study-strategy-selector

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
# AGENT SKILLS STANDARD FIELDS (v2)
name: study-strategy-selector
description: "Select evidence-based study strategies matched to material type, learning goal, and student habits. Use when advising students on revision techniques, homework, or independent study approaches."
disable-model-invocation: false
user-invocable: true
effort: medium

# EXISTING FIELDS

skill_id: "self-regulated-learning/study-strategy-selector"
skill_name: "Study Strategy Selector & Guide"
domain: "self-regulated-learning"
version: "1.0"
evidence_strength: "strong"
evidence_sources:
  - "Dunlosky et al. (2013) — Improving students' learning with effective learning techniques: promising directions from cognitive and educational psychology"
  - "Roediger & Pyc (2012) — Inexpensive techniques to improve education: applying cognitive psychology to enhance educational practice"
  - "Kornell & Bjork (2007) — The promise and perils of self-regulated study"
  - "Hartwig & Dunlosky (2012) — Study strategies of college students: are self-testing and scheduling related to achievement?"
  - "Miyatsu et al. (2018) — Five popular study strategies: their pitfalls and optimal implementations"
input_schema:
  required:
    - field: "learning_task"
      type: "string"
      description: "The specific study task or learning goal"
    - field: "student_level"
      type: "string"
      description: "Age/year group and current study habits"
    - field: "material_type"
      type: "string"
      description: "Type of material: factual/conceptual/procedural/mixed"
  optional:
    - field: "time_available"
      type: "string"
      description: "How much study time the student has"
    - field: "assessment_type"
      type: "string"
      description: "What they're studying for: exam, essay, presentation, practical"
    - field: "student_profiles"
      type: "array"
      description: "From context engine: current study habits, academic performance data"
    - field: "subject_area"
      type: "string"
      description: "Subject context for domain-specific strategy adaptations"
output_schema:
  type: "object"
  fields:
    - field: "recommended_strategies"
      type: "array"
      description: "Ranked list of strategies with evidence rating, implementation guide, and time allocation"
    - field: "strategies_to_avoid"
      type: "array"
      description: "Common but ineffective strategies students should replace"
    - field: "study_plan"
      type: "string"
      description: "A concrete study plan applying the recommended strategies to this specific task"
    - field: "student_guide"
      type: "string"
      description: "Copy-pasteable student-facing strategy guide"
chains_well_with:
  - "retrieval-practice-generator"
  - "spaced-practice-scheduler"
  - "metacognitive-prompt-library"
  - "goal-setting-protocol-designer"
teacher_time: "3 minutes"
tags: ["study-skills", "learning-strategies", "revision", "self-regulation", "metacognition"]
---

# Study Strategy Selector & Guide

## What This Skill Does

Analyses a specific learning task and recommends the most evidence-supported study strategies, with an explicit implementation guide for each. Crucially, the skill also identifies ineffective strategies the student is likely using (highlighting, re-reading, copying notes) and provides specific replacement strategies with the evidence rationale. AI is specifically valuable here because students overwhelmingly default to the least effective study strategies — Kornell & Bjork (2007) found that the most popular strategies (re-reading, highlighting) are rated "low utility" by research, while the most effective strategies (retrieval practice, distributed practice) are the least used. This skill encodes Dunlosky et al.'s (2013) landmark review into actionable, task-specific guidance.

## Evidence Foundation

Dunlosky et al. (2013) conducted the most comprehensive review of study strategies ever published, systematically evaluating ten techniques against four criteria