Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation. This paper evaluates whether four frontier Large Language Models (GPT, Claude Opus, Gemini, and GLM) can approximate expert judgment when grading short Linux/bash command responses. The study adopts a four-level cognitive taxonomy that combines cognitive complexity and operational impact, ranging from information retrieval (L1) and basic
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- Linked via arxiv authorManuel Alonso-Carracedo →
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
- Linked via arxiv authorRuben Fernandez-Boullon →
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
- Linked via arxiv authorPedro Celard →
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
- Linked via arxiv authorFrancisco J. Rodriguez-Martinez →
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
- Linked via arxiv authorLorena Otero-Cerdeira →
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach