Educational Design Models

👧🏽 Free Guide PDF: Children-Centered Design (https://lnkd.in/dfj52fNe), a lovely free handbook on children-centered design approach in Save the Children Finland — with activities, games, design process and tips on working with children, respecting children’s needs, perspectives and rights. Kindly written by Reetta Kalliomeri, Katja Mettinen, Anna-Maija Ohlsson, Sonja Soini, Hanna Tulensalo, Inari Savola, shared by Maija Hansen. Children are rarely invited to take part in actual design efforts. But to grasp their needs and perspectives, we need to engage with their thinking and behavior. This is a very unique guide exploring how to involve children in co-designing digital products with them, not for them. --- 🔸 1. Key takeaways 1. Ask explicit consent first — participation must be voluntary, never assumed. 2. Trust in children’s ability to talk about and express themselves. 3. Safety first: child’s ability to choose what to talk about, how, how much, how quickly. 4. Children find it easier to understand explanations that are supported with pictures. 5. Draw or use pictures of what is going on and what you’d like to understand. 6. Leave a picture on a wall, so children can easily go back to it to ask for more details. 7. Physically get down to the child's level — sit on the floor, not across a desk. 8. Take notes openly; tell children what you're writing and why, let them comment. 9. Close every session by explaining what happens next with what they told you. 10. Take note of how specific children behave alone and with others. 11. Listen to how children speak, and the words they use. Don't “translate” it. 12. Use props instead: sun/ghost/tree drawings, puppets, hand-built models, picture collages. 13. Cap group interviews at 6 children per 2 adults, so everyone actually gets heard. 14. For ideas, let children draw, build, or act it out rather than describe it verbally. 15. When children can't be present, keep their exact words and photos visible. 16. Notice when children describe the same issue differently for themselves vs. parents. 17. Adults will underestimate what a child finds imaginative or worth building on. 18. Give children with disabilities extra support, so participation stays equal. As Deb Gelman wrote, children communicate volumes by how they play, what they choose to play with, how long they choose to play with it, and when they decide to play with something else. Yet they don’t get as disappointed when something isn’t working. They just choose to browse or play something else. Designing for children is difficult. Children tend to lose focus and motivation. They quit when they get bored, and they move on if they can’t get anywhere quickly enough. They need steady achievements. So as designers, we need to appreciate, reward, and encourage small wins to develop habits and support learning — with progress tracking and gamification. Useful resources and books in comments ↓

If you feel like you’re sprinting through the curriculum you’re not alone. 🏃♂️ But here’s the catch: Cognitive science says fast teaching doesn’t equal deep learning. Cognitive Load Theory (Sweller, 1988) reminds us that the brain’s working memory is limited. When we overload it, learning stalls no matter how great the content is. This isn’t just about students. It’s about teacher sustainability too. So many of us are under pressure to “cover everything.” But here’s the truth: Trying to do too much leads to shallow learning and teacher burnout. What works better? Teaching with the brain in mind: • Chunking content into manageable parts (Miller, 1956; 7±2 rule) • Using worked examples to reduce extraneous load (Sweller, 2006) • Providing pause time so students can consolidate and process • Eliminating distractions—less “busywork,” more focus • Building schemas through repetition, connection, and reflection • Focusing on one learning intention at a time As Willingham (2009) puts it: “Memory is the residue of thought.” We must give students time to think deeply not rush to the next thing. Slow learning is strong learning. Let’s ditch the overload and create space for what really matters: Clarity. Connection. Purpose. And yes - our own wellbeing too. #CognitiveLoadTheory #EvidenceBasedTeaching #TeacherWellbeing #DeepLearning #PrimaryTeaching #CurriculumDesign #BrainBasedLearning #EducationResearch #NeuroaffirmingPractice #LessIsMore

Shopping Malls Find New Life as College Campuses CLEVELAND — Where teenagers once congregated around food courts and shoppers browsed department store racks, students now hurry to lectures, study in converted retail spaces, and even live in former anchor stores. Across America, developers and educational institutions are reimagining struggling shopping centers as college campuses and student housing, creating an unexpected second act for these fading temples of consumerism. These spaces were built for crowds, The infrastructure is already perfectly suited for educational purposes—wide corridors, multiple entrances, food service capabilities, and acres of parking. The transformation makes financial sense. Construction costs for new university buildings have soared past $500 per square foot in many regions, while renovating existing mall structures can cost 30 to 40 percent less, according to the American Association of College Facilities Officers. At the former Eastgate Mall outside Cincinnati, classrooms now occupy what was once a Sears. Students study in a library housed in an old JCPenney, while the food court serves as a student union with healthier dining options than its previous incarnation. "We're addressing two problems simultaneously," said Cincinnati Mayor Aftab Karma Singh Pureval. "We're preventing urban blight while expanding educational access in communities that desperately need it." The trend is spreading nationwide. The University of Arizona established a campus at The Bridges, a converted Tucson mall complex. Northern Virginia Community College transformed a vacant Macy's into a medical training center complete with simulation labs. For students, the benefits extend beyond novelty. Mall-campuses tend to be more accessible by public transportation than traditional universities, serving commuter students and those from lower-income backgrounds who cannot afford to live on campus. Some developers are even converting upper floors and outparcels into affordable student housing, addressing another critical need in higher education. Educational leaders see these conversions as more than stopgap solutions. The approach fights urban blight while providing local educational opportunities that don't require students to leave their communities. "Instead of one massive central campus, universities can create satellite locations where students already live and work." With retail analysts predicting thousands more mall closures in the coming decade, and higher education facing infrastructure challenges, these conversions represent an elegant solution to multiple problems. What was once a sign of economic decline may become the classroom of tomorrow.

The Secret Language of Level designers?? What if I told you that Level designers have a secret language we use to communicate with players? Ever wonder how you always know where to go, even without a map? Maybe you felt tension without a single word of dialogue. That’s not luck. That’s the hidden language of level design at work. As level designers, our job goes far beyond just building spaces. We craft experiences. And we do it through a subtle, intentional language made up of light, shape, rhythm, and space. Most players don’t consciously see level design, but they feel it. Every hallway, staircase, shadow, and prop is carefully placed to guide behavior, evoke emotion, and support the narrative. We don’t issue commands. We suggest, nudge, and invite. Here are just a few of the techniques we use to communicate through the world itself: Landmarking – Large structures, unique shapes, or color contrast that help players orient themselves and build mental maps. Lighting Cues – Warm, soft lighting signals safety or narrative importance. Harsh or dark areas introduce tension and uncertainty. Framing – Using geometry to subtly direct the player’s eye toward points of interest, similar to how cinematographers guide attention in film. Breadcrumbing – Placing pickups, enemies, or environmental details in patterns that subconsciously guide players toward their goal. Affordance – Designing elements to suggest their function: a waist-high ledge invites traversal, while a flickering exit sign implies urgency. Echoing – Repeating familiar layouts or motifs (like U-shaped corridors or blocked paths) to build rhythm, recognition, or suspense. Forced Perspective – Aligning objects and environmental elements to lead the eye, encouraging movement or curiosity. Even the smallest details — the tilt of a camera, the curve of a hallway, the placement of clutter — all contribute to an unspoken conversation with the player. We’re not just designing gameplay. We’re shaping emotion, behavior, and storytelling — all through space. and remember if you are interested in learning about these techniques and more the next cohort of Game Design Skills level design course taught by Nathan Kellman and yours truly will be starting up in a few weeks so if you are interested nows the time to reach out!!! #LevelDesign #GameDesign #NarrativeDesign #EnvironmentArt #UXDesign #GameDev #SpatialDesign #PlayerExperience

Silos in mining education crated silos in mining operations – costing the industry billions.    Geologists define resources.   Mining engineers extract them.   Metallurgists process them.    Yet, they rarely communicate well with each other. In countries like Canada, the US, and Australia, mining education is structured along rigid disciplinary lines. Geologists spend years studying ore deposit formation, geochemistry/mineralogy and geostatistics, but rarely learn how their models affect downstream processing. Mining engineers focus on extraction and haulage techniques, but often overlook how blasting impacts processing. While metallurgists optimize processing circuits without considering how geological variability influences plant performance. By the time they enter the workforce, they already think in silos. These divisions date back to the early 20th century when mining education mirrored industry workflows: geologists found the ore, engineers mined it, and metallurgists processed it—each in isolation. By the 1980s, accreditation bodies reinforced these divisions. Committees of retired industry professionals resisted changes that might “dilute” technical expertise. Curricula stagnated while the industry evolved. Mining has changed drastically since the 90s. Yet, universities still train professionals in isolation. What does this look like in practice? Modern geology programs analyze ore deposits in extreme detail but rarely consider how models influence mine planning or processing. As John Steen of UBC’s Bradshaw Research Initiative put it: “Engineers don’t design for the orebody because they’ve never been taught to integrate geological variability into their models.” The result? Mines are designed based on optimistic ore interpretations, often overlooking real variability. When the orebody is more complex than expected (which it almost always is), engineers and metallurgists scramble to compensate. Mining engineers, trained to maximize extraction efficiency, prioritize high-tonnage bulk mining without considering processing impacts. And metallurgists? They inherit these problems without having been involved in the decisions that caused them. Processing programs emphasize extraction techniques but often overlook the geological origins of ore variability. Graduates excel at mass balancing and circuit design but struggle to optimize processes for fluctuating feed grades or mineralogical complexities. These silos create a culture of risk avoidance. It doesn’t have to be this way. In Brazil, where I studied (Escola Politécnica da USP), mining education is different. Instead of separate geology, mining, and processing degrees, we study Mine Engineering—a multi-disciplinary program integrating exploration, extraction, processing, and tailings management. And the difference is clear. Thinking across disciplines prevents problems from being designed into operations before they even start.

Why do some teams thrive under pressure while others collapse? It often comes down to two hidden forces: The level of psychological safety people feel. The performance standards they’re held to. Not just one of the two. Both. Amy Edmondson’s framework shows how these forces interact, creating four very different team dynamics: Apathy Zone (low safety, low standards):  People disengage. They show up, but their minds are elsewhere. Minimal energy, minimal outcomes. Comfort Zone (high safety, low standards):  People are relaxed and friendly, but without challenge. It feels nice—but progress stalls. Anxiety Zone (low safety, high standards):  Pressure is high, but fear dominates. People play it safe, withhold ideas, and avoid risks. Performance suffers despite effort. Learning Zone (high safety, high standards):  This is the sweet spot. People feel safe enough to speak up, experiment, and fail, while being stretched to achieve ambitious goals. This is where true innovation and growth happen. Here’s the key insight: Psychological safety alone is not enough. A comfortable team without high standards doesn’t move forward. But also: Performance standards alone are not enough. High standards without safety create fear. Strong leaders cultivate both: they build an environment of trust and respect, and set the bar high enough to push people to their potential. The best teams don’t just feel safe. They feel safe and challenged to do hard things. Which quadrant are you or your people in today?

Your code works on your laptop. Congrats. 🎉 Happy? Now make it work for 100 million users. That's where most of the data engineers need to emphasize on System Design. 🎯 Why System Design Actually Matters The brutal truth: →Your SQL query might be perfect →Your pipeline might be beautiful But can it handle Black Friday traffic? Database failures? Regional outages? System Design = Building solutions that don't collapse under real-world chaos. 📚 Here's the Learning Roadmap you can follow(No Fluff) - 🟢 FOUNDATION LEVEL Master these first, or everything else falls apart: Core Infrastructure: → Load Balancers (distribute traffic before it breaks you) → API Gateways (your system's front door) → CDNs (stop making users in Tokyo wait 3 seconds for data from Virginia) Data Fundamentals: → ACID vs BASE properties → SQL vs NoSQL (and when each will save/ruin your day) → Indexing strategies (the difference between 10ms and 10s queries) 🟡 INTERMEDIATE LEVEL Now you're building for scale: Performance Patterns: → Caching Layers (Redis, Memcached) - because hitting the DB every time is a crime → Database Sharding - when one database isn't enough anymore → Read Replicas & Replication Patterns - spread the load, reduce the pain Reliability Building Blocks: → Rate Limiting & Throttling → Circuit Breakers (fail fast, recover faster) → Retry Mechanisms with Exponential Backoff 🔴 ADVANCED LEVEL Welcome to distributed systems nightmares: The Hard Stuff: → CAP Theorem (you can't have it all, so choose wisely) → Consensus Algorithms (Raft, Paxos) - how distributed systems agree on reality → Event Sourcing & CQRS patterns → Distributed Transactions & Saga Pattern Data Engineering Specifics: Stream Processing Architecture (Kafka, Flink, Spark Streaming) → Lambda vs Kappa Architecture → Data Lake vs Data Warehouse design → ETL/ELT Orchestration at scale Preparing for data engineering system design interviews? DO THIS: ✅ Think out loud (interviewers want to see your thought process) ✅ Ask clarifying questions (shows you don't make assumptions) ✅ Discuss trade-offs (every decision has pros/cons) ✅ Draw diagrams (visual communication matters) ✅ Mention monitoring & observability (production-ready thinking) ✅ Consider failure scenarios (what happens when X goes down?) Stick to these Impactful Habits to grow - →Don’t focus only on tools—master principles, system thinking, and communication with non-data teams. →Pursue hands-on learning (projects, peer reviews, learning from production mishaps). →Treat AI and new tech as force multipliers, not adversaries—learn to steer, not just ride. Here's amazing System Design Blueprint crafted by Alex Xu!! Start simple. Learn incrementally. Practice real problems. What's the most complex system you've designed or broken in production? Share your challenging stories below 👇

Good guidance from the U.S. Department of Education to developers of education technology; focus on shared responsibility, managing risks, and bias mitigation. 🛡️ One think I really like about this document is the use-case specific guidance and examples (clearly there were industry contributors that helped facilitate that). 🎓 Key Guidance for Developers of AI in Education -------------------------------------------------- 🔍 Build Trust: Collaborate with educators, students, and stakeholders to ensure fairness, transparency, and privacy in AI systems. 🛡️ Manage Risks: Identify and mitigate risks like algorithmic bias, data privacy issues, and potential harm to underserved communities. 📊 Show Evidence: Use evidence-based practices to prove your system's impact, including testing for equitable outcomes across diverse groups. ⚖️ Advance Equity: Address discrimination risks, ensure accessibility, and comply with civil rights laws. 🔒 Ensure Safety: Protect data, prevent harmful content, and uphold civil liberties. 💡 Promote Transparency: Communicate clearly about how AI works, its limitations, and its risks. 🤝 Embed Ethics: Incorporate human-centered design and accountability throughout development, ensuring educators and students are part of the process. BABL AI has done a lot of work in the edtech space, and I can see an opportunity for us to provide assurance that some of these guidelines are being followed by companies. #edtech #AIinEducation #aiassurance Khoa Lam, Jeffery Recker, Bryan Ilg, Jovana Davidovic, Ali Hasan, Borhane Blili-Hamelin, PhD, Navrina Singh, GoGuardian, Khan Academy, TeachFX, EDSAFE AI Alliance, Patrick Sullivan

The Silent Curriculum: What School Architecture Teaches Without Words Not all lessons come from textbooks. Some are absorbed through walls, corridors, and ceiling heights. Through the presence......or absence.....of light, nature, and places to pause and breathe. The layout of a school silently communicates what matters. Do students walk long, narrow hallways to identical classrooms with rows of desks? Or do they enter vibrant, varied and dynamic environments that invite movement, collaboration, and curiosity? In many traditional school buildings, the design sends an unspoken message: "Sit down. Follow instructions. Stay within the lines.” But imagine what students might believe about themselves and the world if their learning environment said instead: "You belong here. Your ideas matter. Explore freely.” Architecture is not neutral. It is a hidden teacher—often more powerful than the lesson plan. It shapes how students feel, how they interact, and ultimately, how they see themselves as learners.

As we continue to work with schools and districts, we are being asked more and more about the best way to identify GenAI EdTech tools to pilot. Based on our experience as EdTech builders in the past, we created this guide to anchor conversations with tool providers (newly revised and re-designed). Here's what we suggest: Human Oversight and Quality Control Our users need to trust AI-generated content from your platform. What human oversight and quality control measures do you employ? Are there user warnings about accuracy of outputs? How do you ensure that feedback from users is being collected and actioned? Mitigating Bias in Outputs It’s important that the tools we use do not cause harm to our students or teachers. What steps are you taking to identify and mitigate biases in the underlying GenAI models your product uses? How will you ensure fair and unbiased outputs? Student Privacy and Ethical Data Use Protecting student data privacy and ensuring ethical use of data is our priority. What third parties have access to our data (e.g., OpenAI, Google)? Is our data used to train any internal or external GenAI models? What policies and safeguards can you share to address privacy concerns? Evidence of Impact We need evidence that your AI tool will improve learning outcomes for our student population and/or effectively support our teachers. Can you provide examples, metrics and/or case studies of positive impact in similar settings? Accessibility and Inclusive Design Our school needs to accommodate diverse learners and varying technical skills among staff. How does your tool ensure accessibility and usability for all our students and staff? What ongoing support and training is available? Link in the comments to save or download the PDF version! AI for Education #GENAI #edTech #responsibleAI