error-analysis-protocol

Classify student errors and generate targeted instructional responses.

Updated Jun 14, 2026
One-click install
npx skills add https://github.com/vvieira010-pixel/education-agent-skills --skill error-analysis-protocol-vvieira010-pixel
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: error-analysis-protocol
Source: https://github.com/vvieira010-pixel/education-agent-skills/tree/main/Users/vviei/education-agent-skills-main/skills/self-regulated-learning/error-analysis-protocol
Command: npx skills add https://github.com/vvieira010-pixel/education-agent-skills --skill error-analysis-protocol-vvieira010-pixel

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps educators move beyond simply marking mistakes by diagnosing the root causes of student errors and matching feedback to the underlying misconception, procedure issue, or execution slip.

Core Features & Use Cases

  • Error Classification: Identifies whether student mistakes are conceptual, procedural, or careless based on evidence from student work.
  • Diagnostic Guidance: Generates teacher questions, root cause hypotheses, and targeted responses to uncover student thinking.
  • Use Case: A teacher reviewing incorrect mathematics solutions can use this Skill to determine whether a learner misunderstood a concept, applied a method incorrectly, or made an isolated calculation mistake, then plan the appropriate intervention.

Quick Start

Ask the error-analysis-protocol skill to analyse a student's work sample, task description, and subject area to identify error types and recommend targeted feedback.

Frequently Asked Questions about error-analysis-protocol

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I diagnose the root causes of student errors from work samples?

Error analysis classifies student mistakes as conceptual, procedural, or careless execution slips by examining work samples against task contexts and learning objectives. This diagnostic process identifies root causes and generates targeted feedback to address specific misconceptions.

What is the best way to provide targeted feedback for student misconceptions?

Targeted feedback is generated by diagnosing whether a mistake stems from a misunderstood concept, an incorrectly applied method, or an isolated calculation error. You then match instructional responses and teacher questions directly to that specific root cause.

How do I classify student mistakes as conceptual, procedural, or careless?

You classify student mistakes by analyzing evidence from work samples against the task description and subject area. This structured review identifies whether errors stem from flawed understanding, incorrect method application, or isolated execution slips.

Can I use formative assessment error analysis to support self-regulated learning?

Yes, formative assessment error analysis supports self-regulated learning by generating diagnostic guidance and teacher questions that help students uncover their own thinking. This targeted feedback moves beyond marking mistakes to address underlying misconceptions.

What do I need to provide for a student work review and misconception identification?

You need to provide a structured student work sample, the task description, and the relevant learning objectives. These inputs allow the diagnostic protocol to classify errors accurately and produce targeted instructional guidance.

Why does marking student mistakes fail to improve learning outcomes?

Marking mistakes fails because it skips error classification and root cause diagnosis. Without identifying whether an error is conceptual, procedural, or a careless slip, feedback cannot target the specific misconception or procedure issue hindering the student.