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In the rapidly evolving domain of vocational education, the deluge of digital teaching resources necessitates an advanced personalized recommendation system to optimize learning experiences. Traditional recommendation algorithms struggle to grasp the dynamic nature of user interests and the intricate web of interactions between users and resources. Therefore, in this paper, an efficient approach combining dynamic user interest modeling with a graph-based recommendation system is proposed. Transformer architectures are utilized to capture temporal shifts in user preferences through feature engineering of browsing behaviors, click history, and preference settings. A graph neural network is employed to capture the complex relationships within a graph-based framework. It uses graph convolutional or attention networks to dynamically assign weights that reflect the influence of neighboring nodes. Experiments are conducted on real-world datasets to assess the performance of the proposed approach in terms of precision, recall, and $F$1-score compared to traditional systems. The proposed approach provides more accurate and personalized teaching resource recommendations in vocational education settings.

Vocational education can be seen as an important tool in leveraging India’s demographic dividend. Given the labor surplus in terms of the young population, it is important to examine how vocational education and training can serve as a critical driver of economic growth. This study aims at examining the impact of vocational education and training on economic growth in India, using the framework of the augmented Solow growth model by decomposing human capital into vocational education, along with primary, secondary and higher education. Using an ARDL model based on the annual data from 1990 to 1991 and 2019 to 2020, the study analyzes the short-run and long-run impact of vocational education. The results indicate that in the case of India, the impact of vocational education on economic growth is both a short-run and long-run phenomenon exhibiting a stable positive relation. Other control variables used in this study, such as Investment, Population, Openness and Inflation are found with a significant impact on economic growth. The study suggests that it is necessary to upgrade vocational education programs to create high-quality workers with a view to improving productivity and macroeconomic outcomes in India.

This study investigates the relationship between minimum wage policies and educational choices, specifically the decision between vocational and general school enrollment. While higher minimum wages often attract students to vocational education as a faster route to employment, our analysis of Indonesia Family Life Survey data finds little evidence of increased enrollment in vocational education. This suggests that hikes in the minimum wage are unlikely to divert students away from general education. Our findings reveal notable heterogeneity, showing that an increase in the minimum wage is associated with a statistically significant reduction in the likelihood of men and individuals in rural areas to opt for vocational education. These findings underscore the critical need for Indonesia to revitalize its vocational education system, striking a balance between general and vocational pathways to equip its workforce with the skills required for a rapidly changing environment.

This study examined entrepreneurial inclinations among vocational training centres (VTCs) using a sample of 508 trainees from 12 VTCs in Iringa region, central Tanzania. A majority of VTC trainees have interest in starting own businesses, motivated primarily by the need to have control over their own lives. While gender and having taken entrepreneurship courses have no significant effects on start-up inclinations, perceived chances of getting a job and background of entrepreneurial family are positively associated with interest in entrepreneurial intention. Results suggest a need to re-examine entrepreneurship training in VET and for policy makers to be careful in promoting entrepreneurship as an alternative career for those unable to secure paid jobs.

With the rapid development of China’s economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.