Instructor-Led Versus Automated Feedback in Cloud Security Laboratories: A Controlled Evaluation of Configuration Error Correction
Abstract
Cloud security laboratories can provide immediate automated feedback, but instructors may identify contextual errors that automated checks miss. We compared instructor-led and automated feedback in a controlled evaluation involving 102 participants completing six cloud-security configuration laboratories. Tasks covered network policy, IAM, secrets handling, logging, encryption, and misconfiguration remediation. Participants were assessed on first-attempt accuracy, time to correction, recurrence of the same error class, and performance on a final unseen task. Automated feedback shortened time to correction for syntax and policy-format errors, whereas instructor feedback produced larger reductions in recurring conceptual errors. Final-task accuracy was similar overall, but participants who received instructor feedback performed better on permission-inheritance and threat-model reasoning. Automated feedback generated consistent guidance and allowed faster iteration, while instructors varied in response time and level of detail. A combined model, in which automated checks handled deterministic errors and instructors focused on reasoning-intensive issues, achieved the best efficiency in a secondary analysis. Feedback design should therefore match the type of error being corrected rather than assuming one delivery mechanism is universally superior.
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