Learning Systems That Learn — Visualizing Design Intelligence for Every Course
modulr.blueprint dashboard™
Course
Entering and Excelling in Juried Portrait Competitions
Organization
paint moto
Status
Ready
DESIGN RATIONALE — WHAT DID WE PRIORITIZE AND WHY
content priority
Competition alignment and submission compliance—because the learner’s primary risk is wasting time and money on misaligned competitions.
instructional priority
Scenario-based judgment practice—because the learner faces recurring trade-offs (fit vs. ambition, authenticity vs. rules).
design constraint
Extremely limited learner time—requiring simplified workflows, checklists, and low-friction decisions.
design drivers
Behavioral
Difficulty identifying competitions of appropriate rigor
Risk of discouragement from misaligned rejections
No art-world network or guidance
operational
Misreading or overlooking submission rules
Lack of time for reviewing guidelines or preparing materials
design overview
Audience
Re-entry expressionist portrait painter (50-year-old, full-time non-art job). Works independently with no gallery contacts. Limited time, minimal peer feedback, and high uncertainty around competition landscape. Goal: identify and submit to three aligned juried portrait competitions.
content
This course guides a re-entry expressionist portrait painter through the full process of entering juried competitions with confidence and efficiency. It begins by teaching how to identify competitions that genuinely match the learner’s style and career stage, then builds skill in decoding the fine print—size limits, framing rules, themes, and other disqualifiers that commonly trip up new entrants. Finally, it shows how to create polished, compliant submission packages that balance artistic authenticity with competition requirements. Across all modules, the course is designed for a time-limited learner and emphasizes strategic selection, accurate guideline interpretation, and clear, compelling presentation of artwork.
objectives
Learners will:
Identify aligned juried competitions → Modules 1–3.
Interpret call-for-entry requirements → Modules 1–2.
Construct compliant, compelling submission packages → Module 3.
Context-Drive Instruction™ Insights
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Learner success depends on matching expressionist portrait work to appropriate competitions.
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Fine-print interpretation is essential to avoid preventable rejection.
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Limited evening hours require efficient filtering and reusable workflows.
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Learner must balance artistic integrity with competition requirements.
Strong Context and Andragogy anchor the design. Merrill, Bloom, Gagné, and CLT appear in moderate support roles through problem-centered scenarios, evaluation tasks, and structured workflows.
Module-Level Framework Overview
Module 1: High Context + Andragogy; moderate Merrill/Bloom/CLT.
Module 2: High Context; moderate Merrill/Gagné; strong CLT via rule decoding.
Module 3: High Context; moderate Bloom (evaluate/choose); moderate CLT via templates and accuracy.
Learning Frameworks
Modulr.Context: Scenario anchors tied to home studio, limited time, and paint moto expectations.
Merrill: Problem-centered tasks—misalignment, fine-print conflicts, artwork selection.
Bloom: Analysis and evaluation dominate judgment prompts.
Gagné: Attention via realistic intros; practice through decision dilemmas; feedback embedded.
Knowles: High relevance and autonomy; directly tied to learner’s constraints and goals.
CLT: Streamlined steps, checklists, and simplified comparisons to reduce overload.
How This Course Differs from Generic Training
DESIGN Emphasis
Focused entirely on real-world dilemmas faced by a time-limited re-entry painter, not generic competition advice.
Design Insights
• Learner performance hinges on preventing misalignment errors, not artistic skill.
• Time scarcity shapes every instructional choice—checklists outperform long explanations.
• Fine-print decoding is a core competency that differentiates successful submissions.
• Authenticity remains a strategic asset but only when anchored to explicit competition criteria.
Module Highlights
Module 1: Scenario of sorting real calls-for-entry; central challenge—identifying true stylistic fit.
Module 2: Scenario involving size/framing conflict; challenge—spotting non-negotiables.
Module 3: Scenario of selecting one representative painting; challenge—balancing authenticity and criteria.
Downloads
next steps
Review → Approve → Build → Integrate into LMS.
Option: Request Dashboard Walkthrough for SME artifact alignment.
Team Rating (Pending): ⭐⭐⭐⭐☆
Final feedback summary to be added once implementation is complete.
Metadata and Footer
Generated By: Modulr.Outline v1.4.1
Based On: Modulr.Context v2 and Modulr.AI Behavioral Rules v1.2.5
Compliance Check: ✔ Tier 1 Behavioral Rules
Review Notes: [auto-filled or left blank]
Blueprint Version: 1.0