is that this book is an indispensable guide for creating effective and engaging learning experiences.

Recommendation

By concisely summarizing vital learning-science principles, learning technology expert Clark Quinn provides a guide for instructional design strategies. With a basis on insights into known and effective design research and practices, this guide is ideal for new – and perhaps intermediate-level – instructional designers. Quinn takes care to provide examples of both effective (and less effective) instructional designs. He provides detailed guidelines on how to separate hype and myth from valid, evidence-based learning science. Quinn advocates for instructional design practices that emphasize learning by doing.

Take-Aways

• “Learning science” explores research and evidence into what helps and what hurts learning.
• Learning happens when neurons fire together and then wire together.
• In the process of learning design, minimize distractions and guide learners to effectively focus their attention.
• Structure learning deliberately, using feedback, models and realistic context.
• Emotion and social connections play critical roles in learning.
• Encourage learners to practice and reflect on thinking and learning.
• Emphasize action and doing over passive learning.

Summary

“Learning science” explores research and evidence into what helps and what hurts learning.

Learning science owes its origins to psychologist Hermann Ebbinghaus’s exploration of memory in the 1800s. His forgetting curve explained the speed at which memory deteriorates. In the decades that followed, various theories of learning emerged, including the “constructivist” view that learning comes from experience and doing. These discoveries and other research matter in learning – just as they do in medicine and other serious fields. Instructors should keep pace with learning research to apply the most rigorous findings in their teaching, and to help them avoid fads, hype and myths.

“If we’re truly being professionals about designing learning, there’s a clear onus to be aware of what learning science tells us.”

As an instructor, you should set a high standard for the research and evidence you incorporate into your work. Beware of meaningless catchphrases like “neurolearning” or “brain-based learning.” Rely on peer-reviewed and replicated research wherever possible. Follow reputable thought leaders who translate academic jargon into insights that practitioners can apply.

Learning happens when neurons fire together and then wire together.

Human brains contain vast networks of neurons that interact with each other. When you think or learn, you activate and strengthen those connections. Your thoughts form through patterns of neuronal activity. Each concept activates a different pattern, and repeated activation strengthens the patterns. For example, the concept of leadership triggers a certain pattern, rather than a single “leadership neuron,” in your brain.

You take information in through your senses – sight, sound, touch, smell and taste. Sight and sound prove most relevant in learning, which includes listening, reading and other visuals. Ensure that learners can easily detect key information by using appropriate channels and allowing sufficient time for comprehension. Enable control over dynamic media like videos by pausing or restarting. Also, consider accessibility and audience limitations, supplying learners with supporting mechanisms like screen readers, for example.

“Our job, then, is to help learners filter out unnecessary distractions and know where to point their attention.”

The process of attention, which transfers information from your senses to conscious thought, is limited. It requires effort. It’s mostly under conscious control, allowing you to focus on specific tasks or information. However, unexpected stimuli, like hearing your name in a crowd, can attract attention involuntarily. Attention isn’t time-limited, but can undergo influence from distractions, especially in today’s digitally saturated world.

In the process of learning design, minimize distractions and guide learners to effectively focus their attention.

Working memory, where conscious awareness resides, has limited capacity. Generally, it holds between two and four elements, and potentially fewer for young children. Avoid overwhelming learners’ working memory by limiting the volume of information that needs processing. For example, if you were teaching people how to change a tire, you wouldn’t want to distract them (or overload their working memory) by telling them about automotive history.

Help learners develop skills to retain information for greater lengths of time and to move it to long-term memory. “Chunk” large amounts of information into smaller, more digestible and memorable “bites.” Link new learning to students’ previous experience. These interactions between working memory and long-term memory help learners make sense of unfamiliar information by mapping it to what they already know.

Be clear about what information learners need to memorize and what they can reference. Remember, simple repetition doesn’t guarantee long-term memory transfer. Instead, deep understanding and recall are crucial for information to make it into long-term memory. Deep understanding involves linking new information to what you already know, thus solidifying the new idea in your memory. When you activate a new idea in the context of well-practiced ideas, you strengthen connections, creating patterns of information.

“If we learn knowledge without applying it, it’s unlikely to be available when needed.”

Recall involves bringing stored knowledge to your conscious awareness. How you learn something affects its retrieval. Learning without application can result in “inert knowledge,” making it inaccessible in real-life situations. Note the difference between recognizing an answer and generating one from memory.

Encourage learners to relate new information to their experiences, and offer practices that reflect how they’ll use the knowledge. Learners use two types of memory in learning: “declarative” (articulable knowledge, like rules) and “procedural” (performance-based knowledge, like riding a bike).

Remember also that people naturally process and remember information through stories. Different learning outcomes necessitate varied instructional approaches, involving both declarative and procedural information. Let the type of knowledge and the desired learning outcome guide your instruction and practice design.

Structure learning deliberately, using feedback, models and realistic context.

To enhance learning, effective memory retrieval needs feedback that confirms correctness or explains mistakes, allowing learners to adjust future approaches. Feedback reinforces the strength of neural connections on the basis of performance. Three types of feedback exist: “corrective, directive and epistemic.” They serve different functions, from simple correction to guiding self-discovery. Feedback should focus on performance – not personal attributes – and must be clear, precise and concise. It should avoid blame or praise, and concentrate on explaining outcomes. Typically, you should provide feedback after the learning task is complete. However, for complex tasks, immediate feedback can be beneficial.

“Feedback lets our brain know whether or not to strengthen the relationship. If we get it wrong, we don’t want to reward whatever choice we made.”

The human brain excels in pattern recognition and meaning-making, preferring narratives to rote or abstract information. Learners naturally create mental models to explain and predict outcomes, informing decision-making and adaptation. Utilizing these models when you design learning can enhance understanding and decision-making skills, especially in unpredictable situations. In learning, you can use misconceptions or commonly misunderstood concepts as opportunities to remedy incorrect assumptions through targeted feedback. This process contributes to rapid improvement. Moreover, learning proves more effective when learners retrieve information during concrete tasks rather than abstract ones. This means learning scenarios should be contextual and relatable to enhance recall and application.

Again, overloading working memory hinders learning. Psychologist John Sweller’s cognitive load theory suggests maintaining a balance between necessary and extraneous details. To avoid overwhelming learners, eliminate irrelevant details while retaining the context.

Best practices to reduce cognitive load include embedding labels into diagrams, rather than referring to separate legends – thus minimizing the need for mental integration. Also, frequent reprocessing of knowledge leads to automation, making some tasks nearly subconscious. This automation frees mental resources to focus on more complex tasks. Strive to automate routine tasks, either mentally or technologically, to focus on more important issues.

Effective learning design demands the use of spaced retrieval. Neuronal connections can only strengthen to a certain extent at one time, and they require rest, including sleep, for further strengthening. Cramming, for example, results in sharp memory decline after an exam. In contrast, spacing learning over time enhances retention. The complexity of the material, its criticality, and the frequency of use in performance dictate the amount of practice and spacing necessary. Skills that are crucial and complex, yet infrequently used, need ample practice. For example, pilots spend hours in simulators, preparing for situations they hope they won’t ever encounter. Include spaced practice in your learning design for effective retention.

Structural tools can also compensate for cognitive limitations. As physician and author Atul Gawande argues in The Checklist Manifesto, checklists can bolster various aspects of performance by externalizing necessary information. This acknowledges learners’ memory shortcomings and creates a scaffold for success.

Emotion and social connections play critical roles in learning.

In his book Thinking, Fast and Slow, Nobel laureate Daniel Kahneman points out that you have two cognitive systems. The first is a fast, intuitive one, which you rely on primarily for quick decisions. The other is a slow, more logical and conscious system, which proves tougher to engage because it takes effort. People often justify decisions that the first system makes by using the second, which can lead to mistakes. Your thinking may also suffer from the Dunning-Kruger effect, which implies that individuals with limited knowledge of a subject often overestimate their understanding. These human biases underscore the need for good design and careful instruction.

“Motivation, lack of anxiety, challenge, and more work together to create an experience that’s compelling.”

Emotion affects learning because it has an impact on memory. Highly salient or emotional events cause you to pay attention, which aids memory. Stories, simulations, humor, scenarios and other experiences all aid learning. Incentives to learn can also engage a person, such as giving learners points and recognition for their accomplishments. Better yet, causing learners to want to learn sparks their engagement through intrinsic motivation. Serious games – which challenge learners by asking them to solve problems in scenarios reminiscent of play or sport – can motivate deep learning. They spark interest and focus attention. Design challenges can stretch the learner’s abilities, but do not exceed them. Light and transient stress can sharpen the mind for learning, but deeper anxiety detracts from and harms learning.

As social creatures, learners share knowledge and practices without relying on the slow, species-wide evolution to improve. This extends to social learning, in which the collective value isn’t just in what you know individually, but in whom you know and learn from. While social learning can lead to enhanced understanding and outputs, it requires careful management. The overheads in social learning, such as scheduling, demand consideration.

Psychologist Albert Bandura’s Social Cognitive Theory emphasizes learning from others through observation and internalization. Social learning can occur via videos or by learners observing their peers, with the critique of other people’s performances helping to cultivate self-monitoring skills. Observing performances within a learner’s ability range can improve his or her performance. Observing performances beyond a person’s range may merely entertain – while watching the performance of tasks a person has already mastered may simply be a bore.

Encourage learners to practice and reflect on thinking and learning.

Learners need instruction in how to think about learning. In other words, they need to “learn how to learn.” Help students develop good learning and study habits. Encourage them to take the time to reflect on their learning. Help them become self-driven learners. Explain the difference between having a fixed mindset, in which you don’t believe you can learn new things, versus a growth mindset, in which you believe you can learn anything.

“You shouldn’t leave it to chance that your learners know the most effective ways to learn. Building in learning-to-learn is an opportunity to improve your learners beyond just the focus of a course.”

When you’re designing courses, take the learner’s perspective. Think about what learners should be able to do after the course, and why it should matter to them. Tell your learners what to expect from the course, what its relevance is, and what’s in it for them. Equip them with models and narratives that help them understand problems they might solve in the context of their work or activities. Use illustrations or videos where appropriate. Give learners a chance to practice what they’ve learned, in a realistic context – ideally, over the space of days, weeks and months. This gives them time to reflect.

Emphasize action and doing over passive learning.

Include as much time as possible for learners to do and experience, reflect, get feedback and repeat. Create these activities and experiences using either real-life situations your learners will encounter, or realistic ones you invent. Set clear goals for your learners that you tie to outcomes, and offer timely feedback.

As a learning professional, you should stay abreast of the latest learning science by reading articles, listening to expert podcasts and webinars, attending conferences and generally engaging with what’s new. Vet your research to consider credible sources that are applicable to your context. Don’t let the latest technologies drive your decisions. Let learning science guide you.

About the Author

Clark Quinn is a scholar in learning technology. He is the executive director of Quinnovation, an independent learning experience design strategy consultancy that helps organizations “work smarter.”