learning-design 3

I conducted an ideation session for my TDS course. It was an open session along with students, TAs and instructors. It roughly explained how we conduct TDS - and in particular, how we design questions that teach something even when students can (and are encouraged to) work with AI agents as well as each other. Here’s the link to the session Here’s my summary of the session: Pick what to teach. My AI Advice page lists AI-era skills. Yours may vary. Evaluate questions against six tests: Learning test. Will they learn something by solving the question? Forward test. Does copying the answer work for everyone? Solver test. Can one person build a solver and share with everyone? Agent test. Can Codex solve it if they’re logged in? Cheating test. Is cheating costlier than doing it honestly? Or, will they learn something even when they cheat? Evaluation test. Is it easy and robust to evaluate automatically? Measure learning transfer. How do we know the actually learnt the skill? Interestingly, just passing these principles to Claude / ChatGPT gives me pretty good AI and collaboration proof questions on many topics. ...

To teach a mindshift change like AI adoption, I’ve tried to: Workshop: get them to do it. “Let’s try something. Can you share your screen?” Live-code: show them how. “I’ll share screens and tyep this.” Demo: show what’s possible. “Here’s what I built.” Talk: explain it. “Here’s something we can build.” Interview: ask them about it. “What do you think?” Listen: let them yap. The most effective are on top. ...

Here’s a post I shared with the students of my Tools in Data Science course at IITM. This was in response to a student posting that: The design of TDS course lecture videos are designed in such a way that it could be understood only by the data scientists not by the students like me who are entirely new to the field of data science. Though I have gone through 6 weeks of course lecture videos, I am not fully aware of the usage of ChromeDevTools, Bash, Github etc…. ...