Search Results for
"deep learning"

The insufficient capabilities of high school students in Historical thinking skills (HTS) and Higher order thinking skills (HOTS) present a significant issue, primarily attributed to traditional teaching methods that prioritize memorization over critical analysis, particularly in history education. Consequently, this study aims to evaluate how well digital teaching materials that use Inquiry-Based learning by Primary Sources (IbLPS) can improve high school students' Historical thinking skills (HTS) and Higher order thinking skills (HOTS). A quasi-experimental research design featuring pre-tests and post-tests was employed, involving 300 students from six high schools in Surakarta, Indonesia, selected through stratified random sampling. Data were collected using validated instruments for HTS and HOTS, with analysis conducted via multiple linear regression. The analyzed data show that students' thinking skills improved significantly, with 72% of the differences in HOTS explained by how often they used primary sources, participated in discussions, and used digital teaching materials. The study concludes that the integration of digital resources with an inquiry-based approach substantially enhances students' critical and historical thinking abilities. It is expected that using primary sources in digital learning will encourage students to think more deeply and critically about history, making it important to include this approach in history teaching programs. © 2025, OpenED Network. All rights reserved.

This chapter examines Project-Based learning (PBL) and Experiential learning (EL) as transformative pedagogical approaches within competency-based education. Grounded in constructivist theory, it explores how these learner-centred strategies foster deep learning, real-world relevance, and student agency-essential competencies for 21st-century success. The chapter outlines the theoretical foundations of PBL and EL, offers practical implementation strategies, and highlights innovative practices suited to diverse educational contexts. Drawing on empirical research and case studies, it identifies best practices while addressing key challenges such as teacher readiness, assessment complexities, and institutional constraints. It also considers the potential of digital tools and emerging technologies-including Artificial Intelligence (AI) and Large Language Models (LLMs)-to support the design, facilitation, and assessment of PBL and EL. The chapter concludes with actionable insights for educators, curriculum designers, and policymakers aiming to embed meaningful, future-oriented learning within education systems. © 2026, IGI Global Scientific Publishing. All rights reserved.

With computer networks as the foundation, the new generation of information technologies is accelerating the empowerment of various sectors of society. Industries and enterprises urgently require talents equipped with data-oriented thinking skills, which raises higher demands for computer network education. To address the challenges in teaching-such as students' difficulties in constructing a systematic knowledge framework, insufficient practical abilities, and inadequate professional competencies-this study is based on the positioning of an application-oriented undergraduate institution. Guided by the philosophy of Outcome-Based Education (OBE) and constructivist learning theory, and considering the data-oriented thinking characteristics of the course, this study proposes the teaching concept of “making learning proactive.” It clarifies teaching objectives oriented toward professional competencies and leverages ChatGPT, knowledge graphs, and intelligent agents to reconstruct course content and optimize teaching methods. In addition, online platform resources are established, an evaluation system is constructed, and flipped classroom teaching is implemented. Practical results demonstrate that the flipped classroom approach significantly enhances teaching efficiency. Empowered by digitally and intelligent technologies, students engage in deep learning of computer networks through task-driven and data-oriented thinking teaching strategies, thereby improving their data-oriented thinking ability, practical skills, and application capabilities. © 2025 Copyright held by the owner/author(s).

Women’s participation in Green-STEM (Science Technology Engineering and Mathematics) careers remains low. This study examined how to remove gender barriers in Environmental-STEM technical and vocational training programs, aiming to expand opportunities for women in the green economy. SENA, Colombia’s public TVET institution, served as the case study. Gender Transformative and Participatory Action Research approaches, along with the MICMAC method, were used to identify, analyze, and address key barriers. Surveys, interviews and focus groups provided qualitative and quantitative data. The MICMAC analysis revealed the relationships among barriers and their interdependencies, identifying nine core barriers. To address these, women-led smart strategies were implemented through learning cycles, supported by small- and full-scale green pilots focused on sustainable resource utilization, production patterns, and circular economy knowledge transfer. As a result, female apprentices strengthened their sustainability-focused skills and confidence, while SENA enhanced its capacity to foster more inclusive Green-STEM vocational pathways. This study expands existing knowledge by deepening the understanding of gender barriers in vocational STEM careers related to sustainability and environmental management in Latin America, where research remains Limited. It offers actionable recommendations on leveraging education to drive progress toward SDGs 4, 5, 12, and 13. © The Author(s) 2025.

This study addresses persistent challenges in traditional talent cultivation models, including misalignment with industry demands, outdated instructional content, and limited depth in school-enterprise collaboration. To overcome these issues, the study proposes an innovative approach by integrating Back Propagation Neural Network (BPNN) technology from the field of artificial intelligence to construct a multimodal, integrated teaching quality evaluation system. By aggregating diverse data sources—including classroom performance, online learning behaviors, and hands-on practice videos—the study builds a comprehensive digital representation of the teaching process. An enhanced BPNN model is introduced, optimized by determining the ideal number of hidden nodes to improve evaluation performance. The method employs a hybrid fusion strategy, in which features from different data modalities are extracted independently, processed through dedicated neural networks, and fused at the decision level. This design enhances the model’s adaptability to complex instructional environments and improves predictive accuracy. Empirical validation uses 1,500 samples collected from 18 public courses offered by Nanjing Institute of Industry Technology. The experimental results show that the improved BPNN model significantly outperforms baseline models in both prediction accuracy and convergence speed. Specifically, it achieves a prediction accuracy of 92% for teaching quality rated as “excellent” and 90% for those rated as “very good.” This study provides a novel and effective solution for evaluating teaching quality in vocational education, fostering deeper integration between industry and education, advancing talent development, and supporting broader goals of industrial transformation and upgrading. © The Author(s) 2025.

This study examines how student-led game creation can serve as a transformative pedagogical model for integrating the Sustainable Development Goals (SDGs) into vocational education. Drawing on a semester-long, project-based course in a Chinese vocational university, the research investigates how designing educational games shaped students' creativity, collaboration, critical thinking, global awareness, and digital agency. Adopting a qualitative case study approach, we analysed reflection journals, project artifacts, peer evaluations, and group interviews through thematic analysis. Findings show that the process of designing games—rather than consuming them—enabled students to engage deeply with complex sustainability issues, negotiate ethical tensions, and develop systems thinking. Students shifted from passive learners to active creators, expressing a sense of authorship, civic responsibility, and collaborative ownership. The digital learning platform played a critical role in scaffolding this transition by facilitating reflection, co-creation, and iterative feedback. We argue that game creation, when situated within a structured, SDG-focused design task, fosters transversal competencies central to 21st-century vocational learning. The study contributes to emerging debates on design-based pedagogy, global competence, and transformative education in applied learning contexts. © 2025 John Wiley & Sons Ltd.

The integration of digital technology into Technical and Vocational Education and Training (TVET) institutions is critical for developing workforce preparation and matching educational outcomes with changing industry needs. This study, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, examines the factors and barriers of technology adoption at five TVET institutions in Sheger City, Ethiopia. Quantitative data was gathered from 413 participants to examine fundamental UTAUT variables, including performance expectancy related to the perceived advantages of technology for learning results, such as the Certificate of Competence (COC) test. Effort expectancy was assessed in terms of how easy respondents find it to use digital technologies. Social influence was considered from the perspective of teachers’ readiness to incorporate technology into their teaching. Lastly, facilitating conditions, including support from management, were analyzed to understand how they impact the acceptance and use of technology within these institutions. Activity Theory contextualizes the findings by examining the dynamic interaction of students and instructors, tools (digital technologies), communities (TVET institutions), and rules (curriculum and policy frameworks), revealing systemic tensions that impede effective integration. Adoption percentages vary greatly, ranging from 81% at Sebeta TVET College to 61% at Sululta TVET College, reflecting differences in institutional preparation. Challenges include poor infrastructure (identified by 67.8% of respondents), limited access to industry-standard software, and a lack of digital literacy, all of which have a direct influence on the mediating tools and community support structures. Logistic regression reveals that institutional affiliation and field of study have a substantial impact on student performance in the COC test, although gender and internet access had little influence. Technical disciplines, such as automotive and construction technology, have significant integration issues, indicating deeper conflicts within the activity system and a lack of alignment between curriculum and technological instruments. The study underlines the value of institutional (management) support, focused instructor training, and curriculum-industry alignment in improving enabling conditions and resolving systemic inconsistencies in technology integration. Recommendations include strategic infrastructure investments, extensive digital literacy campaigns, and regulatory and policy reforms to harmonize the activity system’s components to best suit these TVETs toward technology integration. These findings add to the wider discussion of digital transformation in TVETs, providing a theoretically supported framework for policymakers and educators looking to maximize technology-mediated vocational trainings. © The Author(s) 2025.

This study explores the integration of the educational philosophy of Science, Technology, Engineering, Arts, and Mathematics (STEAM) with deep learning technologies for the development of high school English course resources. By introducing deep learning models, particularly the Transformer architecture, the study enables automatic generation and personalized delivery of instructional content, thereby enhancing the intelligence and relevance of course resources. An innovative Transformer-based framework incorporating STEAM-related semantic features is designed and implemented. The framework is trained on a large-scale, preprocessed English text dataset collected from 300 high school students participating in STEAM-integrated English learning tasks, with data from 150 students forming the core corpus for model training. The model employs an encoder-decoder architecture and is optimized using the Adam optimizer to support the generation of high-quality content tailored to diverse teaching objectives. Through on-site teaching experiments conducted with the 300 high school students, the results demonstrate that, the model’s performance is rigorously evaluated through metrics such as Bilingual Evaluation Understudy score (0.78), innovation index (0.75), and content diversity (0.70). Results reveal that the proposed model outperforms comparative models (Model A and Model B) in language generation accuracy, innovation, and content diversity, highlighting its superior capability in producing high-quality English teaching resources. Although the model demonstrates promising results, its implementation in resource-limited environments presents challenges, highlighting the need for further refinements. Nonetheless, these findings demonstrate the model’s potential to revolutionize English teaching by enhancing resource quality and offering a more efficient, innovative approach to course development. © The Author(s) 2025.

This study develops and validates an integrated model explaining teachers’ core competence, focusing on three antecedents-teaching professionalism, workplace embeddedness, and innovation capability-with teachers’ job satisfaction as the key mediator that converts capabilities into sustained outputs. Using a questionnaire survey of university faculty, we distributed 800 surveys and collected 713 valid responses, for an effective response rate of 89%. The results show: (1) teaching professionalism positively enhances both job satisfaction and core competence; (2) within innovation capability, exploitation contributes more to job satisfaction, whereas exploration exerts a stronger influence on both subdimensions of core competence; (3) job satisfaction has a stable but moderate positive effect on core competence; and (4) the two subdimensions of core competence are highly correlated, indicating that institutional and process knowledge capabilities are mutually reinforcing. Theoretically, drawing on RBV/KBV, dynamic capabilities, social capital, and the JD-R framework, this study integrates the evidence chain among capability, satisfaction, and output, and proposes testable second-order process constructs. Practically, it offers governance-level investment priorities and evaluation indicators, integrating professional learning communities, deep workplace linkages, and value-aligned digital/AI integration into a single closed-loop management cycle to drive evidence-based improvements in curriculum and assessment, thereby enabling teachers to consistently deliver high-quality learning outcomes. © 2025, Expert Projects. All rights reserved.

Introduction: Teacher engagement (TE) is crucial since it substantially impacts students' academic achievement, personal growth, and general well-being. While extensive research has explored TE in various university settings, there is a noticeable gap in studies focusing on the context of health professions education (HPE). In HPE, TE is key in fostering/developing clinical competencies, critical thinking, communication, and teamwork skills essential for effective healthcare delivery. However, the literature on TE in HPE is fragmented and controversial, with conflicting findings regarding its relationship to student outcomes and professional satisfaction. Our scoping review aims to explore and address these gaps. Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology. Seven online databases were used to identify relevant literature. Out of 249 articles, 18 were selected for analysis. Results: Studies on TE used varied dimensions and measurement tools, making it challenging to understand the concept consistently. Also, five studies employed the Utrecht Work Engagement Scale (UWES-9), two used the Engaged Teacher Scale (ETS), another two used a Chinese adaptation of the work engagement scale, and one used both the UWES-17 and a faculty engagement questionnaire. Qualitative studies highlighted key factors such as job satisfaction, feedback, autonomy, and professional development opportunities. The outcomes were categorized into personal satisfaction, teaching practices, student engagement, self-efficacy, job satisfaction, retention, and teaching performance. Conclusion: The analysis demonstrates a growing interest in TE within HPE, though there is a notable lack of consistency in how it is defined and measured. Further research is essential to deepen this understanding and to develop targeted strategies that cultivate a mutually beneficial and sustainable teaching and learning environment. © The Author(s) 2025.