Instructional Design Basics

💡 Are You a "Top Trainer" or Just a Trade Expert? I see incredible tradespeople being instantly labeled "top trainers" in the vocational sector. We celebrate their industry expertise, but often skip a crucial step: understanding how humans actually learn. My personal journey began back in 1997, when I started spending my own money - ultimately over £20,000 - to study educational psychology and instructional design. I became a dual professional, studying everyone from foundational theorists such as Piaget and Vygotsky to experts on multimedia learning like Richard E. Mayer. This investment taught me that even state-of-the-art simulated environments are only part of the solution. As David Hargreaves argued in 1996, we must adopt evidence-based practice - respecting both trade science and learning science. 🧠 Stage 1: Design Smartly (Mayer's Tips) You don't need to spend £20k to improve, just apply a few research-backed principles. Since almost everyone uses slides, make your PowerPoints and e-learning effective using principles from Mayer's Cognitive Theory of Multimedia Learning (CTML), which reduces cognitive load: 1. Stop Reading Your Slides (Redundancy Principle): Use images and graphics while you speak. Slides should complementyour speech, not duplicate it. 2. Cut the Clutter (Coherence Principle): Remove all decorative elements or text not essential to the core goal. If it doesn't support learning, delete it. 3. Put Graphics and Text Together (Contiguity Principle): Place labels, arrows, and key definitions immediately next to the relevant graphic. 📉 Stage 2: The Retention Crisis (Ebbinghaus's Reality) Even with perfectly designed slides, training often fails because we ignore the most fundamental reality of memory, researched over a century ago by Hermann Ebbinghaus (1885). Ebbinghaus's Forgetting Curve shows that unless knowledge is actively used or reviewed (as later explored by Bartlett), it dissipates dramatically within days. The problem with many courses is that students leave with a certificate but never engage in post-course practice. The knowledge is lost. The hallmark of a great engineer is continuous application and engagement with peers. Trainers must encourage all learners - including the 9,000 people tax payers have paid for to be lifelong learners by encouraging them to continually apply that knowledge. Being a true "top trainer" means respecting the learner's brain across the entire learning lifecycle. #EvidenceBasedEducation #VocationalTraining #InstructionalDesign #ForgettingCurve #LifelongLearning Charlotte Lee Alex Butcher Katy King Matt Isherwood Andrew Johnson Tom Arey John Hancock Madeleine Gabriel BPEC LCL Awards Dr Matthew Aylott Rhiannon de Wreede SNIPEF

High-quality, consistent annotations are essential for building robust machine learning (ML) models. However, conventional methods for training ML classifiers often require domain experts to annotate data, which is then passed to data scientists for model training, review, and iteration. This process can be resource-intensive and time-consuming. In a recent blog, Netflix's machine learning engineers share how they’ve developed a system to address these challenges. The business needed to create granular video understanding for various downstream applications, which required building ML classifiers capable of identifying visuals, concepts, and events within video segments. Their solution involves a three-step process to build these classifiers systematically. First, users (i.e. video experts) search for an initial set of examples from a large, diverse corpus to kickstart the annotation process. This is done through text-to-video search, powered by video and text encoders from a Vision-Language Model that extracts embeddings. Next, an active learning loop is used to build a lightweight binary classifier based on these embeddings. This classifier scores all video clips in the corpus and presents select examples to the user for further annotation and refinement. Finally, users review the fully annotated clips. This step helps spot annotation mistakes and discover new concepts, prompting users to revisit earlier stages for refinement when needed. This self-service architecture empowers video experts to continuously improve without relying on data scientists or third-party annotators. It has also demonstrated improved average precision over competitive baselines. With its multiple benefits, this system serves as a valuable reference. #machinelearning #datascience #activelearning #video #embedding #annotation – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gU_9hFfN

In my former life, I was a graphic designer. I spent years obsessing over layouts, grids, color palettes, and the tiny details that make a design feel right. When I moved into learning design, I realized those same skills gave me an edge. The PARC principles I had been using for years—Proximity, Alignment, Repetition, and Contrast—translated perfectly into creating clearer, more engaging learning experiences. Proximity Group related content so learners instantly understand what belongs together. Alignment Position elements with purpose. Consistency in placement makes content easier to follow and trust. Repetition Repeat visual cues like colors, fonts, and layouts. Predictability helps learners focus on the message instead of figuring out the interface. Contrast Highlight what matters most. Use size, color, and whitespace to create a clear visual hierarchy. This simple system works in both worlds—graphic design and learning design—because it’s all about reducing friction, improving clarity, and guiding attention. What principles have you borrowed from another field that’s improved the way you create learning experiences?

🦴✨ Anatomy Beyond Textbooks – When Science Meets Creativity! ✨🦴 Have you ever tried learning the human skeletal system in a way that feels less like rote memorization and more like an art form? 🎨📚 This visual is a perfect example – the entire human skeleton designed using the names of bones themselves. From the cranium to the phalanges, every part is written in place, making it not just informative but also unforgettable. For students in pharmacy, medicine, physiotherapy, nursing, and life sciences, remembering over 200 bones can feel overwhelming. But when knowledge is presented through creative visuals, it becomes engaging, interactive, and easier to retain. 👉 This shows us that learning doesn’t have to be boring – it can be innovative, fun, and artistic. Sometimes a new perspective is all we need to spark interest and deepen understanding. 💡 Whether you’re a healthcare student or educator, try blending creativity with academics. It can transform the way we look at science. #Anatomy #SkeletonSystem #MedicalEducation #PharmacyStudents #InnovationInLearning #HealthcareEducation #CreativityInScience #LearningMadeFun

Most instructional designers skip the hardest part. Not the tools. Not the authoring platform. Not even the storyboard. 👇 Understanding the content deeply enough to explain it simply. That's where learning breaks down long before the first slide is built. The Feynman Method was designed for learners. I adapted it for course designers. Here's how it works, and where AI fits in at every step: Step 1 — Map the expert's knowledge Interview your SME. Record it. Sort what you hear into facts, processes, and judgements. → AI can transcribe, cluster, and surface patterns you might miss. Step 2 — The content readiness test Explain the core concept out loud. No slides. No notes. If you can't do it clearly — the content isn't ready for design. → AI can be your first "explain it to me" audience. Ask it to challenge your explanation. Step 3 — The gap audit Every place your explanation broke down = a learning gap = a module. → AI can help you map gaps, suggest missing links, and flag assumptions. Step 4 — The anchor metaphor One strong analogy gives learners something to return to when they get lost. → AI can generate 10 metaphor options in 30 seconds. You pick the one that actually fits. But here's the thing about AI in this process: It can help you simplify, organize, and iterate faster. It cannot do the understanding for you. After all the prompts and iterations, it's still you who needs to be able to explain it clearly to another human. That's the test. That's the standard. 💬 What do you think — does an instructional designer need to truly master a topic before designing a course around it? Or is it enough to structure what the SME provides? #InstructionalDesign #LearningDesign #FeynmanMethod #LXD #ElearningDevelopment #AIinLearning #CourseDesign #LearningAndDevelopment

𝘓𝘦𝘵’𝘴 𝘣𝘦 𝘳𝘦𝘢𝘭: Instructional Design is evolving—fast. AI isn’t just a tool anymore. It’s a collaborator. If you're still designing static courses in Storyline or obsessing over ADDIE without integrating AI, you're stuck in the old L&D model. That model is 𝘥𝘦𝘢𝘥. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗳𝘂𝘁𝘂𝗿𝗲-𝗽𝗿𝗼𝗼𝗳 𝘆𝗼𝘂𝗿 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗮 𝗛𝘂𝗺𝗮𝗻-𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: 1️⃣ 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗩𝗮𝗹𝘂𝗲 Stop thinking like a content creator. Start thinking like a 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦𝘳. Ask: “How can I use AI to close performance gaps in real time?” 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗗𝗲𝗲𝗽𝗹𝘆) Don't just “play” with ChatGPT, Copilot, Gemini, and Claude. Master how to: ▪️Structure prompts ▪️Chain prompts ▪️Design AI workflows ▪️Generate data-driven learning assets in seconds 3️⃣ 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝗶𝗻 𝗣𝘂𝗯𝗹𝗶𝗰 Share what you’re building. Post your AI-powered learning experiences on LinkedIn. Turn your process into 𝘱𝘳𝘰𝘰𝘧 of skill. 4️⃣ 𝗥𝗲𝗽𝗹𝗮𝗰𝗲 “𝗖𝗼𝘂𝗿𝘀𝗲𝘀” 𝘄𝗶𝘁𝗵 “𝗦𝘆𝘀𝘁𝗲𝗺𝘀” Employees don’t need more content. They need performance systems: ▪️AI copilots ▪️Embedded nudges ▪️Just-in-time guidance You design the systems. AI delivers the scale. 5️⃣ 𝗔𝘂𝗱𝗶𝘁 𝗘𝘃𝗲𝗿𝘆 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗳𝗼𝗿 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁 Go task-by-task through your ID process. Ask: “𝘊𝘢𝘯 𝘢 𝘮𝘰𝘥𝘦𝘭 𝘥𝘰 𝘵𝘩𝘪𝘴 𝘧𝘢𝘴𝘵𝘦𝘳, 𝘣𝘦𝘵𝘵𝘦𝘳, 𝘰𝘳 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴𝘭𝘺?” If yes—build the automation. You’re not just an Instructional Designer anymore. You’re the architect of 𝗛𝘂𝗺𝗮𝗻-𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. Make the leap. Or risk being automated out of the equation. What part of your current workflow do you think AI could take over tomorrow? Drop it below. Let’s dissect it together.

“My lessons are completely student-centred,” we say but... in class we talk non-stop for 35 minutes, while students quietly copy notes from the board. The intention is there but the practice? Not quite. So what actually makes a lesson centred around the learner? 📍 It’s not just about group work or giving students a say. 📍 It’s about how we design learning, what we prioritise and how much ownership students truly have. One powerful way to bridge this gap is by returning to the principles of instruction—based on solid research into what really works in classrooms: 📍 Begin lessons with a short review of prior learning 📍 Present new material in small, manageable steps 📍 Ask questions to check for understanding throughout 📍 Provide models and worked examples 📍 Guide student practice with scaffolds before releasing independence 📍 Engage students in frequent, successful retrieval practice 📍 Provide immediate, clear feedback 📍 Ensure a high success rate before moving on 📍 Monitor independent practice and support where needed 📍 Revisit material to strengthen retention These are not rigid rules but they remind us that effective teaching is intentional, layered and focused on how students learn, not just how teachers teach. True student-centred learning isn’t about stepping back completely. It’s about designing instruction that empowers students without leaving them to figure it all out alone. 📍 What principle do you lean on most in your teaching? #ZippysClassroom #MakeTeachingGreat #PrinciplesOfInstruction #StudentCentredLearning #TeacherReflection #EducationMatters #EffectiveTeaching

The Invisible Work Behind Every Effective Learning Design In my line of work in instructional design, often people see the final learning experience and assume it comes together after a meeting or two and a bit of content sharing. From the outside, a strong learning experience can look almost effortless. But as an instructional designer, I know that the work behind an effective course is much more than it appears. What looks “effortless” or seamless on the surface is usually the result of a lot of invisible work behind the scenes: clarifying goals, challenging assumptions, translating subject expertise into learnable moments, designing for engagement, building practice opportunities, revising, testing, and refining. It is rarely just about collecting text and turning it into slides or modules. It is about shaping an experience that actually helps people learn, remember, and apply. So, one meeting with the SME and a folder of content will not result in a polished course a week later. What actually happens is that we start by clarifying the real learning need, not just the topic. We ask: What should learners be able to do differently? What do they already know? Where are the likely gaps, misconceptions, and friction points? Then we shape the experience around the answers. That means deciding what content matters most, what can be left out, where learners need practice, where they need reflection, and how to make the experience feel intuitive rather than overwhelming. Sometimes that means rewriting dense content into plain language. Sometimes it means turning a long presentation into scenarios, decision points, job aids, or guided practice. Sometimes it means pushing back gently so the final product serves the learner, not just the content. As an educational developer, I also think a lot about what it takes to lead that work well. Leading a team of instructional designers is not just about tracking timelines. It is about setting the tone for thoughtful collaboration, creating space for iteration, helping the team ask better questions, and making sure quality does not get sacrificed for speed. The best learning experiences usually come from trust, iteration, and a shared commitment to the learner. They rarely happen in one or two meetings. They take partnership, expertise, and a lot of behind-the-scenes refining. What does “effortless” learning design look like from your side of the work? #InstructionalDesign #LearningDesign #EducationalDevelopment #LearningExperienceDesign #CourseDesign #HigherEd #SMECollaboration #LearningAndDevelopment #TrainingDesign #InstructionalDesigner #LearningExperience #ProfessionalDevelopment #ReflectivePractice

🎨 Why Structure Supports Learning Visuals aren’t shortcuts — they’re bridges. For many neurodiverse learners, visuals transform confusion into comprehension and anxiety into clarity. Practical Supports: 🔹 Use visuals to make invisible steps visible — it builds independence. 🔹 Pair images with simple text; it supports processing speed. 🔹 Visuals reduce verbal overload and frustration during transitions. 🔹 They help learners generalize across settings — school, home, community. 🔹 Visual supports empower—not replace—language and self-expression. Structure doesn’t limit creativity; it makes it possible. — Marc L. Esposito, LMSW 🌐 https://lnkd.in/em_gkhTf | 📩 Guide2Empower345@gmail.com | IG: @unlockingpotential1  #VisualSupports #SpecialEducation #Neurodiversity #ABA #TeachingStrategies #SpeechTherapy #InclusiveEducation

🎓 Why I Stopped Designing Around “Learning Styles” This might surprise some people in L&D, but I used to be a big believer in learning styles. You know the idea — some people learn best by seeing, others by hearing, others by doing. It felt intuitive. It made sense. And it became a staple in how we thought about training design. But here’s the kicker: the science doesn’t back it up. Researchers have found no solid evidence that matching learning delivery to someone’s preferred “style” actually improves learning. What does matter is matching the method to the content — for example, using visuals for geometry, or discussion for leadership development. So, if learning styles aren’t the magic formula, what really makes a difference? Here’s what I’ve learned (and seen work time and time again): 💡 Structure building – helping learners connect the dots and see how new information fits into the bigger picture. 🧩 Rule learning – teaching people how to apply principles, not just memorize examples. 🚀 Active learning – using retrieval practice, spacing, and reflection so learning actually sticks. 🧠 Dynamic testing – focusing less on “what do I know now?” and more on “what can I get better at next?” It’s freeing, actually. We don’t need to label people. We need to design learning that stretches everyone — visual, verbal, hands-on, or otherwise. Real learning isn’t about preference. It’s about progress. What about you? Have you noticed a shift away from learning styles in your organization? #LearningAndDevelopment #LearningScience #InstructionalDesign #GrowthMindset