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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.

7) Evidence Base​

Section 4 requires citations for non-trivial claims, and this protocol is held to the same rule. Strength: Strong = replicated meta-analyses; Moderate = a meta-analysis or several RCTs with caveats; Weak = theory or a single study; Contested = credible counter-evidence exists. Sources checked 2026-10-01.

Protocol ruleEvidenceStrength
Retrieval practice; low-stakes frequent checks (§3)Practice testing beats restudy (g = 0.51) and no activity (g = 0.93) across 217 studies (Adesope, Trevisan & Sundararajan, 2017); Roediger & Karpicke (2006)Strong
Distributed practice over cramming (§3)Cepeda et al. (2006) meta-analysis; Dunlosky et al. (2013) rate practice testing and distributed practice the highest-utility techniquesStrong
Interleaving for transfer (§3)Rohrer & Taylor (2007). Starting novices blocked (AABBCC) is a reasonable reading but less directly tested than interleaving itselfModerate
Cognitive load management; chunking; scaffolding (§2)Sweller's cognitive load theory; worked-example effect (Crissman, 2006, dissertation meta-analysis, d ≈ 0.57); fade support as expertise grows (Kalyuga et al., 2003)Strong
Coherence; remove non-essential material (§2)Mayer, Multimedia Learning (multiple experiments per principle)Strong
3–5 minute micro-lessons (§2)Engagement drops after ~6 minutes across 6.9M edX sessions (Guo, Kim & Rubin, 2014). This measures engagement, not learning, so treat the length as a ceiling, not a learning effectModerate
Mastery gates; foundational mastery before complexity (§6)108 controlled evaluations: positive, lasting effects, largest for weaker students (Kulik, Kulik & Bangert-Drowns, 1990); Bloom (1984)Strong
Progression signals used responsibly (§6)Gamification has small effects: cognitive g = 0.49, motivational 0.36, behavioral 0.25 (Sailer & Homner, 2020)Moderate
Peer assessment with rubrics (§6)g = 0.31 against no assessment; similar to self-assessment (Double, McGrane & Hopfenbeck, 2020)Moderate
Autonomy balanced with scaffolding; self-paced pathways (§1, §6)Self-paced online courses lose most learners: 52% of MOOC registrants never start (Reich & Ruipérez-Valiente, 2019). Time management, metacognition and effort regulation predict online achievement (Broadbent & Poon, 2015). Pathways need planning prompts and visible progressStrong (the risk)
Andragogy (§1)Knowles's assumptions are widely used but mostly untested as causal claims about learning outcomesWeak
Connectivism (§1)A theory of networked learning (Siemens, 2005) with little controlled evidence that designing for it improves outcomesWeak / contested

What this means for contributors: rules rated Strong are requirements. Rules rated Weak are design stances. Keep them, but don't cite them as evidence in learner-facing claims (§4: "do not imply causality from observational studies").

Sources​

  • Adesope, O. O., Trevisan, D. A., & Sundararajan, N. (2017). Rethinking the use of tests. Review of Educational Research, 87(3), 659–701. https://eric.ed.gov/?id=EJ1141817
  • Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make It Stick. Harvard University Press. (Readable synthesis of the retrieval, spacing and interleaving research.)
  • Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education. The Internet and Higher Education, 27, 1–13.
  • Cepeda, N. J., et al. (2006). Distributed practice in verbal recall tasks. Psychological Bulletin, 132(3), 354–380.
  • Crissman, J. K. (2006). The design and utilization of effective worked examples: A meta-analysis (Doctoral dissertation, University of Nebraska). https://digitalcommons.unl.edu/dissertations/AAI3208114/
  • Double, K. S., McGrane, J. A., & Hopfenbeck, T. N. (2020). The impact of peer assessment on academic performance. Educational Psychology Review, 32, 481–509. https://link.springer.com/article/10.1007/s10648-019-09510-3
  • Dunlosky, J., et al. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58.
  • Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement. L@S 2014. https://dl.acm.org/doi/10.1145/2556325.2566239
  • Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31.
  • Kulik, C.-L. C., Kulik, J. A., & Bangert-Drowns, R. L. (1990). Effectiveness of mastery learning programs. Review of Educational Research, 60(2), 265–299.
  • Mayer, R. E. (2021). Multimedia Learning (3rd ed.). Cambridge University Press.
  • Reich, J., & Ruipérez-Valiente, J. A. (2019). The MOOC pivot. Science, 363(6423), 130–131. https://www.science.org/doi/10.1126/science.aav7958
  • Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning. Psychological Science, 17(3), 249–255.
  • Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35, 481–498.
  • Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32, 77–112. https://eric.ed.gov/?id=EJ1245270

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.
  • internal/quality/evidence-vetting-checklist.md
  • internal/quality/peer-review-sop.md
  • docs/safety/shadowwork-safety-standard.md
  • .github/copilot-instructions.md