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.

Social media has become deeply integrated into students’ daily lives, extending beyond social use into informal academic practices, including self-assessment. This study examines how EFL students at a university use social media to assess their own academic abilities, the extent to which they engage in academic social comparison through these platforms, and the difficulties they encounter in doing so. A mixed-methods approach was employed, using an online questionnaire comprising closed and open-ended questions administered to 133 students. Quantitative findings revealed that the majority of students used social media for academic self-assessment on a frequent basis, with YouTube and WhatsApp emerging as the most commonly used platforms. While social comparison was a common practice among students, it was not strongly associated with increased motivation or academic confidence. Qualitative findings further revealed that students primarily used social media for comprehension checks, language practice, and peer feedback, while also reporting challenges related to distraction, unreliable information, and negative social comparison. These findings suggest that social media functions as an informal, self-directed space for academic self-assessment, one that offers meaningful opportunities for reflection and feedback but also carries risks that may undermine effective learning if left unaddressed. The study is limited by its focus on EFL students at a single university and its reliance on self-reported data, and future research should extend to larger, more diverse samples across multiple institutions and disciplines. The findings nonetheless highlight the need for greater digital literacy support and suggest that educators consider ways to intentionally integrate informal social media practices into formal academic guidance.

Losses of data are a widespread issue of the real-world tabular data, utilized in machine learning (ML). Missing values may dramatically hamper the quality of the model, be biased, and result in incorrect inferences unless addressed correctly. This is a systematic literature review (SLR) that explores and syntheses 52 research articles published 2020-2026 in high-impact peer review journals. The review is done under the guidelines of PRISMA (Preferred Reporting Items to Systematic Reviews and Meta-Analyses). Methods of imputation can be divided into 5 broad categories: statistical and conventional imputation methods (mean, median, mode, and Last Observation Carried Forward), machine learning-based methods (k-Nearest Neighbors, Random Forest, Decision Trees and Support Vector Machines), multiple imputation methods (including MICE, missForest and missRanger), deep learning-based methods (including Autoencoders, Vari There is a systematic comparison between methods based on type of dataset, missing data mechanism (MCAR, MAR, MNAR), evaluation measures (RMSE, MAE, accuracy, AUC), computational complexity and scalability. Using our results, it appears that, up to low missingness rates, conventional approaches are equally competitive, but that deep generative models (with GAN-based models or diffusion-based models being two different approaches to the same task) are matched when applied to high-dimensional and heterogeneous tabular data. However, there is no one particular approach that prevails in all situations. This review finds the overall gaps in research, such as the absence of standardized benchmarks, the relative dearth of interest in MNAR mechanisms, and the lack of research on imputation in federated learning. The results give practical advice to practitioners and researchers to use the right imputation techniques when using tabular ML tasks.