Plano de Conclusão de Curso
PROTransforma cursos online overwhelming em chunks diários alcançáveis de 20 minutos com scheduling inteligente, repetição espaçada e pacing adaptativo. Vence taxa de abandono de 90% de cursos.
Exemplo de Uso
Tenho 5 cursos online começados e nunca termino nenhum. Me ajuda a criar um plano.
Como Usar Este Skill
Copiar o skill usando o botão acima
Colar no seu assistente de IA (Claude, ChatGPT, etc.)
Preencha suas informações abaixo (opcional) e copie para incluir com seu prompt
Envie e comece a conversar com sua IA
Personalização Sugerida
| Descrição | Padrão | Seu Valor |
|---|---|---|
| Total course duration in hours | 10 | |
| Target daily session length in minutes (15-30 recommended) | 20 | |
| Individual pace multiplier (0.7 fast, 1.0 average, 1.3 slow) | 1.0 | |
| Days between review sessions | 1, 7, 21, 45 | |
| Target mastery level percentage for assessments | 75 | |
| Extra time buffer as percentage of total duration | 25 |
Transform overwhelming online courses into achievable 20-minute daily chunks. This AI skill solves course abandonment (90%+ failure rate) by intelligently chunking content, scheduling spaced repetition reviews at scientifically-proven intervals, and adapting to your personal learning pace in real-time.
How It Works
- Analyze your course: Share course details (duration, lectures, topics)
- Get your schedule: Receive a day-by-day learning plan with 15-25 minute chunks
- Follow & adapt: The system tracks your pace and adjusts automatically
- Review strategically: Spaced repetition reviews maximize retention (65% → 85%+)
- Complete successfully: Beat the 90% abandonment rate with sustainable daily commitments
Perfect For
- Busy professionals with limited daily study time
- Online learners who’ve abandoned courses before
- Career changers building new skills
- Anyone facing a long course (10+ hours) feeling overwhelmed
- Multi-course learners needing prerequisite sequencing
Fontes de Pesquisa
Este skill foi criado usando pesquisa destas fontes confiáveis:
- Enhancing human learning via spaced repetition optimization Lindsey et al. PNAS study proving recall probability predicts optimal review timing
- DRL-SRS: Deep Reinforcement Learning for Spaced Repetition Modern DRL method achieving 11% lower error than baseline algorithms
- LECTOR: LLM-Enhanced Concept-based Repetition LLM-powered algorithm achieving 90.2% success rate in vocabulary learning
- Effectiveness of Microlearning and Spaced Repetition 2025 study showing age-specific retention rates across demographics
- Chunking Strategy for Training Practical guide to cognitive load theory and information clustering
- Spaced Repetition Schedule Guide SuperMemo algorithm reference with recommended intervals
- Anki SRS Algorithm Deep Dive SM-2 algorithm implementation with Python code examples
- Adaptive Learning Algorithms in Curriculum Design Real-world adaptive platform architecture and personalized learning paths
- Master Udemy Courses Study Tips Practical Udemy-specific guidance on timeboxing and scheduling
- Mechanisms and Optimization of Spaced Learning Neuroscience perspective on spacing effects and optimal training protocols