Education

👧🏽 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 ↓

We keep being told that more screens improve learning. The evidence is far less convincing. What stands out to me in this debate is the gap between what parents are told and what the research actually suggests. During a recent Senate hearing, brain expert Jared Cooney Horvath made a simple point: Children do not learn better just because a screen is involved. That matters. Because “digital” has too often been treated as if it automatically means “effective.” More devices. More platforms. More screen-based instruction. But children still learn the way they always have: through movement, conversation, hands-on experience, curiosity, and real engagement with the world around them. That is why many families are stepping back and rethinking education. They are choosing: → hands-on learning → real books → open conversation → curiosity-led exploration → the freedom to learn at a natural pace To me, this is the real issue. The question is not whether technology belongs in education. It does. The question is whether we are confusing constant digital exposure with meaningful learning. Because more screen time is not the same as deeper understanding. Are we improving education, or just making it more digital and calling that progress? #Education #Learning #Parenting #Homeschooling #EdTech #FutureOfLearning #ChildDevelopment #Teaching #Schools

If you're feeling overwhelmed with how fast AI is evolving, you're not alone. Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind. But the good news is that you don’t need to learn everything all at once. What you need is structure. So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable. 💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition. If you're moving faster or slower based on time or experience, that’s okay too. And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here. 👇 Here’s the roadmap: 🔖 Level 1: GenAI & Transformer Foundations Tokens, embeddings, transformers, decoding, and inference with open-weight models. 🔖 Level 2: Prompting & Language Model Behavior Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting. 🔖 Level 3: Retrieval-Augmented Generation (RAG) Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation. 🔖 Level 4: LLMOps & Tools LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data. 🔖 Level 5: Agents & Agent Frameworks Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation. 🔖 Level 6: Memory, State & Orchestration Vector and symbolic memory, episodic vs persistent state, memory compression. 🔖 Level 7: Multi-Agent Systems Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams. 🔖 Level 8: Evaluation & Reinforcement Learning LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops. 🔖 Level 9: Protocols & Safety MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates. 🔖 Level 10: Build & Deploy FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens. 📌 Bookmark this. 🛠️ Build something after every level. And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 💻 Pro-tip: Start with cookbooks! 〰️〰️〰️ Follow me (Aishwarya Srinivasan for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

I taught myself machine learning > 10 years ago. If I had to start again today, I wouldn’t touch models, LLMs, or agents first, as many AI experts suggest. I'd start with the math and the code. Ugly truth: 90% of people skip the foundations, then wonder why everything feels like magic or falls apart in production. If you want to be different, actually understand ML, not just copy-paste, this is the roadmap I'd follow: Start with fundamentals: Because no matter how fast LLMs or GenAI evolve, your math, code, and logic will keep you relevant. Here's what you should focus on: 📐 1. Linear Algebra Learn these core ideas: Vectors, matrices, tensors Matrix multiplication (dot products, broadcasting) Transpose, inverse, rank, determinants Eigenvalues & eigenvectors (especially for PCA & embeddings) Projections and orthogonality ✅ Use NumPy to implement everything yourself → Practice matrix ops, dot products, and visualizing transformations with Matplotlib 🔁 2. Calculus Focus on: Derivatives & partial derivatives Chain rule (for backpropagation in neural nets) Gradient descent Convex functions, minima/maxima ✅ Use SymPy or JAX to visualize and compute derivatives → Plot functions and their gradients to develop deep intuition 🎲 3. Probability You need a solid grip on: Random variables (discrete & continuous) Conditional probability & Bayes' rule Joint & marginal probability The Chain rule Expectation, variance, entropy Common distributions: Bernoulli, Binomial, Gaussian, Poisson Central limit theorem The law of large numbers ✅ Simulate simple probability experiments in Python with NumPy → E.g. simulate sampling from distributions 📊 4. Statistics These are must-know topics: Descriptive stats: mean, median, mode, standard deviation Hypothesis testing: p-values, confidence intervals, t-tests Correlation vs. causation Sampling, bias, and variance Overfitting/underfitting A/B testing basics ✅ Use Pandas & SciPy to explore real datasets → Calculate descriptive stats, create histograms/box plots, run t-tests 🔧 Essential Python libraries to learn early NumPy – for vectorized math and fast array ops Pandas – for loading, cleaning, and analyzing tabular data Matplotlib / Seaborn – for plotting and visualizing distributions, relationships, and trends SymPy – for symbolic math and calculus SciPy – for stats, optimization, and numerical methods Use Jupyter Notebooks(to combine math, code, & visuals in one place) 📚 Best resources to nail the fundamentals: ✅ Machine Learning Foundations Math series (ML Foundations: Linear Algebra, Calculus, Probability, and Statistics)-series of 4 courses that I've created together with LinkedIn learning ✅ Hands-On ML with TensorFlow & Keras book by Aurélien Géron ✅ The Hundred-page Machine Learning Book by Andriy Burkov If you want to become an actual ML engineer, not just someone who watches and copies demos, start here. ♻️ Repost to help others💚

When Ozan Varol first became a law professor, he’d pause mid-lecture and ask: “Does anyone have any questions?” -Crickets- He assumed he nailed the lesson. But the exam results said otherwise. So he ran an experiment. Instead of asking if anyone had questions, he said: “That was confusing. I’m sure a bunch of you have questions. Now’s the time to ask.” Suddenly—hands shot up. Why did that work? Because it did 3 powerful things: 1. Normalized confusion 2. Created psychological safety 3. Made it feel okay to not “get it” Students weren’t silent because they understood… They were silent because they were scared to speak. This isn’t just for professors. Doctors can say: “I know I used a lot of medical jargon—what questions do you have?” Leaders can say: “That was a tough quarter. I know we’re all facing challenges—what’s come up for you?” People don’t speak up because they don’t want to look weak. Not in front of peers. Not in front of bosses. Not in front of future collaborators. Your job isn’t just to ask questions. It’s to create the conditions for honest answers. The goal isn’t “Who has questions?” The goal is: “How can I make asking questions feel safe, smart, and expected?” Final takeaway from Ozan: Breakthroughs don’t start with smart answers. They start with better questions. Asked the right way. At the right time. To the right people. What’s one question you’ve been asking the wrong way? Reply below—let’s rewrite it together. 👇

Hey Europe, you want a vibrant tech industry, right? The US seems set on destroying its public research funding system. Many US-based scientists are looking for a Plan B. You may have an opportunity to attract some of the best scientists in the world. Scientists will go where they have the means to be the most creative and productive. Here are the criteria that attract them: 1- access to top students and junior collaborators. 2- access to research funding with little administrative overhead. 3- good compensation (comparable with top universities in the US, Switzerland, Canada). 4- freedom to do research on what they think is most promising. 5- access to research facilities (e.g. computing infrastructure, etc). 6- ability to collaborate/consult with industry and startups. 7- moderate teaching and administrative duties. They will seek the best trade-off between these criteria in academia, public research, or industry. European academia rates high on 1 and 4, low on 2 (even if you can get an ERC grant), 5, 6 and 7, and *very* low on 3. European industry rates low on almost every criterion, particularly on 4, but also on 3 and 5 compared to top US industry labs. To attract the best scientific and technological talents, make science and technology research professions attractive. It's pretty straightforward.

The Supreme Court's decision to end affirmative action is already reshaping the landscape of higher education — and not for the better. This year, Harvard Law School enrolled just 19 Black first-year students, representing 3.4% of the incoming class, compared to 43 Black students in last year’s class. These numbers are alarming and underscore the ripple effects this ruling will have on students from underrepresented communities. When diversity diminishes, so does opportunity — for students, institutions and society. Black students bring unique perspectives and experiences that enrich the academic environment and prepare future leaders to navigate a diverse world. Without deliberate efforts to ensure access and equality, we risk widening existing gaps in education, leadership and economic mobility. Some schools are taking bold steps, like making tuition free for students from households below a certain income level. These initiatives are critical for leveling the playing field, particularly for those who have faced historical economic injustices. However, they must be part of a broader commitment to support underrepresented students at every stage of their educational journey. Education has always been a cornerstone of my work. Through organizations like Student Freedom Initiative (SFI), I’m proud to help expand access and reduce barriers for students from underrepresented communities. But we cannot do it alone. It will take a collective effort to ensure that every student, regardless of background, has the chance to thrive. Let’s challenge ourselves to build an educational system that reflects the values we aspire to: equality, inclusion and excellence.

If you’re a 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿, 𝗰𝗹𝗼𝘂𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗗𝗲𝘃𝗢𝗽𝘀 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁, 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿, or just starting your tech journey, 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗟𝗶𝗻𝘂𝘅 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀.  Here’s why Linux dominates the tech world:  ✅ 𝟵𝟳% of the top 1 million web servers run on Linux   ✅ Powers 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱’𝘀 𝘁𝗼𝗽 𝟱𝟬𝟬 𝘀𝘂𝗽𝗲𝗿𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀   ✅ Forms the backbone of 𝗮𝗹𝗹 𝗺𝗮𝗷𝗼𝗿 𝗰𝗹𝗼𝘂𝗱 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀   ✅ Android (billions of devices) runs on the 𝗟𝗶𝗻𝘂𝘅 𝗸𝗲𝗿𝗻𝗲𝗹   ✅ Most 𝗗𝗲𝘃𝗢𝗽𝘀 & 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 are designed for Linux  𝗧𝗵𝗲 𝗟𝗶𝗻𝘂𝘅 𝗙𝗶𝗹𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 – 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄   Understanding these core directories will set you up for success:  📂 /𝗯𝗶𝗻 – Essential system commands   📂 /𝗯𝗼𝗼𝘁 – Boot files for system startup   📂/𝗱𝗲𝘃 – Device files (hardware access)   📂 /𝗲𝘁𝗰 – Configuration files (the system’s control center)   📂/𝗵𝗼𝗺𝗲 – Your personal user space   📂 /𝗹𝗶𝗯 – Shared system libraries   📂 /𝘃𝗮𝗿 – Logs & variable files   📂 /𝘂𝘀𝗿 – Installed programs   📂 /𝘁𝗺𝗽 – Temporary files  𝗗𝗮𝗶𝗹𝘆 𝗟𝗶𝗻𝘂𝘅 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝗬𝗼𝘂’𝗹𝗹 𝗨𝘀𝗲   🖱️ 𝚌𝚍 – Change directory   📂 𝚕𝚜 – List files & directories   📁 𝚖𝚔𝚍𝚒𝚛 – Create a new directory   📄 𝚌𝚙 – Copy files   🚀 𝚖𝚟 – Move/rename files   🗑️ 𝚛𝚖 – Remove files (handle with care!)  ✅ 𝗡𝗘𝗩𝗘𝗥 blindly copy-paste commands (especially with 𝚜𝚞𝚍𝚘)   ✅ Understand 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 before making changes   ✅ Learn to 𝗿𝗲𝗮𝗱 𝗹𝗼𝗴𝘀 (`/var/log`) to troubleshoot issues   ✅ 𝗠𝗮𝘀𝘁𝗲𝗿 𝗼𝗻𝗲 𝘁𝗲𝘅𝘁 𝗲𝗱𝗶𝘁𝗼𝗿 (`vim` or 𝚗𝚊𝚗𝚘)   ✅ Keep your 𝗵𝗼𝗺𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝘆 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗲𝗱 for efficiency  𝗛𝗼𝘄 𝘁𝗼 𝗚𝗲𝘁 𝗦𝘁𝗮𝗿𝘁𝗲𝗱 𝘄𝗶𝘁𝗵 𝗟𝗶𝗻𝘂𝘅 𝗧𝗼𝗱𝗮𝘆   📌 Install Linux in a 𝗩𝗠 (𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲)   📌 Use 𝗯𝗮𝘀𝗶𝗰 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝗱𝗮𝗶𝗹𝘆   📌 Read 𝗺𝗮𝗻 𝗽𝗮𝗴𝗲𝘀 (`man ls`, 𝚖𝚊𝚗 𝚌𝚙 … they’re your best friend)   📌 𝗕𝗿𝗲𝗮𝗸 𝘁𝗵𝗶𝗻𝗴𝘀 (𝗶𝗻 𝗮 𝘁𝗲𝘀𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁) – then 𝗳𝗶𝘅 𝘁𝗵𝗲𝗺 (best way to learn!)  🔹 𝗜𝗻 𝗮 𝗰𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗹𝗱, 𝗟𝗶𝗻𝘂𝘅 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝗢𝗦—𝗶𝘁’𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗼𝗱𝗲𝗿𝗻 𝘁𝗲𝗰𝗵.    The sooner you get comfortable with it, the further you’ll go in your career!  

More than half of academics come from the 20% richest families, whereas only 5% come from the poorest 20%. But how about their academic productivity? A new study finds that, whereas there are "no differences in the average number of publications, academics from poorer backgrounds are both more likely to not publish and to have outstanding publication records." "These findings suggest that academics from lower socio-economic backgrounds are more likely to pursue research agendas off the beaten path, which may result in scientific breakthroughs but also in a higher failure rate" The study also examines peer recognition, finding that even though "academics from poorer backgrounds introduce more novel scientific concepts, [they] are less likely to receive recognition, as measured by citations, Nobel Prize nominations, and awards." Two other findings: 🔹"socio-economic composition of academics has remained remarkably stable over seven decades," 🔹"representation from lower socio-economic backgrounds is higher in disciplines with a stronger emphasis on quantitative relative to verbal skills." "While around 60% of academics in the humanities come from the top quintile of the parental income distribution, around 40% of academics in mathematics and economics come from the top quintile." Read the full study here: Climbing the Ivory Tower: How Socio-Economic Background Shapes Academia https://lnkd.in/eeJsryQw by Ran Abramitzky, Lena Greska, Santiago Pérez, Joseph Price, Carlo Schwarz, and Fabian Waldinger The study looks at US academics. Would be very interesting to know whether the situation in other parts of the world is (much) different.

This Teacher Changes 30 Lives Each Morning Here's Why This Works Every morning, a teacher greets her students one by one - not with rules, but with choice: A hug, A high-five, a nod, or quiet. A ritual so simple. Yet it tells 30 children: You are seen. You are safe. You belong. Here’s what this teaches us about leadership - and how to apply it at work: 1. Honor Autonomy (Self-Determination Theory) When people get to choose how they engage, they show up with more agency. Autonomy isn’t about letting go of structure - it’s about giving room to opt in. Try this: 🔷 Let people set their own work cadence - async, deep focus, or collaborative sprints 🔷 Ask: “What support looks best for you right now?” *** 2. Create Micro-Moments of Connection (Broaden-and-Build Theory) We don’t need hour-long one-on-ones to build trust. A genuine check-in. A name spoken with intention. That’s the glue. Try this: 🔷 Pause to celebrate effort, not just outcomes - a quick voice note, a public thank-you 🔷 Remember small details - a kid’s soccer game, a partner’s surgery - and follow up *** 3. Signal Safety in Small Ways (Polyvagal Theory) The nervous system responds before the intellect does. Safety is felt first. And safe leaders create brave spaces. Try this: 🔷 Ask: “Is now a good time?” before giving feedback or asking for decisions 🔷 Stay calm and present, especially when tensions rise - your tone sets the tone *** 4. Design for Anticipatory Joy (Affective Forecasting) The brain lights up for what’s coming next. The ritual at the door gave students a reason to show up smiling. Try this: 🔷 Drop a kind, unexpected message in the team chat - just because 🔷 Celebrate mundane milestones - 100 days in the role, 50th client call, 1st brave no *** 5. Anchor Culture in Meaningful Rituals (Harvard Research on Rituals) Rituals are memory-makers. They codify values in action - they say, this is who we are. Try this: 🔷 End each quarter with storytelling: what stretched us? what did we learn? 🔷 Welcome new hires not with logistics, but with a story of your team's "why" *** This teacher didn’t redesign the curriculum. She redesigned how people enter the day. You don’t need a big title to lead like that - Just the courage to meet people at the door. 💬 What’s one ritual you’ve seen shift the energy of a space - or want to create where you work? 🔁 Repost to inspire kind actions in the workplace. 🔔 Follow Bhavna Toor for more on conscious leadership.