med-education

name	med-education
description	Use when the user wants to write an education news piece — school policy, research findings, student achievement data, teacher issues, curriculum reform, or campus events — from supplied material (transcripts, press releases, research papers, data, policy documents, interviews). Specializes the parent med-news-reporter skill for the education beat with research-methodology discipline, demographic verification, effect-size auditing, and education-law red lines. Triggers on phrases like 'write up this education story', 'turn this research into a news piece', '整理校園事件成新聞', '寫一篇教育政策新聞', '幫我把這份 108 課綱新聞寫好', 'draft an education research story', '解讀這份 PISA 排名報導'. Do NOT use for press releases (use pr-press-release), school marketing (use mkt-*), or teacher-training content (use tech-teaching or ecom-*)
metadata	{"category":"WP-50 大眾傳播","tags":["news","journalism","education-news","research-methodology","media-ethics"]}

Education News Reporting

This skill specializes med-news-reporter for the education beat. Read med-news-reporter first for the general 6-step workflow (type selection, material audit, fact-check, balance, ethics, literacy). This file adds education-beat-specific discipline on top: research evidence auditing, demographic integrity, effect-size verification, and education-law red lines (兒少法, teacher privacy, curriculum interpretation).

Overview

Distilled from education-journalism curricula at Spencer Foundation, Education Writers Association (EWA), Columbia Journalism School (Education Track), and Taiwan educational institutions (師大新聞系 education track, NTU journalism education reporting). Covers four main education-news sub-types: policy reform / research findings / campus events / student data.

IRON LAW: Effect Size + Population, Not Just "Research Shows"

Education research is widely sensationalized into "study finds X improves Y by Z%".
The LLM tendency is to lead with the headline effect and skip the methodology footer.
Instead: always report (a) effect size (Cohen's d, NNT, % point change), (b) sample size
and demographic (N=500 Taiwan Grade 4, etc.), (c) replication status (single study vs
meta-analysis vs unpublished), (d) source funding (ministry, private foundation, etc.).

This is not optional. A study with d=0.08 is "statistically significant" but educationally
meaningless; a study of 35 suburban Grade 5 students cannot generalize to national policy.
Readers must have this context to judge whether the news is real improvement or noise.

Default LLM failure mode: "A new study shows bilingual education boosts test scores by 12%"
(leading effect, no Cohen's d, no sample demographic, no replication context).

Correct: "A 2024 study of 240 Grade 4 students in Taipei bilingual programs found a 0.6
standard-deviation improvement in reading (Cohen's d=0.6), sustained in a follow-up cohort
but not replicated in rural schools. The National Taiwan University research was funded by
the Language Ministry. Previous international meta-analyses show effect sizes ranging d=0.2
to d=0.5 depending on classroom intensity."

Why this is non-obvious: the headline % is true, the study is real, the writing flows naturally — but the reader cannot judge whether the news is a meaningful education breakthrough or a statistically-significant artifact of a small, unrepresentative sample. This is how education policy gets made on bad evidence.

Rationalization Table — these justifications DO NOT override the Iron Law:

Claude might think...	Why it's still a violation
"The abstract says 'significant improvement', that's enough"	Significance ≠ effect size. A p < 0.05 with N=1,200 and d=0.08 is real but educationally trivial. Always convert to effect size or NNT.
"Adding methodology details makes the story less punchy"	Punchy ≠ misleading. A "punchy" headline with no effect-size footer is how bad education policy gets funded. The footer is the story.
"It's a meta-analysis, so the effect is robust"	Meta-analyses vary wildly (d=0.1 to d=0.6). Always report the range and heterogeneity, not just the aggregate mean.
"The paper is from Stanford/MIT, it must be credible"	Source prestige is not methodology. Stanford studies of n=42 still need effect-size footnotes. Cross-check the paper's own limitations section.
"The policy maker said it works, so it's fine"	Policy makers have incentive to overstate. Cite the independent evaluation's effect size, not the policy maker's claim.
"Single school case studies are human-interest, not policy claims"	Correct. Mark them as anecdote ("one teacher's experience") not systemic trend. "One school tried X and saw better writing" ≠ "X improves writing"

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When to Use

Trigger conditions:

• User supplies education material — 教育部新聞、校園事件、教育研究論文摘要、課程改革公告、升學統計、教師訪談、學生表現數據 — and asks for a news piece.
• User asks for "教育新聞" / "校園報導" / "教育研究新聞" / "education policy story" / "campus event coverage" / "student achievement news".
• User paraphrases: "寫一篇教育政策新聞", "整理校園事件成報導", "幫我解讀這份 PISA 排名", "draft an article on this research finding".

Input signals:

• Named schools, students (or deidentified cohorts), educators, education institutions, student data, curriculum changes, test scores, research findings.
• Education-specific terminology: 教育部、國教院、頂大、全教總、會考、學測、108 課綱、升學率、GPA、PISA、effect size.

When NOT to use:

• School / education institution press release in the institution's own voice → use pr-press-release.
• Teacher training / pedagogy guidance / school-internal communication → use tech-teaching or domain-specific skill.
• Student recruitment / marketing ("discover our innovative bilingual program") → use mkt-*.
• Pure curriculum design or lesson-planning → out of scope for journalism.

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Methodology

Step 0: Defer general workflow to med-news-reporter

Read or have already loaded med-news-reporter for: material audit, fact-checking, source-strength tagging, balance principle, media-ethics check, media-literacy self-check. Do not re-implement those steps here. This file specializes Steps 1–3, adds education-specific Step 3.5 (Research Evidence Audit), and modifies Step 4 (ethics) to include education-specific red lines.

Step 1: Classify the education-story sub-type

Sub-type	Signals	Sub-template focus
Policy reform	教育部公告、課綱改革、考試制度異動、教育經費、教師待遇	Policy text + affected stakeholders (students/teachers/parents) + evidence of impact (if any) + cost source
Research findings	論文摘要、研究機構發布、效果研究、實驗性介入	Effect size + sample demographic + replication status + funding + limitations
Campus events	校園事件、學生表現、教師表揚、學校特色	Deidentify minors; verify with school; avoid generalizing single case to "trend"
Student data / achievement	升學率、考試排名、PISA / TIMSS 結果、學習成果統計	Define the metric (升學率 vs 錄取率 vs 申請成功率); cite official source; note demographic skews

If ambiguous, ask the user — do not guess.

Step 2: Source vetting & demographic tagging

Every education claim involving data or outcomes must carry demographic context at first mention:

• ❌ Vague: 「研究顯示,雙語教育提升閱讀成績。」
• ✅ Specific: 「一項由台灣大學 2024 年進行的研究,針對 240 位台北市雙語班四年級學生,發現閱讀成績提升 0.6 個標準差(Cohen's d=0.6)。」
• ✅ With limitation: 「該研究樣本侷限於市區雙語班;在農村地區試行時效果未再現。」

Education source tier tagging (extends med-news-reporter):

Tier	Examples	Treatment
Public education data	教育部統計、PISA / TIMSS 官方報告、聯招中心數據	Direct citation; verify source year + calculation method
Institutional official	學校發言人、教育局長、大學主任秘書	Name + title; note if statement is preliminary vs final
Researcher / academic paper	論文摘要、研究者本人、教育研究機構	Always extract effect size + sample + replication from paper, not author's summary
Teacher / student	Named educators, named or deidentified students	兒少法 §69 protection; parental consent; no name + school combo
Interest group	教師工會、家長團體、教育評鑑機構	Identify stake; separate fact claims from advocacy positions

Step 3: Education-specific risk check

Beyond med-news-reporter's general ethics check, add:

1. 兒少法 §69 — do NOT disclose name + school + identifying details of minors:

   • ❌ "12 歲的王小華就讀範例國中"
   • ✅ "一位 12 歲女學生" / "示範國中一名四年級男童"
   • Even with parental consent, err toward deidentification.

2. 升學率 / 錄取率口徑混淆:

   • "升學率"(進入高等教育比例) ≠ "錄取率"(申請者中被錄取比例) ≠ "申請成功率"(報考人數 ÷ 錄取人數)
   • Always cite official source (聯招中心、教育部) and definition. If ambiguous in source, flag as [待查證: 升學率定義].

3. 研究結果過度延伸(Goodhart's Law):

   • "One school improved writing with Method X" does not support "Method X should be national policy".
   • "PISA ranking rose 1 position" does not support "reform worked" (within error margin; trends matter more than rank).
   • Mark speculative cause-effect as conditional: "如果..." / "可能" / "若要推廣至全國,須進一步驗證".

4. 教師受訪許可:

   • Teachers employed by schools usually need school approval to speak publicly.
   • 「受訪時曾言及」need confirmation: did the school grant permission? Mark risk if unclear.

5. 校園隱私與實驗倫理:

   • Classroom data (test scores, attendance, behavioral notes) may contain PII.
   • Educational research on students requires IRB approval or school consent + parental consent.
   • If data source unclear, ask before citing.

Step 3.5: Research Evidence Audit (education-specific addition)

For every research-based claim, extract and verify:

1. Effect size: Convert headline claim to Cohen's d, odds ratio, percentage point change, or NNT (number needed to treat).

   • If not in abstract, read Methods + Results. If absent, flag as [待查證: 效果量統計值].
   • Benchmark: education interventions with d < 0.2 are weak; d > 0.5 are strong.

2. Sample size and demographic:

   • Who? (Grade level, school type, region, SES if known, language background for language research).
   • How many? (N = total sample size).
   • If N < 50 or sample is non-representative (private urban school, bilingual cohort only), note as "preliminary evidence" or "context-specific".

3. Replication and consistency:

   • Is this a single published study, a pre-print, a meta-analysis, or a series of replications?
   • If single study: "a study found" not "studies show".
   • If meta-analysis: report heterogeneity (I²), range of effect sizes across studies, and which studies drove the mean.

4. Funding and conflict of interest:

   • Who paid for the research? (Ministry, private foundation, school, textbook company?).
   • Relevant conflict (e.g., a bilingual-program-company-funded study of bilingual-program effectiveness has incentive skew).
   • Disclose in piece: "The research was funded by [source]" or include in demographic tag.

5. Publication status:

   • Peer-reviewed journal > preprint > press release > blog.
   • If data is unpublished or under review, note explicitly: "preliminary findings" / "not yet peer-reviewed".

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Output Format

Use the med-news-reporter base format, with these education additions to the meta footer:

[Headline / sub-headline / body paragraphs per med-news-reporter]

---

**稿件類型**: 教育政策新聞 / 研究新聞 / 校園事件 / 升學新聞
**字數**: approx. XXX
**消息來源層級**: 教育部公開資料 N / 具名教育者 N / 研究論文 N / 學校 N / 利益相關團體 N / 學生/家長 N
**教育專業檢核**:
- 人口統計完整性: ✅ / ⚠️ (列出缺項: 樣本數 / 地區 / 年級 / 家庭背景)
- 效果量稽核: ✅ / N/A / ⚠️ (報告 Cohen's d / 百分點 / 其他指標 + 樣本)
- 研究複製狀態: ✅ / ⚠️ (單一研究 vs 後續複製 vs 後設分析)
- 兒少法 §69 保護: ✅ / ⚠️ (無名字 + 學校組合 / 數據去識別)
- 升學率定義澄清: ✅ / N/A / ⚠️ (列出採用之定義與來源)
**經費與利益揭露**: 〔研究經費來源、利益關係人〕
**待查證事項**: ...
**倫理 / 識讀檢核摘要**: 〔交給 med-news-reporter 的 Step 4-5 footer〕

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Examples

Good Example

Scenario: User supplies (a) 國家教育研究院 2024 年一份教科書閱讀理解研究摘要(樣本 640 名中部六年級學生),報告採用新編版與舊版教科書的效果比較,Cohen's d=0.45,95% CI [0.28, 0.62];(b) 教育部新聞稿回應;(c) 親子天下與報導者過往類似研究的對比。要求寫 900 字教育新聞。

Analysis:

1. Step 1: classified as research findings (研究新聞),焦點是教科書介入的效果證據。
2. Step 2: source tier — 國教院論文屬 Tier 1(academic); 教育部新聞稿屬 Tier 1(institutional); 先前研究為背景參考。
3. Step 3: risk check — 樣本為國小六年級,中部地區,無兒少法問題(因為報告已去識別)。
4. Step 3.5 (Research Evidence Audit):
   • 效果量: Cohen's d=0.45 —— 屬中等效果,教育上有意義。
   • 樣本: N=640, 台灣中部六年級 —— 樣本大,但侷限於一地區、一年級,全國推廣需後續驗證。
   • 複製狀態: 此為首次研究;未來應進行rural區校驗。
   • 經費: 國教院主導,中立來源。
5. Output footer 標示效果量、樣本demographic、複製狀態,以及後續驗證需求。

Result: 讀者清楚知道:改革有evidence support(d=0.45),但證據來自特定地區特定年級,推廣需謹慎與後續評估。

Bad Example

Scenario: Same input. Writer produces piece that (a) leads with "教科書改革提升學生閱讀成績達 12%" without effect size or sample context, (b) omits sample demographic ("中部六年級" → 改為泛稱「台灣學生」), (c) cites 親子天下 過往發現 as "一致證據" without reporting that past study had N=85 and d=0.2 (much weaker), (d) removes methodological footer because "it looks clean".

What went wrong:

• (a) The "12%" headline is 真的,但12 % point ≠ 0.45 Cohen's d。讀者無法判斷是meaningful reform還是統計雜訊。
• (b) Demographic omission makes readers think evidence applies to "all Taiwan students", not "600+ students in Central region". Single-region, single-grade studies do not automatically generalize.
• (c) Treating d=0.45 and d=0.2 as equally "consistent evidence" misleads. The new study is stronger; the historical comparison is less so. Reader needs that hierarchy.
• (d) Removing methodology footer hides the real limitation: this study alone cannot support national policy.

Net:每個句子都technically true,但讀者會高估evidence strength,導致政策決定可能過度樂觀。

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Gotchas

• 「一項研究發現」就開始鬼扯推廣: single study without replication does not support sweeping claims. Distinguish "preliminary evidence" (d=0.4, N=240, one school) from "established finding" (meta-analysis, d=0.3-0.5, multiple regions, 5+ studies). If only one study exists, say so.
• 升學率、錄取率、申請成功率混用是新聞硬傷: 「上榜率 90%」可能意思是「報考人中 90% 至少被某校錄取」而非「錄取人數 ÷ 招生名額 = 90%」。查教育部 / 聯招中心公開定義,不要自己猜。
• PISA 排名變動 1-2 名常在誤差範圍內: 國家排名 year-over-year 波動通常在統計雜訊內; 改革效果需 3-5 年與趨勢觀察,不是單年名次。把trend當rank報導是政策麻木不仁的來源。
• 「校園事件」 ≠ 「教育趨勢」: 一所學校發生的事不代表全國或全市現象。不要把"一個學生寫很好的小說"改寫成"108 課綱提升創意寫作能力"。加「一個案例」的記號;系統主張須系統證據。
• 教師受訪許可風險被低估: 公立教師(或按聘僱契約)通常須學校同意才能公開發言。直接訪問教師而學校不知,可能違反僱傭規約或公務員身分限制。事先確認:學校知道嗎?有沒有同意?
• 兒少法 §69 疏漏導致法律風險: 不得揭露兒少身分(姓名、就讀學校、住址、相片等)。即使有家長同意也建議謹慎;二次傳播時無法控制。學生用代號或deidentify。
• 研究經費來源不揭露會誤導: 「一項研究發現雙語教育更優」未說明是自費教科書公司資助,讀者無法自行judging bias。永遠註明誰付的錢。

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References

File	Purpose	When to read
references/sources_and_beats.md	教育線消息來源、機構、官方資料庫、主要利益相關者	Step 2 source vetting
references/glossary.md	教育專業術語:升學率 vs 錄取率、108 課綱、會考 vs 學測、PISA / TIMSS	When unfamiliar terminology appears
references/ethics_and_law.md	兒少法 §69、校園隱私、教師言論限制、教育資料去識別	Step 3 risk check
references/research_evidence_reading.md	效果量判讀、樣本代表性、複製危機、Goodhart's law in education	Step 3.5 research audit
references/policy_landscape.md	台灣教育制度概覽、近年重大改革(108 課綱、雙語政策、少子化)	Background context

Related skills:

• med-news-reporter — general news workflow (this skill specializes it)
• med-political — for education-policy stories with strong political dimension
• stat-hypothesis-testing — for deep methodological critique of research
• stat-eda — exploratory data analysis on education datasets
• grad-survey-design — for evaluating educational surveys and sampling
• hum-source-criticism — source vetting frameworks

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Limitations

• Does not verify live education databases: this skill flags claims that need verification; it does not query 教育部統計、聯招中心即時數據、或校務評鑑資料。Operator must verify or supply.
• Jurisdictional scope: legal references (兒少法、個資法) reflect Taiwan context. For US (FERPA), EU (GDPR), HK, or PRC, the principles still apply but specific statutes differ — substitute jurisdiction-specific references.
• Not a curriculum-design skill: judging whether a proposed curriculum is pedagogically sound requires subject-matter expertise beyond this journalism skill. This skill flags research claims; does not evaluate curriculum itself.
• Effect-size threshold opinions: guidelines like "d > 0.4 is strong" reflect international benchmarks; Taiwan education ministry may have different policy thresholds. Check ministry guidance for policy-relevant effect-size floors.
• Education-policy lag: reforms typically show effects 3-5 years after implementation. Attributing short-term score changes to new policy is common and often wrong. Flag temporal claims carefully.
• Student recruitment marketing claims disguised as news: some "education stories" from institutions are low-key marketing. If the piece is originated/heavily promoted by the school being featured, disclose the source relationship.