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Friday Finds
Fresh ideas, practical tools, and marketing-inspired thinking for people who design learning.
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Things are feeling a whole lot more hopeful around here this week, for all sorts of reasons. Not the least of which is because my daughter is home from college today! And maybe, just maybe, this week's election results are a sign that we're finding a little common sense again here in the US. π
Whatever the reasons, I'm heading into Friday with a gigantic smile on my face. It's a great day for a great day, and I hope yours is one too!
Thanks for reading!
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π΅ Today I'm listening to an old-fashioned mixtape that I made. |
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Supported by iSpring
What Actually Makes a Course Work?
Hereβs a solid refresher on the fundamentals that matter. On November 11, iSpring is hosting a live conversation with instructional designer Marina Arshavskiy on the ID principles worth keeping, what new tech is changing, and practical ideas you can use right away.β
βJoin the conversation ββ
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π BEST FIND
You Can't Connect the Dots If You Don't Have Any
We've all heard that learning should be about understanding, not memorizing. But what if that's a false choice?
Carl Hendrick explores new work from John Sweller and colleagues that challenges this familiar divide. The idea isn't that memorizing facts automatically creates understanding. It's that understanding depends on what we know and how those pieces connect.
Think about teaching someone to spot phishing emails. Recognizing a suspicious link matters. So does understanding how urgency, impersonation, and a convincing story work together to fool us.
For your next learning project, ask: What must people know, and how will I help them connect the dots?
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Also supported by Neovation
AI is changing how organizations learn and share what they know. Putting AI to Work, a weekly newsletter from Neovation CEO Dan Belhassen, shows what that looks like in practice: how other teams are applying AI, tips you can try, and the strategy behind it.
βSubscribe to get it every Tuesday morning ββ
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π‘ IDEA
Stop Building Faster. Start Defining Better.
AI can crank out training faster than ever. But who's deciding what good looks like?
Dr. Philippa Hardman reviewed 158 L&D job ads and found signs of a shift in a small group of emerging roles. Instead of focusing on creating content, these jobs place greater emphasis on defining problems, setting quality standards, and verifying results.
It connects with something I explored at iSpring AI Days last week: making bad training faster doesn't make it better.
Here's something to try: Before your next AI project, write down what success looks like. Include the real problem, what people need to do differently, and how you'll judge the results.
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π οΈ TOOL
Your Slides Went Out. Did Anything Happen?
Ever send someone a presentation and wonder if they even opened it?
βShare Deck lets you share PowerPoint or PDF files via individual links, and see which slides are viewed and for how long.
You can also update a deck without changing the link.
A clever solution that I've been looking for for ages!
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π² WILDCARD
Your Job Aid Shouldn't Be a Memory Test
What if the best way to help people remember something is to stop making them remember it?
Nielsen Norman Group explains why recognition is often easier than recall.
Instead of making employees dig through an expense policy, give them choices like βI lost a receiptβ or βI paid in another currency.β
Good job aids don't test people's memory. They help people get the job done.
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The Bottom Line
Better learning starts with better questions, not better tools.
What do people need to know? What does good look like? And how can we make it easier to get the job done?
Before you create more training, make sure you're solving the right problem.
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