Plan de Finalización de Curso
PROTransforma cursos online abrumadores en chunks diarios alcanzables de 20 minutos con scheduling inteligente, repetición espaciada y pacing adaptativo. Vence la tasa de abandono del 90% de cursos.
Ejemplo de Uso
Compré un curso de Python de 40 horas y llevo 3 meses sin avanzar. Créame un plan realista de 20 minutos diarios para terminarlo.
Cómo Usar Este Skill
Copiar el skill usando el botón de arriba
Pegar en tu asistente de IA (Claude, ChatGPT, etc.)
Completa tus datos abajo (opcional) y copia para incluir con tu prompt
Envía y comienza a chatear con tu IA
Personalización Sugerida
| Descripción | Por defecto | Tu 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
Fuentes de Investigación
Este skill fue creado usando investigación de estas fuentes autorizadas:
- 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