[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fnh1oohacz48f":3},{"code":4,"message":5,"data":6,"timestamp":42},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":16,"summary_md":17,"topic":18,"github_topics":21,"license":38,"homepage":39,"updated_at":40,"history":41},"xiaoyang111222\u002Fcn-math-taxonomy",2,"数学（小学到高中）、英语（三年级到高中）、初中物理的知识图谱：1644 个知识点、2403 条先修边、6723 道题，对齐课标，附自适应前置诊断。","Python",3232,745,486,6,[],"An open K-12 knowledge graph with prerequisite edges and diagnostic questions for math, English and physics.","## The problem\n\nAnyone building study tools for Chinese K-12 students hits the same wall: there is no machine-readable map of what depends on what. Textbooks and curriculum standards are PDFs full of prose; the prerequisite chain behind \"completing the square\" only exists in teachers' heads. This project turns that into data: 1,644 knowledge points, 2,403 prerequisite edges and 6,723 questions spanning primary-to-high-school math, English and physics, plus a diagnostic tool that walks errors back along those edges to an earlier concept.\n\n## What's different\n\nMost curriculum datasets are flat lists of topics or exam-question banks tagged by chapter. Here the value is the edge set — hard vs. soft prerequisites, cross-stage links from junior-high back to primary arithmetic — which enables the backtracking diagnostic rather than simple tagging. It is also unusually explicit about provenance: which textbook table of contents came from the official smart-education platform, which from third-party sites, and which are marked 待核. Compared with commercial adaptive-learning products, it is open data and inspectable, not a black box.\n\n## Who it's for\n\nEdtech developers who need a prerequisite graph or question bank under a Chinese curriculum; researchers studying knowledge tracing; teachers who want to see the dependency chain for a topic. It is not for anyone needing classroom-ready, teacher-vetted content: questions for high-school math and all English have not been blind-solved, and item difficulty or discrimination data is absent.\n\n## Cost of adoption\n\nPython plus a static JSON data format (topics + dependencies, borrowed from os-taxonomy). The README is long but mostly a provenance ledger — worth reading before trusting any edge. The license badge says CC BY-NC 4.0, so commercial use is restricted; the repository's LICENSE metadata does not confirm this, so verify before shipping. The top risk is quality: the author states plainly that this is an AI-generated first draft with no teacher review, that some scope (integrated math, some textbook volumes) follows unverified third-party tables of contents, and that English lacks listening\u002Fspeaking items. Budget for human review, not just integration.\n\n## Verdict\n\nThis is a six-day-old project with roughly 3,200 stars and about 500 more per day, so the attention is a spike, not a track record, and it has not appeared on the daily trending board. The underlying artifact — a curriculum-aligned prerequisite graph — is genuinely scarce and hard to assemble, which explains the interest. But the author's own disclaimer that nothing has been teacher-reviewed should be taken at face value: treat this as a strong draft to fork and audit, not a drop-in content source. It is worth an afternoon if you need the graph structure; skip it if you need verified items.",{"slug":19,"name":20},"learning","Learning Resources",[22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"chinese","curriculum","diagnostic","edtech","education","elementary-school","english","high-school","junior-high-school","k12","knowledge-graph","math","mathematics","middle-school","open-data","primary-school","NOASSERTION","https:\u002F\u002Fmath.pansgo.com\u002F","2026-10-24T22:05:11Z",[],1792901933]