curriculum-design-guide

Curriculum Design Guide

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.

Backward Design Framework

Understanding by Design (Wiggins & McTighe, 2005) reverses the traditional content-first approach:

Stage 1: Identify Desired Results

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)"

Stage 2: Determine Acceptable Evidence

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))