This paper examines traceability and compliance monitoring systems in global apparel supply chains through a structured stage-wise evaluation framework. The analysis focuses on supplier documentation, product traceability records, compliance checkpoints, and exception visibility across sourcing, production, inspection, warehousing, and distribution. Five analytical variables are used in the study: document completeness, traceability continuity, checkpoint coverage, exception visibility, and compliance response status. Two evaluation measures summarize system performance: the Traceability Coverage Ratio (TCR) and the Compliance Monitoring Effectiveness Index (CMEI). The results show that upstream stages, especially supplier qualification and sourcing, maintain stronger documentation and more continuous traceability links. In contrast, warehouse and distribution stages show weaker linkage, lower monitoring coverage, and reduced visibility of exceptions. The findings indicate that the main problem is not the absence of records, but the weak connection among records across supply chain stages. Fragmented data structures reduce visibility, weaken accountability, and limit compliance review. The paper presents a structured method for assessing traceability systems and identifies key gaps in cross stage integration within apparel supply chains.

Programming courses at the college level in Vietnam continue to suffer from high failure rates, early drop-out, and incomplete student projects. This paper presents Vibe Coding, a four-stage pedagogical model integrating AI coding assistants into Fundamentals of Programming and Basic Web Design. The model was evaluated using a hybrid quasi-experimental design combining a three-year historical trend (2023–2025, n = 735) with a concurrent control group in the 2025–2026 academic year (232 traditional vs. 150 Vibe Coding students). The Vibe-Coding group significantly outperformed the traditional group on the proportion scoring ≥7.0/10 (53.3% vs. 41.8%; z = 2.21, p = .027) and on course pass rate (83.3% vs. 67.2%; z = 3.48, p < .001), and also exceeded the three-year historical baseline (39.7%; z = 3.08, p = .002), while the traditional group did not differ from that baseline. However, this overall effect was driven almost entirely by Fundamentals of Programming (z = 3.13, p = .002); in Basic Web Design, Vibe Coding did not outperform traditional instruction on the merit measure (z = -0.24, p = .811), with only a non-significant favourable trend on pass rate (z = 1.66, p = .097). Class-level variation was substantial, including one traditional section that improved by 40 percentage points without the intervention. Despite this course-level heterogeneity, over 85% of surveyed students reported greater engagement and confidence in debugging. The findings indicate that Vibe Coding can meaningfully improve programming outcomes, but its benefits are course-dependent and require further validation through randomized, multi-institutional studies.