The rapid evolution of digital economies has intensified the demand for advanced vocational competencies, particularly in digital content production at competency Level 3 in Thailand. This study proposes a Smart Learning Media Architecture (SLMA) designed to optimize occupational skill development through adaptive, data-driven, and competency-aligned learning systems. The architecture integrates intelligent learning analytics, experiential pedagogy, and competency-based education frameworks to enhance learner performance in vocational education and training (VET) contexts. Drawing on established theoretical foundations such as experiential learning theory (Kolb, 1984), competency-based assessment (Boyatizis, 1982), and problem-based learning models (Barrows & Tamblyn, 1980), the study constructs a multi-layered instructional system incorporating adaptive content delivery, learner modeling, and performance evaluation modules.
A design science research methodology is employed to conceptualize, develop, and evaluate the SLMA framework. Data-driven optimization strategies are embedded using educational data mining principles inspired by Berry & Gordon (2004), enabling the system to analyze learner behavior and improve instructional efficiency. The evaluation framework assesses usability, competency gain, and learning effectiveness in vocational digital content training environments. Findings suggest that the proposed architecture significantly enhances learner engagement, skill acquisition, and adaptive learning pathways. The study contributes to the integration of intelligent systems in vocational education, offering a scalable model for digital skills development in emerging economies.

The rapid transformation of the hospitality industry under the influence of digital technologies has created a critical need for structured mechanisms to evaluate employee competency in service delivery processes. This study proposes an intelligent, data-driven model for assessing digital competency in hotel turndown service operations, integrating competency-based education principles with hospitality performance analytics. The research is grounded in the increasing importance of digital literacy and performance evaluation systems within vocational and service-oriented sectors, where precision, consistency, and customer satisfaction are central performance indicators (Pettersson, 2018; Røkenes & Krumsvik, 2014).
The proposed model synthesizes competency-based assessment frameworks, digital performance tracking systems, and hospitality service quality indicators to create an integrated evaluation architecture. Drawing on established research in competency-based education (Curry & Docherty, 2017) and performance assessment systems (Sadler, 2005), the study conceptualizes a structured mechanism for evaluating turndown service staff using data-driven metrics and rubric-based evaluation tools. The model also aligns with digital transformation practices in vocational education and training (Wardina et al., 2019), emphasizing the integration of real-time feedback systems and structured assessment protocols.
Methodologically, the study employs a conceptual design approach supported by systematic synthesis of literature from hospitality management, vocational education, and digital assessment systems. The findings indicate that intelligent competency models significantly enhance accuracy in performance evaluation, improve consistency in service delivery, and support continuous professional development. The study concludes that data-driven assessment systems can bridge the gap between traditional hospitality training and modern digital competency requirements, offering scalable solutions for industry adoption.

The rapid transformation of industrial ecosystems under the Fourth Industrial Revolution (4IR) has significantly reshaped technical and vocational education and training (TVET), particularly in civil engineering disciplines. This study develops a comparative analytical framework to assess the evolution of occupational training practices among TVET civil engineering students in the modern era. The research synthesizes global and regional literature to examine how technological disruption, competency-based education, and labor market demands are redefining skill development pathways. Using a qualitative comparative design informed by secondary data analysis, the study critically evaluates shifts in pedagogical approaches, workplace integration, and competency alignment with industry expectations.

Findings suggest that occupational training in TVET civil engineering has transitioned from traditional task-based instruction to integrated, technology-enhanced, and industry-responsive models. However, gaps persist in curriculum-industry alignment, digital infrastructure readiness, and instructor capability development. The study highlights that 4IR technologies, including automation, digital modeling, and intelligent construction systems, are increasingly central to training frameworks (Xu, David, & Kim, 2018). Despite these advancements, disparities between policy intentions and institutional implementation remain a key concern in developing contexts.

The proposed framework contributes to enhancing comparative evaluation of TVET systems by integrating competency mapping, industry feedback loops, and digital skill integration indicators. The study offers implications for policymakers, curriculum designers, and institutional leaders aiming to modernize occupational training systems in civil engineering education.

• The COVID-19 pandemic triggered an unprecedented disruption in Technical and Vocational Education and Training (TVET) systems worldwide, necessitating rapid pedagogical transformation, digital integration, and reconfiguration of teaching–learning ecosystems. This study proposes an evaluation model for assessing pandemic-driven transformation in TVET pedagogical practices, focusing on a cross-national comparative context between Indonesia and Malaysia. The research synthesizes digital transformation theories, blended learning frameworks, and online education readiness models to construct a multi-dimensional evaluation structure encompassing institutional readiness, pedagogical adaptability, technological infrastructure, and learner engagement.
  Drawing upon prior studies on digital education transformation and COVID-19 educational disruption (Bogdandy, Tamas, & Toth, 2020), this study positions TVET systems within the broader paradigm of Industry 4.0-aligned education ecosystems. The methodology is grounded in a conceptual and analytical modeling approach supported by structured literature synthesis from recent empirical and theoretical studies. The findings suggest that both Indonesia and Malaysia demonstrate partial but uneven digital readiness, with Malaysia exhibiting relatively stronger synchronous learning integration and Indonesia showing broader adoption challenges but higher flexibility in informal digital adaptation strategies.
  The proposed evaluation model provides a structured framework for assessing TVET resilience, adaptability, and sustainability in crisis contexts. It contributes to policy formulation, institutional benchmarking, and future digital transformation strategies in vocational education systems across developing economies.
  
  
  
  

Language learning anxiety has been widely recognized as a critical affective variable influencing second language acquisition, particularly in technical education environments where cognitive demands are already high. This study develops an empirical modeling framework to examine the relationship between language learning anxiety and English proficiency among learners in technical education contexts. Drawing upon established theoretical constructs such as Foreign Language Classroom Anxiety (FLCA) and affective filter hypothesis, the study integrates quantitative modeling techniques to evaluate how anxiety dimensions—communication apprehension, test anxiety, and fear of negative evaluation—affect language achievement outcomes. Using a structured methodological approach, the study synthesizes existing empirical findings and proposes a conceptual-analytical model for understanding anxiety-performance dynamics. The results indicate a statistically significant inverse relationship between anxiety levels and English proficiency, with test anxiety emerging as a dominant predictor. The findings contribute to both theoretical and practical domains by offering a structured model for educators and policymakers to design interventions that mitigate anxiety and optimize learning outcomes in technical education settings.