Search Results for
"deep learning"

This study integrates BERT with multi-source data fusion technology to construct a chain-mediated model linking practical education base construction, teacher guidance effectiveness, collaborative education, and employability improvement. It uses a domain-adapted fine-tuned BERT for deep semantic encoding of unstructured internship texts and designs a "feature-layer attention + decision-layer GBDT"framework to integrate diverse data, building a high-precision employability prediction model. Empirical findings show: (1) Practical education base construction directly enhances employability, with its interaction with teacher guidance effectiveness indirectly influencing employability through the chain-mediation path of collaborative education (accounting for 59.15% of total mediation effect); (2) BERT-parsed textual semantic features contribute most to employability prediction (38.7%), verifying the technology's key role in evaluating implicit abilities. The study provides quantitative evidence for collaborative education mechanisms, offers scalable technical solutions for multi-source data fusion and intelligent analysis, and proposes technology-empowered optimization paths covering institutional guarantees, teacher evaluation reform, enterprise platform development, and students' reflective practice. © 2025 Copyright held by the owner/author(s).

With the deep penetration of artificial intelligence technology in the field of education, human-machine collaborative education models have gradually become a research hotspot. However, the inadequacy of technology application and educational scenario compatibility remains the core bottleneck restricting its development. Based on the theory of cognitive symbiosis, this article constructs a dynamic matching matrix as an intelligent decision-making model. By following the technical path of "dimension design-rule modeling-scenario validation", it achieves precise matching of three major mechanisms: coexistence of educational scene characteristics and knowledge network, dynamic transfer of control, and maintenance of emotional connections. Research shows that this matrix is compatible in liberal arts, science, and interdisciplinary educational scenarios, balancing technological possibilities with educational necessities, and providing a replicable technical paradigm for the deep integration of artificial intelligence and teaching and learning. © 2025 Copyright held by the owner/author(s).

Because of the difference between the virtual simulation environment and the real working environment, when designing the virtual simulation teaching method, there is usually a problem that the teaching performance of the method is poor due to unreasonable data mapping rules. In this regard, a virtual simulation teaching method of deep integration of production and teaching under digital mode is proposed. According to the types of existing teaching resources, firstly, the data is cleaned, and then the teaching resources are integrated and standardized. By obtaining the relevant data of the real teaching environment, the corresponding data mapping rules are formulated, and the obtained data are mapped into the digital space to build a virtual simulation teaching environment, thus designing a virtual simulation teaching scheme. Through the above design, the design of virtual simulation teaching method is completed. The experimental results show that the designed virtual simulation teaching method with deep integration of production and teaching in digital mode has good teaching performance in practical application. © 2025 Copyright held by the owner/author(s).

How to enhance the teaching ability of green energy economics from the perspective of industry education integration and rely on digital twin technology has become a key issue in breaking through the bottleneck of higher education development research. This study used data from Chinese higher education institutions from 2010 to 2024 as samples, and verified the impact and mechanism of industry education integration on the teaching ability of green energy economics through a multi period double difference model. The results indicate that the integration of industry and education can significantly enhance the teaching ability of green energy economics; The integration of industry and education can be achieved through the development and application of effective digital teaching tools for energy economy. Its core path includes utilizing digital twins and IoT technology to optimize the construction of virtual simulation teaching scenarios, realizing dynamic simulation of complex scenarios such as the economic operation of wind and solar power plants, thereby promoting the improvement of teaching capabilities in green energy economics; The integration of industry and education can be achieved by optimizing the construction of virtual simulation teaching scenarios, relying on Python, machine learning algorithms, and blockchain technology to promote the development and application of digital teaching tools for energy economics (such as intelligent energy cost calculation systems and distributed energy trading simulation platforms), ultimately promoting the improvement of teaching capabilities in green energy economics,. Based on this, it is recommended to deepen the collaboration between schools and enterprises in technology (jointly developing digital courses and algorithm tools), optimize teaching processes (integrating big data analysis and AI policy simulation training), establish a digital tool iteration mechanism, and improve the computing resource guarantee system, in order to leverage the collaborative empowerment of industry education integration and computer technology. © 2025 Copyright held by the owner/author(s).

Introduction. Vocational Training aims to improve students’ professional profiles through the development of professional, personal, and social skills. These are related to socio-emotional competence, which is the understanding and awareness of one’s own emotions. The objective of this study is to determine the relationship between resilience and basic competencies as predictors of socio-emotional competencies in vocational training students. Method. The sample consisted of 151 distance learning vocational training students. Two instruments were used for data collection: the University Students’ Basic learning Competencies Questionnaire and the Socio-Emotional Competencies Questionnaire. SPSS and AMOS 26.0 were used for data analysis. A descriptive analysis was performed to analyze the relationship between variables, and a structural equation model was developed to delve deeper into the causal relationships between variables. Results. The results show significant differences according to gender, professional background, and religion. Resilience and basic competencies are identified as predictors of students’ socioemotional profile. Discussion. Students with higher levels of resilience and basic competencies have a higher level of socioemotional development, highlighting the need to establish emotional development programs in these studies. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License https://creativecommons.org/licenses/by-nc-nd/4.0/.

The problem-based learning (PBL) teaching method is a renowned international educational approach widely implemented in engineering education. This paper examines the effects of a comprehensive teaching strategy that incorporates PBL and English literature within the 'Power System Overvoltage' course. The goal of this integrated teaching strategy is to improve students' critical thinking and communication skills, which are important for today's global engineers. A structured questionnaire was conducted with 210 students to gather their feedback on this teaching method, and the results were analyzed using cluster analysis. The findings revealed that 86% of students responded positively indicating improvements, in their technical skills and language abilities. The students feedback also emphasized that instructors implementing this teaching method need to possess deep professional knowledge, stay updated with current scholarly developments, and invest extra time and effort for effective implementation. © 2024 IEEE.

This book offers deep insights into the agencies of teaching—pedagogies, professional identities, and knowledge—in the English further education (FE) sector. The sector’s porous, inclusive, diverse, and prominent features of vocational offers have led to it being overlooked and misunderstood by policymakers. To rectify this misconception and re-emphasise its importance in England’s educational ecology, FE is reframed as a rich, all-encompassing and connected sector. It proposes a new language of educational engagement by uniting vocational education and training under a new umbrella term: occupational education (OE). OE intersects work, teaching, and learning across pre-university, higher education, and professional education. A new system is provided that maps lifelong learning, irrespective of age, ethnicity, gender, disabilities, or station in life. The key audience is students, teachers, teacher educators, institutional managers, policymakers, and academics. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Work-integrated learning (WIL) has gained significant attention in higher education for its impact on employability. This study presents a comprehensive bibliometric analysis of WIL research from 2002 to 2023, using data from 1,392 articles indexed in Scopus. The analysis highlights key trends, influential authors, institutions, and collaboration networks, revealing that WIL research has grown substantially, particularly since 2013. The findings underscore the dominance of Australian institutions and authors in the field and the multidisciplinary nature of WIL, which spans social sciences, business, engineering, and healthcare. Through co-authorship and cocitation analyses, this study identifies the central themes and most cited works that shape WIL research. The study also explores the global distribution of WIL research, noting strong collaborative networks, particularly between Australia, New Zealand, and Canada. By mapping the evolution of WIL research, this study provides a valuable reference for future studies, helping to guide research directions and deepen the understanding of WIL’s role in enhancing employability. ©: CC BY 4.0.

This book examines evolving trends in pre-employment and continuing education, focusing on how formal learning affects learners’ skills, career motivation, and work readiness. It also addresses adult workers’ skills development, continuous training and workplace learning, highlighting key enablers, barriers and factors influencing their motivation and employability. Drawing on insights from experienced scholars and practitioners from Singapore and some Asia-Pacific contexts, including Australia, Bangladesh, and China, the book uniquely integrates the synergy between pre-employment and continuing education, offering a deeper understanding of their interconnectedness in fostering a sustainable workforce. As a comprehensive resource, it provides valuable insights for advancing higher education, vocational training, continuing education, workplace learning, and workforce development. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

The rapid and profound permeation of generative AI technology into all aspects of modern society also changes the landscape of higher education and thus leads to a compelling call to harness the power of AI for transforming higher education. One of the most significant areas of opportunity offered by generative AI is in the assessment of learning. The goal of assessment is to foster learning especially essential for success beyond the classroom, and this can only be possible with well-designed assessments that have the potential for determining whether students have learned the content. By harnessing generative AI, assessments can be designed that have the potential to support inquiry-based learning and foster creativity, all essential for the development of a deeper conceptual understanding of content. Educational Assessments in the Age of Generative AI contributes to the effort to bring generative AI to the forefront of assessing students’ learning by exploring how the use of generative AI tools and applications can transform and revolutionize assessment practices. Furthermore, it is devoted to exploring the use of AI in assessments to measure knowledge, skills and students’ abilities in order to prepare them for careers in the 21st century. Covering topic including academic integrity, higher education, and mathematics education, this book is an excellent resource for educators, higher education administrators, policymakers, information technology support professionals, tests and assessment developers, researchers, scholars, academicians, professionals, and more. © 2025 by IGI Global Scientific Publishing. All rights reserved.