Search Results for
"Vocational education"

This study addresses three central research questions (1) How do autonomy and related contextual factors—specifically social relatedness and competence recognition from Self-Determination Theory (SDT), and self-regulation capacity from ego-depletion theory—relate to educational outcomes, particularly given the conflicting predictions from these frameworks? (2) Do these associations differ between the two key educational outcomes: vocational competence and persistence (Outcome-Specificity)? (3) Do these associations vary depending on the domain-specific (company-level) versus domain-general (occupational-level) nature of the educational outcomes (Domain-Specificity)? The sample included 458 vocational education and training (VET) students in Germany (36 % male, 64 % female; Mage = 19.66 years), followed throughout their training programs. Four longitudinal regression models were estimated—based on two types of educational outcomes (competence and persistence) across two domain scopes (company-specific and occupation-wide). Models controlled for time effects and the nested structure of the data (students within classes and schools). Findings indicate that autonomy and related contextual factors—such as social relatedness and competence recognition— show significant positive associations with persistence, but not with vocational competence. Outcome-specific patterns were observed, while domain-specific versus domain-general distinctions were less clear. Despite the dual structure of VET, integration between company-based and school-based learning may blur the boundaries between company-specific and occupational wide learning effects. While the longitudinal design supports temporal analysis, reliance on self-report measures and the absence of experimental manipulation limit causal interpretations. © 2025 The Author(s).

The widespread adoption of virtual and remote laboratories has transformed practical education by offering scalable, safe, and flexible environments for developing technical skills. Despite these benefits, challenges remain in providing timely, meaningful feedback that supports self-regulated learning and improves student outcomes. In this study, we analyze anonymized interaction logs from a virtual laboratory system focused on the Industrial Electrical Control Training Lab, used by 839 students enrolled in Electrical, Electromechanical, Mechanical, or Industrial Automation vocational programs offered by the National Service of Industrial Apprenticeship of Santa Catarina (SENAI/SC, Brazil). We introduce a novel methodology that combines sequential pattern mining (SPM) with automatic performance-based segmentation to analyze student behavior in virtual laboratory environments. Using interaction logs from students containing time-stamped cable connections and interface interactions, we segment learners into performance (High, Intermediate, and Low) based on the z-scores of accuracy and speed, using two strategies: (i) a K-means clustering approach, and (ii) a rule-based decision function. Building on this segmentation, we propose an algorithm to identify frequent action sequences and recurrent error patterns specific to each group. Chi-square tests confirmed significant associations (p < 0.05) between distinct error types and performance levels, and one-way ANOVA validated that both segmentation methods produce statistically distinct clusters. Our findings indicate that students in the high-performance group are likely to refer to schematics beforehand, whereas those in the lower performance group frequently begin with montage connections. These results can be integrated into LA dashboards to support real-time feedback for students and instructors, ultimately enhancing teaching strategies and learning in practical disciplines. © 2013 IEEE.

There is a growing trend in Europe and North America for legislation promoting public funding for zero carbon construction to incorporate inclusive equity goals, so incentivising employment and vocational education and training (VET) providers to recruit groups under-represented and disadvantaged in the skilled construction occupations, especially women. The barriers to women’s entry and survival in the sector include the nature of VET provision, the structure of the industry and its labour market, employment and working conditions, and social and cultural aspects. We used convenience sampling and biographical interviews to ascertain how sixteen tradeswomen from various countries experience green construction policy and practice and assess how a low-carbon society can open up opportunities for women. The study shows how women transcend outsider status through networks of collaboration, developing a sense of collective agency in addressing inequities and climate change. Many advocate better VET and workplace actions and greater concern for nature, while several argue that achieving sustainability and equity requires systemic social, cultural, and structural change. From the perspectives of women working in construction, the chapter indicates the ways in which this transformation is taking place and what is needed to move towards an inclusive green industry. © 2026 selection and editorial matter, Susan Buckingham, Martin Hultman, Gunnhildur Lily Magnusdottir and Karen Morrow; individual chapters, the contributors.

This article uses Pierre Bourdieu’s field theory to conduct a relational analysis of the expansion of academia. Drawing on three case studies from the fields of research (open data), higher education (academization of vocational education), and knowledge transfer (real-world labs), we examine the dynamic interactions between the scientific field and its social environment. Our investigation shows that scientific expansion processes can take very different forms depending on social context. We observe contested incorporations, intrusions, and bridgings that feed back into academia as the hybridization of higher education, infrastructuring of research, and recoding of knowledge. Based on these empirical findings, we propose a relational research heuristic that should enable a better understanding of the multifaceted expansion of science in the future. © The Author(s) 2025.