pedagogy on S Anand

How I use AI to teach

Fri, 20 Mar 2026 07:12:47 +0530

I’ve been using AI in my Tools in Data Science course for over two years - to teach AI, and using AI to teach.

I told GitHub Copilot (prompt) to go through my transcripts, blog posts, code, and things I learned since 2024 to list my every experiment in AI education, rating it on importance and novelty.

Here is the full list of my experiments.

1. Teach using exams and prompts, not content

⭐ Use exams to teach. The typical student is busy. They want grades, not learning. They’ll write the exams, but not read the content. So, I moved the course material into the questions. If they can answer the question, great. Skip the content.
Use AI to generate the content. I used to write content. Then I linked to the best content online – it’s better than mine. Now, AI drafts comics, interactive explainers, and simulators. My job is to pick good topics and generate in good formats.
Give them prompts directly. Skip the content! I generated them with prompts anyway. Give students the prompts directly. They can use better AI models, revise the prompts, and learn how to learn with AI.
⭐ Add an “Ask AI” button. Make it easy for students to use ChatGPT. Stop pretending that real-world problem solving is closed-book and solo.
⭐ Make test cases teach, not just grade. Automate the testing (with code or AI). Good test cases show students the kind of mistake they may - teaching them, not just grading them. That’s great for teachers to analyze, too.
Test first, then teach from the mistakes. Let them solve problems first. Then teach them, focusing on what failed. AI does the work; humans handle what AI can’t. This lets us teach really useful skills based on real mistakes.

2. Make cheating pointless through design, not detection

⭐ Allow copying, collaboration, and hacking. In real work, nobody gets bonus points for working alone or re-inventing the wheel. Collaboration, using available resources well, verifying inputs, disclosed shortcuts – all are rewarded.
Reward originality without punishing collaboration. Blanket anti-copying rules assume that all similarity is bad. A more AI-native approach is to allow learning from others openly, but give extra credit for genuine variation, initiative, and novel improvement.
⭐ Give each student a unique variant. If everyone sees the same problem with the same visible answer path, answer-sharing becomes the dominant strategy. Deterministic but unique variants shift the game from leaking answers to actually solving the problem.
Make process logs part of the evidence. When outputs can be copied or AI-generated, the trace becomes more valuable than the final artifact. Logs, verification notes, session recordings, and agent traces show whether the student can actually orchestrate the work.
Use repo-grounded vivas for authenticity. If you really want to know whether a student owns their project, ask them questions drawn from their own repo and make them change something live. That is much harder to fake than polished submitted output.
Use structural similarity, not string matching. Strip docstrings, tokenize, MinHash. Students who rename variables are still caught; students who genuinely collaborated produce detectable clusters rather than suspicious pairs.

3. Test skills that matter in an AI world

⭐ Teach what AI still cannot do well. Syntax and routine execution are declining in value. Judgment, debugging, orchestration, validation, integration, and taste are rising. The curriculum should move upward, not cling to the parts AI is already eating.
Use hard, messy problems to build real resilience. Some questions should be intentionally tricky, partly wrong, hidden in the UI, or out of syllabus. The students who find and solve them anyway are demonstrating exactly the adaptability that real work demands. Smooth progression alone doesn’t build that.
Test live, hands-on AI skills. Don’t just lecture about embeddings, vision, structured outputs, or hallucinations. Put students in live API-driven tasks where they have to use these things under time pressure and genuine uncertainty.
Grade students on designing AI workflows. In many real settings, the important skill is not “give the answer” but “design the chain of steps that gets to the answer reliably.” That includes tools, prompts, datasets, quality checks, fallbacks, and output formats.
Use game-like tasks to teach agentic work. Mazes, escape rooms, and API games force state tracking, exploration strategy, and backtracking — exactly the behaviors agentic systems require. They’re not gimmicks; they’re the syllabus.
Test prompt attacks and defenses. Security and adversarial literacy should not be abstract topics. Make students jailbreak, defend, manipulate, and harden model behavior. That turns “prompt security” from a lecture topic into a measurable skill.

4. Make assessment more like real work

Grade richer work, not just one-line answers. Real work is often multimodal: images, stories, APIs, analyses, and dashboards. If assessment automation cannot handle those, it will keep pushing education toward fake neatness.
Grade the spec, not the code. When AI writes the code, the real artifact is the machine-readable brief: goal, constraints, done-when, counter-examples, eval suite. That is often where the actual thinking lives anyway.
Count real open-source contributions as coursework. A merged PR to a public repo is harder, messier, and more educational than most sealed academic assignments. It teaches scoping, etiquette, usefulness, and real external standards.
Let students build virtual TAs from real course material. The project is educational, and the output becomes infrastructure for the next cohort. Good assignments should create assets, not just submissions.
Reward originality structurally, not just rhetorically. Most courses praise creativity but grade only correctness. Use embeddings to measure cohort-level similarity and explicitly reward meaningfully distinct outputs. Originality becomes real, not decorative.

5. Use AI to build the course, not just teach inside it

Let AI write, test, and fix draft questions. The interesting move is not just “AI drafts items.” It is “AI drafts, runs, breaks, revises, and improves them before any student sees them.” That dramatically changes how fast a course can evolve.
Use coding agents to test the exam before students do. If an agent solves a question instantly, you should ask what the question is actually measuring. Agents become both QA tools and mirrors for curricular relevance.
Use AI-generated comics to explain why the question exists. Students often resist tasks they do not understand. A comic can smuggle in the pedagogical point with very low friction and high memorability.
Use interactive explainers for unfamiliar concepts. AI can generate not just text answers but visual, animated, intuitive explanations. That makes concept onboarding faster for both students and new faculty.
Keep teacher adoption in familiar formats. A good innovation that slots into slides, handouts, OCR flows, and short feedback loops will beat a brilliant system nobody can actually use next semester.

6. Build the infrastructure for AI in education

⭐ Break rubrics into binary sub-criteria; reason before judging. Open-ended project grading becomes more auditable when you decompose it into binary yes/no criteria and ask the model to explain its reasoning before delivering a verdict. High or suspicious scores get re-evaluated with stronger guardrails.
Give every student shared, budgeted AI access. If AI access depends on personal subscriptions, the institution is quietly grading wealth, not skill. Shared governed access makes AI a course capability, not a private advantage.
Let AI handle routine support; keep humans for judgment. AI handles repetitive, searchable, first-pass questions. Humans handle ambiguity, reassurance, escalation, and final accountability. Neither alone is the right model at scale.
Turn recurring answers into canonical Q&A cards. Once the same question appears three times, it should stop living in somebody’s head or an old thread. Convert it into a canonical artifact that both humans and bots can cite consistently.
Govern with green/amber/red review levels. Not every decision needs the same scrutiny. Auto-ship the low-risk, spot-check the medium-risk, always human-review the high-stakes. This is how you scale without losing trust.
Roll out in shadow mode first. High-stakes academic workflows should not be launched with fingers crossed. Run the AI system quietly in parallel with human judgment and learn before turning it loose.
Turn policy into executable checks. A policy that cannot be operationalized at scale is mostly theater. If you can translate rules and rubrics into machine-checkable form, governance becomes consistent rather than person-dependent.
Make the course publicly inspectable. Openness raises the bar. It invites scrutiny, reuse, criticism, and improvement, and it turns the course into a visible institutional experiment rather than a sealed classroom.
Use reasoning models only for the borderline cases. Cheap screening first, expensive verification for the high-stakes or suspicious. Increasing reasoning effort on even a small model can flip an evaluator from sloppy to reliable — the cost curve makes this the natural operating model.

7. Analyze and research learning exhaust

Track which AI tools students actually use. Once AI use is instrumented, you stop guessing. You can see which models students choose, when they ask for help, and what behavior actually correlates with success.
Redesign exams from behavior data, not intuition. Model choice, timing, retry patterns, and deadline behavior all reveal how students really work. That should feed back into question design, support strategy, and pacing.
Analyze broken code before it compiles. Novices often fail at syntax long before you reach the real misconception. Structural parsing of broken code lets you give feedback on thought process instead of just rejecting the submission.
Use code traces to surface hidden misconceptions. Timestamped traces reveal overfitting, thrashing, structural confusion, and missing invariants — patterns that polished final submissions hide entirely.
Turn replay galleries into faculty-readable stories. Raw logs do not change policy. Narrated replays and plain-language error-pattern reports do. The point is to make evidence legible to decision-makers, not just analysts.
Break problem-solving into coachable steps. “Weak student” is too vague to be useful. Better to ask: did they fail at reading the givens, choosing a strategy, surviving a multi-select trap, or knowing when to cut losses? Each is a trainable failure mode.
Study bias in peer review itself. If peer assessment matters, reviewer quality matters too. You can detect generous, timid, extreme, and calibrated graders from the data, then moderate or train accordingly.
Treat learning analytics as a reusable research programme, not a one-off dashboard. The infrastructure for tracking misconceptions, prerequisite transfer, and course-to-course movement can be built once and reused across cohorts. That turns isolated AI experiments into institute-level knowledge and publishable educational research.

8. Upgrade the human role

Make judgment and taste explicit learning goals. AI makes average output cheap. The premium moves to selecting what is worth doing, recognizing quality, and knowing what to reject. That is a teachable skill, not a vague aspiration.
Teach directional feedback as a skill. You do not always need to micromanage AI with detailed corrections. The higher-order skill is to say “more concrete,” “less jargon,” “optimize for faculty adoption,” or “make this defensible.” That is learnable and more effective than micromanaging.
Teach faculty to manage agents, not just chat with them. Institutional AI does not scale on prompting alone. People need to learn specs, budgets, kill switches, and escalation rules — orchestration literacy, not just chatbot familiarity.
Use AI as a personalized coach. The model is not just an answer engine. It can become a research guide, curiosity amplifier, and next-step recommender tailored to the individual learner’s gaps and goals.
Let non-coders build interactive learning tools. AI lowers the cost of making timelines, maps, biographies, and interactive explainers. That opens AI-native pedagogy far beyond computer science into humanities and social sciences.
Teach students to run many AI attempts in parallel. One of the biggest AI-native workflow shifts is from single-path effort to portfolio thinking — run several attempts, compare them, and converge faster. That is a teachable habit, not an obvious default.

Breaking Rules in the Age of AI

Sun, 01 Feb 2026 14:58:28 +0800

Several educators have AI-enabled their courses, like:

David Malan at Harvard CS50 provides an AI-powered “rubber duck debugger” trained on course-specific materials.
Mohan Paturi at UC San Diego has deployed AI-tutors to his students.
Ethan Mollick at Wharton uses AI as tutor, coach, teammate, simulator, even student, and runs simulations.
Jeremy Howard’s Fast.ai encourages students to use LLMs to write code, with a strict verification loop.
Andrew Ng DeepLearning.AI integrates a chatbot into the platform, next to code cells, to handle syntax errors and beginner questions.

But no one seems to have eliminated reading material, nor added an “Ask AI” button to solve each question, nor run it at my scale (~3,000 students annually).

Nice, but what should I teach in the age of AI?

Simon Willison led me to Matt Webb led me to Milan Cvitkovic led me to think about assumptions and things we assume we can’t do.

We’ve taught students a lot of “learned helplessness”, like:

You can’t ask dumb questions
You can’t use AI for help
You can’t fail
You can’t change direction mid-way
You can’t solve without knowing
You can’t question the teacher
You can’t ask others for help
You can’t delegate boring stuff

These aren’t bad lessons. But doing the opposite (and knowing when to) is also an important skill for the AI age.

So, in my course, this term, students will also be taught that:

You CAN ask dumb questions. Every question has newbie prompts like “Why should I study this?” or “Why THIS tool?” or “Can you explain that in Marathi?”. Ask away!
You CAN use AI for help. Every question has an “Ask AI” button. Click on it. If it gives you the answer directly, great! Copy-paste it and save yourself time. If it doesn’t figure it out!
You CAN fail. Try an answer: you get an immediate response. If it doesn’t work, try again. And again.
You CAN change direction mid-way. Some questions will guide you down a path that won’t work. Change direction. Try something else.
You CAN solve without knowing. Some questions will be in foreign languages. Some will be “out of syllabus”. Some will be “beyond your level”. Use AI to bridge the gap!
You CAN question the teacher. Some questions will be wrong, literally unsolveable as stated. Some questions will be ambiguous. Some won’t even have a question. That’s life. Your client and boss will do that too. Solve it anyway!
You CAN ask others for help. Some questions are broken into parts. Different people get different parts. Find your team and ask for help (or hide your work and game, learn politics!)
You CAN delegate boring stuff. There’s no restriction on having AI (or anyone else) do any part of the course for you. If it’s boring, and you can get someone to do it, delegate!

Since AI can solve so many problems, let’s learn to break some rules and ask impossible questions to our AI overlords?

Tools in Data Science - Jan 2026

Thu, 29 Jan 2026 07:33:22 +0800

My Tools in Data Science course is available publicly, with a few changes from last year.

First, I removed all the content! Last year, Claude generated teaching material using my prompts. But what’s the point? I might as well give students the prompts directly. They can tweak it to their needs.

This time, TDS shares the questions needed to learn a topic. Any AI will give you good answers.

Second, it focuses on what AI does NOT do well. Coding syntax? Who cares. Basic analysis? ChatGPT can do that. In fact, each question now has an “Ask AI” button that dumps the question into your favorite AI tool. Just paste the answer and move on.

But, these questions (hopefully) teach you where AI fails. Putting tools together, prompting well, debugging and evaluating the output, etc. These matter more.

Third, it’s easier to audit. Anyone can take the course, even outside IITM. You can join the public Google Group for announcements. All questions are discussed publicly on GitHub.

So, check the new version out, learn, and please share feedback!

Workshops That Teach Me More Than You

Sat, 18 Oct 2025 09:38:22 +0000

I don’t charge for workshops. Altruism? No: it’s self-interest.

“If you’re not paying for it, you’re not the customer; you’re the product being sold.” Andrew Lewis, via Tim O’Reilly, 2010.

My workshop process is designed to benefit me first.

I pick topics I want to learn, not stuff useful to the audience. Example: I picked DuckDB for my PyCon India 2025 talk to learn it.

I experiment on the audience. Example: I tried voice-vibe-modeling in my RIP Data Scientists talk. Experiments fail often, e.g. vibe-coding Minecraft in PyCon IN 2023.

I learn from the audience. Example: Rakesh Roshan’s K-fetish is statistically significant, via my Vibe Analysis workshop.

I collect data from the audience. Example: How student copy.

I gather ideas. Example: skills AI will replace, from my IITM DOMS commencement talk.

I publish these, too, with recordings and transcripts.

LLMs let me do more such stuff:

LLM simulations. We play an LLM-driven case. I learn from the choices.
LLM Q&A talks. Audience questions → LLM → live slides.
Cluster polls. Audience answers a question. We cluster live and learn.
Compare models. I show multiple answers to same question. You votes. We review diffs.
Learn code prompting. You build the same app with your own prompts. We analyze what works.
Security testing. Try to jailbreak or inject prompts. We map what works and why.

If your HR team or college is OK with all the above, ping me. I’m game for a free talk / workshop!

On teaching

Tue, 13 Jan 2009 12:00:00 +0000

This vacation, I took a session each for class XI and XII at my school, Vidya Mandir. The subject was Computer Science (the only one I can teach with some confidence), and the topic was networks.

It was an experiment, in two parts. The first was to understand how students of this generation interact with the Internet. (I'm twice as old as them, so I guess they qualify as the next generation.) The second was to see whether I'd leave them far behind, or they'd leave me far behind.

I began the class with a series of questions.

How many of you have...	Expected	Actual
Access to a PC and the Internet (home or nearby). I was expecting ~80%. Every single one of them raised their hands. Every single one.	80%	100%
Chatted online. I was expecting ~70%. Every single one, except for one girl, raised their hands.	70%	100%
Used a bluetooth device. I was expecting around 60%. I got nearly everyone, but the remaining were wondering what that was.	60%	100%
Video-chatted. I expected ~50%. Got ~80%	50%	80%
Uploaded a photo or video. Again, far more than expected.	40%	80%
Own a blog or website. This is where the surprises started. I thought that at least one in 3 would have a blog. Turns out I was wrong. There were very few.	30%	5%
Written a web application. Not one soul. Some thought they had, but no...	10%	0%
Contributed to an open source project. None at all.	1 or 2	0%

It was an eye-opener. On the one hand, everyone has an Internet connection. (In fact, the announcements following the morning prayer began with the Principal warning about the dangers of chatting with strangers online.) On the other hand, they're doing little of the cool stuff.

Some of the discussions I had after class did lessen my concern a bit. There are, as always, a few that are very interesting in hacking, and are playing around with a lot of interesting things. But still, on average...

As for the other part of the experiment, I spent an hour talking about what goes on behind the scenes when they search on Google, taking them down to some of the elements of HTTP. My slides are below. I do suspect I left a fair number of them behind, but there were a handful that were with me right up to the end.

Computer Networks: An Introduction

View SlideShare presentation or Upload your own. (tags: http)

But I learned something that I did not expect. I spent a lot of time at the staff room, and talking with the teachers. The best way I can summarise what I learnt is through this Calvin and Hobbes strip.

Somehow, I thought the bulk of the discussion at the staff room would centre around students. Or, at the very least, around education. It was eye-opening to listen to a two-hour-long argument on the political reasons behind the tea at primary school staff room being better than at high school's.

I remember my first book on acting defining a modern-day magician as "an actor who plays the role of a magician". The modern-day teacher is, in similar vein, an employee assigned role of a teacher. Teaching is their profession, not passion. Not that they are disinterested, quite the opposite. But oh, it could be so much better!

I read a speech by John Taylor Gatto titled "The Six-Lesson Schoolteacher". He gave this speech on being awarded the New York State Teacher of the Year award in 1991. He teaches six lessons at school, he says.

The first lesson I teach is: "Stay in the class where you belong." I don't know who decides that my kids belong there but that's not my business.

The second lesson I teach kids is to turn on and off like a light switch. I demand that they become totally involved in my lessons... But when the bell rings I insist that they drop the work at once and proceed quickly to the next work station. Nothing important is ever finished in my class, nor in any other class I know of.

The third lesson I teach you is to surrender your will to a predestined chain of command... As a schoolteacher I intervene in many personal decisions, issuing a Pass for those I deem legitimate, or initiating a disciplinary confrontation for behavior that threatens my control.

The fourth lesson I teach is that only I determine what curriculum you will study.... Of the millions of things of value to learn, I decide what few we have time for. Curiosity has no important place in my work, only conformity.

In lesson five I teach that your self-respect should depend on an observer's measure of your worth... A monthly report, impressive in its precision, is sent into students' homes to spread approval or to mark exactly -- down to a single percentage point -- how dissatisfied with their children parents should be.

In lesson six I teach children that they are being watched. I keep each student under constant surveillance and so do my colleagues... Students are encouraged to tattle on each other, even to tattle on their parents. Of course I encourage parents to file their own child's waywardness, too.

I smiled a bit when I read this. It had been a while since I'd been in school, and I was lucky to have been in very liberal colleges. But then I went back to school and saw it for myself. The organisation that comes closest to the school is the military... or the prison. Not exactly the best place to foster creativity.

I began my class this time by saying, "Look, I might be wrong in what I tell you. Usually, it's not deliberate. Quite often, I simply may not know. Or I may mis-communicate. When in doubt, Google and Wikipedia. Let me repeat: this is the single most important thing that I can tell you. When in doubt, Google and Wikipedia."

At the end of the class, a few came over and said, "But how do we do that? Our teachers are asking us not to waste time on the Internet, and to stay away from Wikipedia!"

Sir Ken Robinson gave a TED Talk on Do Schools Kill Creativity? Do watch it. Apart from being one of the funniest 20-minute talks ever, it drives home a strong message. Schools aren't quite organised to foster creativity. When they were created, that wasn't the intent.

Teaching as a profession, I imagine, does not pay as much as many others. So there's little interest for practitioners to enter the field. I can therefore understand and appreciate that it takes a long time for new knowledge to enter the curriculum. But also sad is the way the curriculum is treated. It isn't treated, as Gatto says, as choices among the million things of value to learn. It is treated as a Bible that defines knowledge.

It is easy for teachers to fall into the trap. If it contradicts the curriculum, it is wrong. If it is not in the curriculum, it is irrelevant. Since I know the curriculum inside out, I know all that is required to know. It's not that I refuse to learn. Just that there is nothing more to learn that is relevant.

As an institution, schools aren't going away any time soon. Nor perhaps should they. But in the interest of knowledge and creativity, I can only hope for two things.

Students: keep learning what you like outside of school. It may be your only hope.
Everyone else: drop by to your old school or your nearby school, and offer to teach one class any subject you have a passion for. You'd be surprised at how well you'll be received, how much you know, and how much you can learn by that interaction.

Comments

Kannan 13 Jan 2009 12:22 pm: I am not suprised with results at Vidya Mandir.There is big disparity between metro city(upper mid school) and rest of the state.
Karthikeyan 13 Jan 2009 11:03 pm: Thanks for sharing your experience at the school. It gave an idea about how’s the next generation educated? It’s high time that we need to bring in the right kind of education to all irrespective of which school they belong.
Anish 14 Jan 2009 9:50 pm: On slide 14 did the definitions get interchanged?
Sasidhar 15 Jan 2009 5:09 am: Nice. I’d love to go back to my school and do a session like this.
Sounds like good fun.
Amandeep 15 Jan 2009 11:40 am: Really enjoyed reading this article..maybe i will try and go back to teach….
som_nath 21 Jan 2009 11:43 am: Simply true though my father is an university teacher and i have seen him in class (practicals) too :)
PS: RTed to Twitter
Sachin Gopalakrishnan 23 Jan 2009 12:35 am: I can think of atleast on reason for not a single student contributing code or writing a blog. It’s TABOO in India for students who concentrate on anything beyond course work!

I would know, i had wasted time for atleast half a decade (during graduation)until i landed a job due to those wasted years and gained my redemption.
bootcat 3 Apr 2009 7:15 am: ungal ezhuthukal ennai kavaruthu annae . naan , deepan , verizon la work panuren . naan oru made in chennai , currently in chennai . tamilku indha alavu net a pala “service” kalai ezhupeerukeenga . ungalai paaraatiyae aaganum . keep the good work going annae . neeram kadachuthunaa reply pannunga . eppadiku , bootcat aka deepan prabhu

The Six-Lesson Schoolteacher

Tue, 26 Dec 2006 12:00:00 +0000

The Six-Lesson Schoolteacher, by John Taylor Gatto, New York State Teacher of the Year, 1991. (The first part of it is sarcastic. This man is speaking passionately of things he despises in the education system.)

The first lesson I teach is: “Stay in the class where you belong.” I don’t know who decides that my kids belong there but that’s not my business.

The second lesson I teach kids is to turn on and off like a light switch. I demand that they become totally involved in my lessons, jumping up and down in their seats with anticipation, competing vigorously with each other for my favor.

The third lesson I teach you is to surrender your will to a predestined chain of command. Rights may be granted or withheld, by authority, without appeal.

The fourth lesson I teach is that only I determine what curriculum you will study. (Rather, I enforce decisions transmitted by the people who pay me).

In lesson five I teach that your self-respect should depend on an observer’s measure of your worth. My kids are constantly evaluated and judged.

In lesson six I teach children that they are being watched. I keep each student under constant surveillance and so do my colleagues. There are no private spaces for children; there is no private time.

It is the great triumph of schooling that among even the best of my fellow teachers, and among even the best parents, there is only a small number who can imagine a different way to do things.

He concludes:

School is like starting life with a 12-year jail sentence in which bad habits are the only curriculum truly learned. I teach school and win awards doing it. I should know.

Comments

You are in prison | s-anand.net 21 May 2010 12:01 pm (pingback): […] If you’re a well-paid professional in an India IT services firm, your freedom is limited.(This holds if you’re a student, too.) […]
Sai Suren Kumar 4 Sep 2012 5:25 am: Nice one sir… there is only a small number who can imagine a different way to do things.

TEDTalk by Sir Ken Robinson

Mon, 18 Dec 2006 12:00:00 +0000

Sir Ken Robinson’s TED Talk on education is brilliant and funny. Some quotes that struck me:

If you think of it, children starting school this year will be retiring in 2065. Nobody has a clue, despite all the expertise that has been on parade the last four days, what the world will look like in five years’ time. And yet we’re meant to be educating them for it. So the unpredictability, I think, is extraordinary.

If you were to visit education as an alien and say “What’s it for?”, I think you’d have to conclude, if you look at the output, that the whole purpose of public education throughout the world, is to produce university professors. Isn’t it? They’re the people who come out on top, and I used to be one. (So there!) And I like university professors, but you know, we shouldn’t hold them up as the high watermark of all human achievement – they’re just a form of life.

Some people will never program

Thu, 20 Jul 2006 12:00:00 +0000

All teachers of programming find that their results display a ‘double hump’. It is as if there are two populations: those who can [program], and those who cannot [program], each with its own independent bell curve.

How to pick a course

Thu, 29 Jun 2006 12:00:00 +0000

In his article on The Power of the Marginal, Paul Graham suggests (among other things) a way of picking courses at college.

One way to tell whether a field has consistent standards is the overlap between the leading practitioners and the people who teach the subject in universities. At one end of the scale you have fields like math and physics, where nearly all the teachers are among the best practitioners. In the middle are medicine, law, history, architecture, and computer science, where many are. At the bottom are business, literature, and the visual arts, where there’s almost no overlap between the teachers and the leading practitioners. It’s this end that gives rise to phrases like “those who can’t do, teach.”

Incidentally, this scale might be helpful in deciding what to study in college. When I was in college the rule seemed to be that you should study whatever you were most interested in. But in retrospect you’re probably better off studying something moderately interesting with someone who’s good at it than something very interesting with someone who isn’t. You often hear people say that you shouldn’t major in business in college, but this is actually an instance of a more general rule: don’t learn things from teachers who are bad at them.

A crash course in learning theory

Thu, 05 Jan 2006 12:00:00 +0000

A crash course in learning theory.

Comments

Prakash 7 Jan 2006 12:00 pm: This link was very kool Anand. I m goin to train few users next week. It cud b of gr8 use. Thanks…

Problems in online education

Mon, 14 Oct 2002 12:00:00 +0000

Problems in online education. David McGrath teach English online. He ends up …

spending hours copying excerpts from her paper into cheater catching software programs and search engines to verify that it was authentic. [It is not authentic] 30 to 40 percent of the time.

Universities today certify the ability to do original work, in addition to training students. Corporates, I think, value the ability to create a good quality report, irrespective of originality. This is a mismatch. Maybe the whole objective of courses, at least online courses, will change shortly. via Plastic.

In defense of cheating

Wed, 27 Mar 2002 12:00:00 +0000

In defense of cheating. About how the school system should be changed to encourage co-operation.

Socratic method

Fri, 26 Jan 2001 12:00:00 +0000

Rick Garlikov tried using the Socratic method to teach binary numbers to a third grade class. Looks like it worked well. I’m all for the Socratic method of teaching.