Search Results for
"Engineering education"

This paper presents a brand-new pedagogical framework for teaching Virtual Reality (VR) development to undergraduate students with no prior programming experience through an intensive 24-hour, narrative-driven curriculum. We demonstrate that combining story-centered learning with multi-instructor support enables novice learners to create sophisticated Unity-based VR applications addressing complex social challenges. Our approach integrates academic guidance, industry mentorship, and technical laboratory support to bridge the gap between creative vision and technical implementation. Results show the following four distinct application categories that emerged organically: student projects spanning healthcare interventions (MindEye), cultural preservation (Fire·Blessing), social justice awareness (Invisible Chain, OpenFashion VR), psychological narratives (Mindscape), and empathy-building experiences (Through Their Eyes). These demonstrate the effectiveness of this rapid prototyping methodology. Our findings indicate that narrative-driven, project-based learning can successfully democratize access to VR development education while maintaining high standards for technical achievement and social impact. This pedagogical workflow offers a replicable framework for institutions seeking to implement accessible, immersive technology curricula in resource-constrained environments, challenging traditional assumptions about technical education prerequisites and demonstrating VR's potential to address societal challenges through empathy-building, immersive experiences. © 2025 Copyright held by the owner/author(s).

This study explores the integration of STEAM (Science, Technology, engineering, Arts, and Mathematics) in secondary-level chemistry education, focusing on its impact on student engagement and learning outcomes. Specifically, the study aims to identify research trends, contributors, institutions, and prominent themes in this field. A bibliometric analysis was conducted on 53 research articles indexed in the Dimensions database from 2020 to 2024, and data visualization was performed using the VOSviewer tool. The results revealed a significant increase in publications, peaking at 23 in 2024, indicating a growing interest in STEAM methodologies. Notably, Greek authors demonstrated substantial influence, achieving a high citation count of 132 from only two publications, while Indonesia contributed the highest number of documents with 20 publications and 62 citations. Influential journals such as Applied Sciences and Sustainability were identified, alongside trending themes including augmented reality, gamification, and project-based learning. The findings highlight challenges such as students' negative perceptions of chemistry, limited teacher preparation, and the necessity for effective teaching strategies. This study concludes that interdisciplinary approaches foster critical thinking and creativity among students, ultimately preparing them for modern educational demands. Suggestions include strengthening teacher training, encouraging international collaboration, and adopting innovative pedagogical models. The study is limited to the Dimensions database and the 2020–2024 publication period, which may not capture all global research on STEAM integration. © 2025, FoundAE (Foundation of Advanced education). All rights reserved.

The modern industry requires graduates who are not only theoretically competent but also possess technical skills, problem-solving talents, and job preparedness. However, a large gap still exists between classroom learning and industry requirements. This study examines the practicality and effectiveness of the Science, Technology, engineering, Arts, and Mathematics (STEAM)-based Project-Based Learning (PjBL) approach for helping students improve their skills in fabrication and manufacturing technology and prepare for jobs in the industry. The study involved 64 students, 10 lecturers, and 10 industry practitioners in the Diploma III Mechanical engineering Program at Universitas Negeri Padang, Indonesia. The research was conducted using the Research and Development (R&D) method based on the Borg and Gall model, which Puslitjaknov simplified into six stages: data collection, planning, initial product development, preliminary field testing, product revision, and main field testing. The data analysis prioritized practicality, efficacy, and industry preparedness. The STEAM-PjBL approach dramatically improved students’ technical competencies, capacity for problem solving, design innovation, communication, teamwork, and project management skills—all of which are important in the manufacturing industry. The pretest findings revealed no significant difference between the control and experimental groups (p-value = 0.91), indicating equivalent starting positions. However, the posttest results showed a substantial improvement in the experimental group (p-value = 0.00), indicating that the model successfully enhanced learning outcomes. Thus, including STEAM-PjBL in the curriculum is a viable method for closing the gap between higher education and industry demands. © 2025 by the authors.

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Artificial Intelligence and Machine Learning in Cybersecurity: A Comprehensive Guide to Improving Cybersecurity Protocol is a comprehensive exploration of the intersection between cutting-edge technology and cybersecurity practices. This book offers readers an in-depth understanding of how artificial intelligence (AI) and machine learning (ML) are reshaping the cybersecurity landscape. It begins with foundational concepts, explaining AI and ML's principles and their transformative potential within various sectors, particularly cybersecurity. This book uniquely combines theoretical insights and practical applications, making it an essential resource for graduate students and cybersecurity professionals eager to expand their knowledge and skills. The book's uniqueness lies in its detailed analysis of how AI and ML can predict and counteract emerging threats in real time, shifting the paradigm from reactive to proactive cybersecurity measures. By delving into a wide range of topics, such as AI-powered Intrusion Detection and Prevention Systems (IDPS) and Endpoint Security, the author provides case studies and examples from sectors like finance and healthcare. This hands-on approach not only illustrates successful implementations but also highlights potential challenges, offering balanced perspectives and strategies to overcome hurdles. The inclusion of ethical considerations around AI usage in cybersecurity further distinguishes it as a forward-thinking guide. As cyber threats continue to evolve, the need for advanced AI and ML methodologies becomes increasingly critical. This book addresses this urgency by equipping readers with contemporary knowledge and tools necessary to leverage these technologies effectively. The discussion of future trends, such as AI-powered quantum security and necessary policy implications, ensures that readers are well-prepared to navigate the complexities of cybersecurity in the coming decades. Ultimately, it serves as both an educational textbook for students and a practical guide for cyber practitioners, offering a roadmap for implementing AI-driven cybersecurity solutions that enhance threat detection, response, and prevention. © 2025 Richard Young, Ph.D. All rights reserved.

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).

Industry 5.0 is a new emergent industrial revolution that admits and promotes mutual and coordinated interaction of industrial workers’ cognitive and creative skills, and artificial intelligence machines’ qualities to maximize production in industries. Thus, the advent of Industry 5.0 demands new skills, knowledge, attitude and responsibilities/roles from workers to enable them to fit the positions. Consequently, Industry 5.0 has significant implications for engineering, technology and technical education programs. These programs need to be reshaped for the purpose of producing worthwhile graduates that can easily be absorbed into Industry 5.0. However, this article focuses on the ‘recurriculation’ of engineering, technology and technical education programs for adopting Industry 5.0. The article is anchored on a literature review. Specifically, the article dwells briefly on the pre-industrial revolution, Industry 1.0, Industry 2.0 and Industry 3.0. The article explicates on Industry 4.0 and Industry 5.0. Similarly, the article identifies problems that emanated during the Industry 4.0 era. It explains the importance of human beings in industries. Also, this article explains the needs for improving the knowledge, skills and attitude of industrial workers during Industry 5.0. Being a program where knowledge, skills and attitude needed in industry 5.0 can be acquired, the article briefly conceptualizes engineering, technology and technical education. Furthermore, the article explains the concept of recurriculation of engineering, technology and technical education programs. Finally, the chapter explains the phases for the recurriculation of engineering, technology and technical education programs. © 2025, The Design and Technology Association. All rights reserved.

The development of soft skills remains a critical challenge in vocational education, as the curriculum often prioritises technical competencies while neglecting systematic integration of interpersonal skills. This study aims to develop and evaluate the Peer-Engaged Project Application Model (PePA), a project-based learning framework designed to enhance communication, teamwork, and problem-solving through structured peer engagement. Employing a Research and Development (R&D) approach based on the ADDIE model, the research was conducted in the D4 Mechanical engineering Program at Yogyakarta State University. The PePA model was tested on a group of 15 students in the Fabrication Construction Practices course. Data were collected through Likert-scale questionnaires, structured observations, and semi-structured interviews, and analysed using descriptive and inferential statistics, along with qualitative techniques. The implementation of PePA resulted in improved performance in soft skills, with average scores increasing from 3.03 to 3.65 (communication), 3.20 to 3.78 (teamwork), and 3.13 to 3.70 (problem-solving). The model was also rated as “very valid” by expert evaluators, with an average validation score ranging from 3.58 to 3.85. These findings suggest that PePA is a feasible and effective learning model for strengthening vocational students' soft skills in alignment with industry expectations. The model has potential applicability beyond engineering education and supports policy recommendations for integrating soft skills into vocational curricula. © 2025, Intellectual Research and Development education Foundation (YRPI). All rights reserved.

Energy specialists in various nations, including Malaysia, are currently engaged in discussions around the subject of renewable energy. Nevertheless, Malaysia's capacity to harness renewable energy remains underutilized due to a deficiency of educational programs focusing on this domain in schools and universities. To address this issue, innovative and effective learning models are being developed to improve the knowledge, skills, and attitude of students specifically in Klang Valley communities and mainly in Putrajaya. It is important to note that following a procedure is essential to ensure user safety and maintain the integrity of the process. This paper presents the development of a comprehensive training kit designed to enhance project-based learning for off-grid solar panel installation. The training kit aims to bridge the gap between theoretical knowledge and practical application by providing hands-on, interactive learning experience. It includes detailed instructional materials, interactive simulations, and real-world scenario exercises that guide learners through the entire installation process-from site assessment to system maintenance. The study utilizes a quasi-experimental design employing the One Group Pre-test and Post-test Design model, supported by Aiken's V index for hardware assessment and the Cronbach Alpha method for statistical validation involving 86 respondents. User feedback on the integration of the kit into learning, based on Cronbach Alpha and Aiken V averaging 76.8%, emphasizing its effectiveness and significance. During the trial phase, significant improvement was observed in learning outcomes, with 85.8% of participants demonstrating excellent competency. In conclusion, the training kit proves to be highly effective in teaching essential skills for solar panel installation, offering a hands-on approach that enhances practical understanding. © 2024 IEEE.

This full research paper aims to investigate methods for systematically identifying core tasks within undergraduate engineering work-integrated learning (WIL) opportunities, such as internships and co-ops. It achieves this by automatically analyzing WIL opportunities using transformer models. A dataset of 4,833 engineering internship postings from the last ten years was obtained through a partnership with the University's Career Connections Center. From this, a subset of 374 aerospace engineering internships, yielding 1,913 unique job tasks, was extracted for human labeling. We applied the Llama 2 architecture, a sophisticated pre-trained LLM, to extract a list of specific responsibilities and tasks from the internship postings. The job tasks were used to train an automated classification system to map each task to the established seven ABET student outcomes. Each job task was human-labeled by three subject matter experts, achieving a high level of inter-rater reliability of 0.998, according to Krippendorff's alpha. RoBERTa resulted in the optimal model indicating a label ranking average precision of 0.892 on the validation set and 0.857 on the testing set. Our findings provide novel insights into understanding the evolving skill expectations of undergraduate interns, offering a basis for tailoring engineering education to address these demands. Furthermore, the automated analysis of internship tasks demonstrates the potential for a scalable way to address the gap in understanding the core responsibilities within WIL experiences. © 2024 IEEE.