Error Analysis Protocol

What This Skill Does

The protocol distinguishes between three error types: procedural (incorrect method application), conceptual (fundamental misconception), and careless (execution slip despite correct understanding). Research demonstrates that "errors followed by corrective feedback produce stronger learning than errorless learning" due to prediction violation deepening encoding. However, each error type requires fundamentally different instructional responses—re-teaching for conceptual errors, guided practice for procedural errors, and metacognitive monitoring for careless mistakes.

Evidence Foundation

Research establishes that errors, properly analyzed rather than simply corrected, become powerful learning mechanisms. Borasi's work demonstrated errors as diagnostic windows into student thinking. Black & Wiliam identified error analysis as central to formative assessment's diagnostic power. Metcalfe's review confirms learning gains from error analysis. Siegler's microgenetic research showed mathematical development depends on understanding why incorrect strategies fail. Tulis et al. modeled productive error processing through three components: error detection, attribution, and correction strategy—all teachable through systematic analysis.

Evidence sources:

• Borasi (1994) — Errors as "springboards for inquiry" in teaching experiments
• Black & Wiliam (1998) — Assessment and formative classroom learning through error use
• Metcalfe (2017) — "Learning from errors: benefits of errors in the classroom"
• Siegler (2002) — Microgenetic studies on mathematical understanding development
• Tulis et al. (2016) — Individual error processing and learning models

Input Schema

Required:

• student_work_sample: Description of work containing errors
• task_description: What student was asked to do and learning objective
• subject_area: Subject and year group

Optional:

• correct_response: Model correct response for comparison
• student_profiles: Prior attainment, learning difficulties, error history
• rubric: Success criteria for the task
• error_frequency: One-off or recurring pattern indicator

Output Schema

• error_classification: Procedural, conceptual, or careless errors with evidence
• root_cause_analysis: Hypothesized cause with diagnostic questions
• targeted_response: Specific follow-up actions per error type
• student_self_analysis_guide: Scaffolded prompts for learner reflection

Known Limitations

1. Error classification requires working samples, not isolated answers. Diagnostic questioning is essential for accurate classification.

2. This skill addresses individual errors; whole-class patterns require instructional redesign through class-level diagnostic approaches.

3. Selective application is necessary given time constraints—use for persistent, surprising, or shared errors rather than comprehensive analysis of every student mistake.

Chains well with: feedback-quality-analyser, gap-analysis-from-student-work, metacognitive-prompt-library, worked-example-fading-designer