Math writing is dull when it neglects the human dimension
Math education and motivation
- Many recall school math as decontextualized “formula grinding” with no explanation of why concepts matter, how they arose, or what they’re for.
- Some teachers/educators in the thread report dramatic improvements when they:
- Assume most students can understand abstractions.
- Anchor topics in real problems, stories, or “superpowers” (e.g., integrals as area/accumulation tools, physics applications, video‑game trajectories).
- Others push back: claim most students mainly want to pass tests, not explore “cool applications,” and that heavy applications often frustrate more than they inspire.
- Several blame structural incentives: standardized testing, teachers underprepared in math, and grading systems that reward rote procedures over understanding.
Style of math papers and textbooks
- Many working in or near math find research papers overly terse, maximally abstract, and demotivating: definitions first, no examples, no sense of why results matter.
- A number of commenters argue this is partly:
- Cultural (elegance = brevity/generalization).
- Incentive-driven: reviewers mark down papers with “easy” explanations, pushing authors toward opaque proofs to seem deep.
- Mild gatekeeping/hazing (“I suffered to learn this; so should you”).
- Others defend the expert‑only style:
- Papers are reference materials, not tutorials.
- Experts don’t want to wade through hand‑holding.
- Non‑technical exposition belongs in textbooks, blogs, or review articles.
Narrative, “story mode,” and rigor
- Many support adding limited narrative: motivational framing, historical context, high‑level proof roadmaps, and concrete special cases before general theorems.
- Strong resistance appears to:
- Omitting technical conditions in statements, even in introductions.
- Overly “NYT bestseller” style, hero‑driven stories, or hype.
- Some hate any “story mode” and want equations first; others find precisely those equations approachable only after prose context.
Related topics and analogies
- Parallel complaints appear about engineering design docs and cryptography/math papers: over‑compressed, hard to learn from, optimized for proving the author’s mastery rather than teaching.
- A long side thread speculates that solving P vs NP could automate much of applied math; multiple replies argue this misunderstands both mathematics and complexity theory.