The Problem-Solving Method They Removed From Every Textbook

Main ideas, concepts, and lessons

• The education system (as implemented) trains memorization, not understanding.

  • Textbooks and curriculum approval processes optimize for passing/processing rather than engaging with the material.
  • The “system” effectively hides whether students can reason about problems versus just recall answers.

• A key illustration: the textbook commission failed to check content.

  • In 1964, Pasadena, California, Richard Feynman read every proposed California public-school math textbook himself (others relied on reports/paperwork and did not thoroughly review).
  • A publisher submitted an essentially blank book (front and back covers only).
  • Even though it contained no material, it received ratings comparable to real books because commissioners assigned scores without noticing or verifying content.
  • Feynman’s broader point: averaging/processing can mask the most important truth—“real understanding” can’t be distinguished from empty output if nobody checks.

• Textbook “answers” often become word-replacements instead of explanations.

  • Example: a first-grade science question “What makes it move?”
    • Teacher edition answer: “Energy makes it move”
  • Feynman argues this is a tautology—a labeled word dressed up as an explanation.
  • What children actually need: mechanistic understanding (gears, ratchets, springs, how motion is produced).
  • Another example: “Why does shoe leather wear out?”
    • Textbook-style answer: “Friction”
    • Better explanation: microscopic bumps/notches that grab and tear leather as the shoe drags.

• A second illustration: elsewhere, students could memorize but not generalize.

  • In 1951, during a sabbatical teaching physics in Brazil (engineering school), Feynman observed:
    • Students could answer confidently to one phrasing of a question.
    • When the same question was rephrased, the room went silent.
  • Conclusion: they had fragile knowledge that depended on specific wording/inputs, not understanding.

• What Feynman taught instead: problem-solving via trial and error.

  • Students were taught to use a workflow:
    • Estimate first (guess the rough answer)
    • Calculate
    • Compare guess vs. result
    • Refine and repeat
  • This approach forces reasoning rather than plug-in memorization.

• Education can produce people who pass exams but can’t do real science/engineering.

  • Feynman’s speeches argued the system produces “exam performers” rather than people who can investigate, adapt, and reason from fundamentals.
  • He describes a grim contrast: those who succeed tend to have learned outside the system.

• Cargo cult science: correct-looking steps without the real underlying cause.

  • In 1974, at Caltech, Feynman told the “islanders and planes” story:
    • Islanders replicated visible rituals of landing (runways, wood “headphones,” bamboo antennas, signal huts).
    • Everything looked right, but planes never landed because the missing element wasn’t provided by imitation.
  • Lesson: visible form ≠ functional mechanism.
  • “Invisible” understanding is what makes outcomes work.

• Modern extension: AI mirrors the “memorizer” problem.

  • AI can generate fluent answers by pattern matching from massive text.
  • But like memorized education, it stalls when confronted with novel scenarios, contradictions, or tasks requiring genuine understanding/diagnosis.

• Personal workplace lesson: slide-deck competence fails under questions not on the deck.

  • In meetings, a presenter can provide answers that are “on the slides,” but fails when asked something not covered.
  • Feynman’s framing: that’s not just individual weakness; it reflects training aimed at memorizing presentations rather than understanding the underlying problem.

• Feynman’s alternative “method”: thinking with real problems constantly active.

  • Summarized via Gian-Carlo Rota (1996 lecture):
    • Feynman kept a dozen favorite problems in mind.
    • He constantly tested new ideas/tricks against them.
    • Connections sometimes emerged (“hits”) that others missed.
  • In practice, Feynman’s method includes:
    • keep your own open questions alive
    • test ideas by stripping away formal wording and verifying what you can check
    • estimate before calculating
    • rephrase to confirm understanding

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Methodology / instructions (detailed bullet list)

Feynman-style problem solving (core steps)

• Estimate first
  • Before using formulas, guess the rough answer.
• Calculate
  • Use the relevant equations/relations.
• Compare
  • Check whether the calculated result matches your estimate’s order of magnitude.
• Refine and repeat
  • If they differ a lot, reassess assumptions/equations.

“Understanding check” (how to verify you truly get it)

• Rephrase the question
  • Put the idea into different words.
• Try to answer in the new phrasing
  • If you can’t, you likely never understood, only memorized the original phrasing/format.

“Strip away the wording” and ground in verification

• Ignore official/formal wording
• Ask what you can actually check
  • Determine what you know because you can verify it directly (not because someone said it).

“Start from scratch” mindset

• When someone claims “we’ve always done it this way,” ask:
  • “What would we do if we were starting from scratch?”
• This reorients thinking toward mechanism and reasoning, not habit and authority.

Maintain a personal “problem list” (long-term habit)

• Write down your real open problems
  • Don’t write goals or KPIs; write the questions you genuinely care about.
• Keep them active
  • Don’t delegate them to someone else’s framework.
• Test everything against them
  • When new knowledge appears, see whether it “fits” or yields progress on your active problems.

Worked example of the “keep problems alive” dynamic

• When Feynman saw a plate wobble in the air:
  • He recalled his open questions about spinning motion.
  • He played with the equations to connect wobble with spin rate.
  • That chain of reasoning linked back to deeper physics ideas (eventually connected to major scientific work).

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Speakers / sources featured (identified in the subtitles)

• Richard Feynman (primary subject; speaker and author of referenced works)
• Gian-Carlo Rota (source describing Feynman’s method in a 1996 lecture)
• Senior professor (in the Brazil/year-end speech scene; quoted saying “we have a cancer”)
• Two students (mentioned as having done well in Feynman’s class; identities not named)

Feynman’s referenced publications

• Surely You’re Joking, Mr. Feynman (book)

Institutional contexts / implied audiences

• California public-school curriculum commission (commission members; not individually named)
• National Science Teachers Association (speech setting; not a named speaker besides Feynman)
• Caltech graduating class (audience; Feynman delivering warning)
• Cornell (cafeteria anecdote setting; no named speaker)