I should have loved biology too

Boredom vs. Wonder in Science Education

• Many recall science classes (biology, CS, history, math) as rote memorization and “lifeless recitation of names,” with astonishing facts presented without any sense of awe.
• Teachers are often constrained by state curricula, standards, and benchmarks, leaving little room to dwell on beauty, stories, or conceptual insight.
• Some see great teaching as “performance art” whose goal is to hook curiosity, not just transmit facts; others note many teachers lack deep conceptual mastery themselves.

Role of History and Context

• Disagreement on “history first”: some argue teaching history of a field before basic skills (e.g., computer history to 13‑year‑olds) is backwards.
• Others say historical context makes material more memorable and meaningful (who proved what, why discoveries mattered), especially in subjects like law or advanced math.
• A recurring theme: timing matters—history is far richer once students can already “read” the subject.

Curriculum Design and Systemic Constraints

• Strong criticism of curricula built by committees optimizing for minimum common standards, seen as killing intrinsic motivation.
• Proposed alternative: “passion‑first” or project‑based paths where knowledge is introduced as “power tools” to pursue existing interests (e.g., helping a game‑obsessed kid build games).
• Skeptics ask how such individualized approaches scale to millions of students; defenders counter that current systems are already failing many.

Computer Science, Programming, and Education Paths

• Several recount abandoning formal CS because classes were dry (e.g., long lectures on JVM internals, computer history, or pure C syntax) versus the joy of quickly building things.
• Debate over whether CS is fundamentally about programming or about abstract computation and theory, with some insisting all of CS ultimately serves programming practice.
• Many emphasize that practical programming can be self‑taught cheaply, while degrees are better for foundational thinking, research skills, and long‑term versatility.

Falling in Love with Biology Later

• Numerous commenters describe hating school biology (especially memorization and Latin/Greek terminology) but later becoming captivated by molecular biology, neurophysiology, biochemistry, mycology, or paleobiology.
• Pop‑science books, visualizations (e.g., “Machinery of Life,” immune‑system explainers), and experiences like scuba diving often served as the “switch‑flipping” moments.
• Some pursued or contemplated second degrees or PhDs in biology or related fields, but rising tuition and opportunity costs are major barriers; others highlight funded PhD programs and free online courses as alternatives.

What Makes Biology Hard and Different

• Biology is portrayed as vast, messy, probabilistic, and full of uncertainty, requiring comfort with ambiguity and huge descriptive vocabularies.
• Several note a cultural divide: biologists historically focused on naming/classifying and qualitative description, while physicists/engineers favor quantitative, mechanistic models.
• There’s recognition that modern biology increasingly demands “extra” skills: coding, image analysis, advanced microscopy, statistics, and modeling.

Tech Meets Biology: Promise and Skepticism

• Enthusiasm for areas like bioinformatics, quantitative biology, computational epidemiology, and citizen‑science mycology, where CS skills clearly help.
• At the same time, biologists express fatigue with “tech savior” narratives (e.g., protein folding, medical ML, promises of rapid “silver bullet” cures), arguing these tools are valuable but far from solving core biological and medical challenges.
• A recurring caution: cross‑disciplinary work is powerful when tech people respect domain depth and focus on real bottlenecks (automation, tooling) rather than claiming to “cure cancer” with algorithms alone.

Language, Jargon, and Accessibility

• Latinized terminology and dense jargon are seen as major barriers, especially when teaching non‑native speakers or high‑school students.
• Some argue many processes could initially be taught visually and conceptually, with names and formal ontologies layered on later.
• Others note that naming/classification is unavoidable for precise communication, but agree curricula rarely get redesigned when understanding advances—they only accrete more chapters.