skills/43-wentorai-research-plugins/skills/domains/education/curriculum-design-guide/SKILL.md
Systematic approaches to curriculum design using backward design and alignment
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research curriculum-design-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A structured skill for designing research-informed curricula using backward design, constructive alignment, and competency-based frameworks. Applicable to higher education course design, training program development, and educational research.
Understanding by Design (Wiggins & McTighe, 2005) reverses the traditional content-first approach:
Define what students should know, understand, and be able to do:
course: "Introduction to Research Methods"
big_ideas:
- "Research is a systematic process of inquiry"
- "Methodology must align with research questions"
essential_questions:
- "How do we know what we know?"
- "What makes evidence credible?"
- "When should we use qualitative vs. quantitative methods?"
learning_outcomes:
- "Formulate testable research questions (Apply)"
- "Select appropriate research designs for given questions (Evaluate)"
- "Critically appraise published research methodology (Analyze)"
- "Design and defend a research proposal (Create)"
Design assessments before planning instruction:
# Assessment blueprint generator
def create_assessment_blueprint(outcomes: list[str], bloom_levels: list[str],
weights: list[float]) -> dict:
"""
Generate an assessment blueprint mapping outcomes to
assessment types and weights.
"""
assessment_types = {
'Remember': 'quiz',
'Understand': 'reflection_paper',
'Apply': 'problem_set',
'Analyze': 'case_study',
'Evaluate': 'peer_review',
'Create': 'research_proposal'
}
blueprint = []
for outcome, level, weight in zip(outcomes, bloom_levels, weights):
blueprint.append({
'outcome': outcome,
'bloom_level': level,
'assessment_type': assessment_types.get(level, 'portfolio'),
'weight_pct': weight * 100
})
return {'blueprint': blueprint, 'total_weight': sum(weights) * 100}
outcomes = [
"Formulate research questions",
"Select research designs",
"Appraise methodology",
"Design research proposal"
]
levels = ['Apply', 'Evaluate', 'Analyze', 'Create']
weights = [0.15, 0.20, 0.25, 0.40]
print(create_assessment_blueprint(outcomes, levels, weights))
Sequence activities that build toward assessment readiness. Use the WHERETO framework:
Biggs' Constructive Alignment (1996) ensures coherence between intended learning outcomes (ILOs), teaching/learning activities (TLAs), and assessment tasks (ATs):
ILO: "Students will analyze case studies using SWOT framework"
|
+--> TLA: Workshop where students collaboratively analyze
| a real company case in small groups
|
+--> AT: Individual case analysis report (1500 words)
assessed with rubric mapping to ILO verbs
Misalignment is the most common curriculum design failure. Audit each ILO to verify it has at least one matching TLA and one matching AT.
For programs with multiple courses, create a curriculum map:
Competency | Course 1 | Course 2 | Course 3 | Course 4
------------------------|----------|----------|----------|--------
Research question design| I | D | M | A
Literature review | I | D | D | M
Data collection | - | I | D | M
Statistical analysis | - | I | D | A
Academic writing | I | D | D | A
Legend: I = Introduced, D = Developed, M = Mastered, A = Applied
Ensure every competency reaches at least "Mastered" level by program completion, and identify gaps where competencies are introduced but never developed further.
Validate curriculum designs through:
Document all revisions in a curriculum changelog to maintain institutional memory and support accreditation reporting.
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.