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Evidence-Based Instructional Design Protocol

Evidence-Based Instructional Design Protocol​

This document is the canonical design and presentation protocol for contributors and Copilot when creating or revising learning content in this repository.

Purpose​

  • Ensure objective, unbiased, evidence-based course development.
  • Align design choices with adult learning science and cognitive psychology.
  • Standardize quality, safety, and accessibility practices across all modules.

1) Strategic Foundations​

Core stance​

  • Use evidence-based instructional design, not intuition-first teaching.
  • Measure success by instructional attainment, transfer, and long-term retention.
  • Treat adult learner relevance and application as primary constraints.

Pedagogical anchors​

  • Andragogy: self-direction, relevance, problem-solving.
  • Connectivism: networked learning, peer knowledge construction, non-linear pathways.

Design implication​

  • LMS pathways must balance autonomy and scaffolding.
  • Avoid purely linear, instructor-led progression when learner autonomy is required.

2) Cognitive Science and Presentation Protocol​

Cognitive load management (CLT)​

  • Optimize intrinsic and germane load.
  • Minimize extraneous load in content, interface, and navigation.
  • Use chunking and scaffolding.
  • As a rule of thumb, provide assimilation time during online instruction.

Multimedia standards (Mayer-aligned)​

  • Coherence first: remove non-essential visuals/text.
  • Prioritize high-signal, low-distraction presentations.
  • Maintain active processing over passive consumption.

Microlearning and video standards​

  • 3-5 minute micro-lessons.
  • One concept per lesson.
  • Key message appears early.
  • In-line interaction for active recall; lock progression for critical checks when appropriate.

Accessibility (WCAG 2.2)​

  • Perceivable, Operable, Understandable, Robust.
  • Provide alt text, captions/transcripts, and contrast/readability support.
  • Treat accessibility as cognitive-load reduction, not only compliance.

3) Retention Architecture​

Retrieval practice​

  • Require active recall tasks in each module.
  • Prefer low-stakes frequent checks over passive review.

Spacing and interleaving​

  • Use distributed practice over cramming.
  • Start novice flows with lower switching cost (for example, AABBCC).
  • Move to higher interleaving for advanced transfer (for example, ABCABC).

LMS enforcement​

  • Use activity completion and restricted access gates to enforce retrieval loops.

4) Content Objectivity and Rigor​

Verification and claims discipline​

  • Validate claims against originating sources.
  • Do not imply causality from observational studies.
  • Flag hype terms and overstatements.
  • Require citations for non-trivial claims.

Bias control​

  • Require external review for complex claims.
  • Explicitly include counter-evidence and competing interpretations.
  • For controversial topics, guide perspective-taking and synthesis.

Academic integrity​

  • Design hard-to-plagiarize assessment prompts.
  • Maintain citation and attribution standards.
  • Use analytics to flag integrity anomalies.

5) Assessment Quality Protocol​

Reliability and validity​

  • Align assessment to target outcomes and Bloom level.
  • Standardize administration and rubric interpretation.

Performance-based assessment preference​

  • Prioritize application, reasoning, reflection, and transfer.
  • Use clear rubrics and criterion-level descriptors.

Bias mitigation in grading​

  • Grade blind where feasible.
  • Use multiple assessors for complex work when possible.
  • Monitor stereotype-threat risks and language impacts.

6) Digital Environment and Community​

Personalized pathways​

  • Use completion gates and adaptive sequencing.
  • Ensure foundational mastery before advanced complexity.

Collaboration and peer assessment​

  • Define measurable objectives for collaborative work.
  • Use netiquette and moderation standards.
  • Support peer assessment with clear rubrics.

Motivation and engagement​

  • Use meaningful progression signals (badges/leaderboards) responsibly.
  • Focus on intrinsic motivation and relevance over novelty effects.

Required Contribution Checklist​

Contributors and Copilot must verify all of the following before merge:

  • AQAL mapping is explicit (quadrant, level, line, state, type).
  • Module structure includes Learn, Practice, Reflect, Assess, Integrate.
  • Retrieval loop (spaced and interleaved) is present.
  • Evidence quality and citations are documented.
  • Safety rules are included for emotionally intense practices.
  • Accessibility requirements (WCAG 2.2) are satisfied.
  • docs/quality/evidence-vetting-checklist.md
  • docs/quality/peer-review-sop.md
  • docs/safety/shadowwork-safety-standard.md
  • .github/copilot-instructions.md