worked-example-fading-designer
Worked Example Designer with Completion Fading
What This Skill Does
Designs a complete scaffold sequence that moves students from studying a fully worked example through progressively faded completion problems to independent practice. For a given procedure or skill, it produces: (1) a worked example with annotated reasoning at each step, (2) a series of completion problems where successive steps are removed, and (3) independent practice problems. AI is specifically valuable here because effective worked examples require expert-level annotation of reasoning (not just showing steps, but explaining why each step is taken), and the fading sequence requires careful calibration of which steps to remove and in what order — a task requiring deep knowledge of both the subject content and the cognitive load research.
Evidence Foundation
Sweller & Cooper (1985) demonstrated that novice learners who studied worked examples learned more effectively than those who attempted problem-solving, because worked examples reduce extraneous cognitive load — students can focus on understanding the procedure rather than searching for a solution path. Atkinson et al. (2000) synthesised the worked examples research and identified key design principles: examples must include explanatory annotations (not just steps), and the transition from examples to independent practice should be gradual. Renkl (2014) refined the theory, showing that self-explanation prompts embedded in worked examples significantly enhance learning because they promote germane processing. The fading approach — where worked examples gradually omit steps, creating "completion problems" — was shown by van Merriënboer & Kirschner (2018) to be more effective than an abrupt transition from examples to problems. Critically, Kalyuga et al. (2003) demonstrated the expertise reversal effect: worked examples that help novices become counterproductive for advanced learners, who learn better from problem-solving. This means fading must be calibrated to student expertise.
Input Schema
The teacher must provide:
- Skill to teach: The specific procedure or skill. e.g. "Solving simultaneous equations by elimination" / "Writing a topic sentence for an analytical paragraph" / "Balancing chemical equations"
- Student level: Year group and expertise. e.g. "Year 9, first encounter (novice)" / "Year 11 revision (developing)"
- Steps in procedure: Approximate number of steps. e.g. 5
Optional (injected by context engine if available):
- Common errors: Known errors students typically make. e.g. ["Forgetting to multiply both sides", "Sign errors when subtracting equations"]
More from garethmanning/claude-education-skills
intelligent-tutoring-dialogue-designer
Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.
15scaffolded-task-modifier
Modify a classroom task with language scaffolds that preserve cognitive demand for EAL learners. Use when adapting existing tasks for students at different English proficiency levels.
14experiential-learning-cycle-designer
Structure a direct experience into a full learning cycle with concrete experience, reflection, and conceptual transfer. Use when planning field trips, simulations, or practical tasks.
14gap-analysis-from-student-work
Analyse student work against criteria to identify specific gaps between current performance and learning objectives. Use when reviewing submissions, planning feedback, or diagnosing learning needs.
13backwards-design-unit-planner
Plan a unit using backwards design from desired outcomes through assessment evidence to learning activities. Use when starting a new unit or redesigning an existing one from standards.
13dual-coding-designer
Design a visual complement to verbal content using dual coding principles for stronger encoding. Use when creating slides, diagrams, posters, or visual explanations of complex concepts.
12