In vocational programming education, teachers are supposed to act as technical coaches, yet most of their time gets swallowed by manually grading large batches of code assignments. To address this, I developed an automated code-quality analysis tool for Java projects and used it throughout the 2024 Spring semester. This paper tracks the same teacher—myself—across two consecutive semesters: the baseline semester (2023–2024-2) with fully manual grading, and the intervention semester (2024–2025-2) supported by the automated system. By analyzing teaching logs, system reports, stimulated-recall interviews, and student assignment samples, the study documents three practical shifts in the teacher's daily work. First, attention moved from inspecting individual lines of code to optimizing the teaching process as a whole. Second, diagnostic thinking moved from scattered impressions of single cases to recognizing class-wide patterns in aggregated data. Third, the nature of feedback moved from corrective annotations to developmental guidance. The core value of the tool lies in acting as a cognitive radar: it lifts the burden of repetitive code review and returns that time to the teacher for data-informed instructional decisions. This creates a systemic feedback loop from assessment back into teaching.

Lei Peng, Kai Yang. From Line-by-Line Checking to Reading the Data: How a Vocational Java Teacher's Grading Practice Changed over Two Semesters. Frontiers in Educational Research (2026), Vol. 9, Issue 7: 51-62. https://doi.org/10.25236/FER.2026.090706.

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