Computer Applications in Engineering Education最新文献

Intelligent manufacturing training rooms are pivotal for translating theoretical knowledge into practical applications and enhancing operational skills. To address the limitations of traditional teaching models—specifically their inability to facilitate large-scale, complex, or high-risk professional experiments, which result in insufficient comprehensive practical training for students—this study introduces an augmented reality (AR)-based intelligent manufacturing comprehensive teaching and training system (AR-IMCTTS). The system establishes an integrated framework for teaching, hands-on practice, and assessment, enabling students to master the knowledge and production processes of intelligent manufacturing equipment comprehensively. Three aspects of experimental verification were conducted. First, questionnaire results indicate that students widely acknowledge the system's effectiveness in deepening their understanding of IM equipment operations and improving practical skills. Second, quantitative data reveal that students using AR-IMCTTS achieved an average score increase of 10.82% compared to traditional teaching methods. Third, results demonstrate that the hybrid approach of traditional education and AR-IMCTTS significantly enhances participants' learning motivation and practical knowledge retention. Through interactive engagement with virtual objects in AR environments, students develop a profound grasp of experimental equipment. Simultaneously, the experimental group's increased practice opportunities boost learning confidence and reduce cognitive load.

Improving student engagement in engineering education is crucial for better learning outcomes. Engineering education integrates the latest technologies with gamification that fosters student engagement in innovative learning. Gamification involves incorporating game design elements, making learning more interactive and motivating. This paper explores gamification to transform engineering education, improving students' participation, understanding, and overall learning outcomes. Combining Edge computing with artificial intelligence will enhance the gamification of education by addressing two primary challenges: real-time interaction and personalized service. An Edge-AI-driven gamification framework is proposed for improving student engagement through interactive tools like educational games, quizzes, and competitive learning environments. An algorithm related to the Edge-AI Gamification Optimizer is proposed to improve the overall performance. Results show that the proposed framework improves student engagement by nearly 51%, performance by 30.7% and reduces cognitive load by 57%. A comparative study with traditional learning approaches shows that AI-based gamification significantly enhances knowledge retention, boosts collaborative learning, increases student motivation, and, through statistical analysis, demonstrates a validated increase in student engagement over conventional methods. Future studies will investigate the extendability of this framework to different fields of engineering and learning environments. The proposed framework has the potential to transform educational practices by providing personalized, interactive, and motivating learning experiences, leading to better educational outcomes. Future research could explore broader applications and long-term impacts of Edge-AI-driven gamification.

Technology-Enhanced Learning (TEL) has been integrated into construction engineering and management (CEM) education, offering innovative tools to improve both technical and soft skills. However, the effectiveness and scope of these technologies in addressing critical competencies have not been explored. This systematic review evaluated the current use of TEL in developing competencies in CEM education, with a focus on understanding which competencies and TEL are most frequently addressed, the learning assessment used, and the challenges on applying TEL. Following the systematic review, this study adhered to a five-step process: developing research questions, cataloging research articles, appraising the quality of the literature, synthesizing insights, and acknowledging limitations. Data was synthesized from 28 studies published between 2013 and 2023. The findings reveal a predominant focus on hard skills, particularly in safety training and project management, with limited attention to soft skills. Immersive environments like Virtual Reality and Building Information Modeling are the most commonly used TEL tools. Additionally, while instructors employ both formative and summative assessments, they often refrain from using TEL due to a lack of knowledge in utilizing these technologies and the high investment required. Future research should explore training both students and instructors to overcome the lack of knowledge regarding TEL and to develop adaptive assessment methods that equally emphasize both technical and soft skills. Expanding the use of emerging technologies like Artificial Intelligence and the Internet of Things could further enhance the effectiveness of TEL in CEM education.

Robotics is a discipline gaining in relevance in the last decades. Although it becomes appealing for students, undergraduate courses related to robotics involve certain mathematical and programming skills that usually discourage students. In addition, teaching techniques followed by instructors largely affect the engagement of students and thus their academic results. A well-studied way to overcome this situation is applying the so-called Learning through play paradigm in the form of game-based learning (GBL), which considers games to improve the learning experience. In this study, we leverage an ad hoc computer video game to increase the students' engagement and marks in an undergraduate course of robot programming using the robotic framework ROS2 and the 3D simulator CoppeliaSim. The experience was conducted on the academic year 2024/2025 when 45 students enrolled the course. The results have been evaluated using questionnaires and statistical comparison with the previous 4 academic years.

This article presents an efficient approach for modeling and solving the planar trusses using the “anastruct” module which is a specialized Python library for analysing the structures. By using this existing tool, the procedure automates the tiresome manual calculations, thereby enabling precise determination of the axial forces within the truss members under various loading and boundary conditions. To evaluate the effectiveness of the module, five typical truss problems of increasing complexity were solved, demonstrating its applicability to both academic learning and practical engineering tasks. It has been observed that in all the cases the “anastruct” has given result which are precisely aggreging with the existing literature. A pilot study on 30 structural analysis students revealed the “anastruct” tool's visualization and calculation benefits, but highlighted a need for more stiffness matrix documentation. This study highlights the adaptability of Python for the structural analysis and provides a foundation for incorporating advanced functionalities like nonlinear behavior and dynamic analysis in future studies thus laying the groundwork for further advancements in structural modeling and analysis automation. The pedagogical importance of this tool has also been discussed which will offer an actionable insight for the educators and the people engaged in the development of the curriculum.

This study examines the programming-oriented computational thinking (PoCT) skills of engineering and nonengineering faculty students, comparing them in terms of grade level and the three subdimensions of PoCT: Conceptual knowledge, algorithmic thinking, and evaluation. Additionally, the study investigates the extent to which students' PoCT skill levels can be predicted by programming language self-efficacy and design thinking skill through hierarchical regression analysis. The data of 571 computer science students from two types of faculties in Turkish universities were analysed. The findings revealed that engineering students had superior computational thinking skills to their nonengineering counterparts, as well as a slightly higher predictive power of programming self-efficacy and design thinking on computational thinking. These results offer valuable insights and strategies for developing instructional practices that address the disadvantages faced by nonengineering disciplines.

In an increasingly complex world where increased interactions between users and systems are taking place and a greater amount of information must be processed, Machine Learning techniques are becoming increasingly relevant as they help researchers to generalize how the different variables in a process are related and, consequently, reduce the margin of error in the estimation of results. One of these techniques is Support Vector Machines (SVM), which are characterized for being simple, flexible, and computationally efficient. They are also useful for classifying and analyzing data for regression tasks in multiple areas such as natural language processing, image classification, bioinformatics, signal processing, as well as robotic systems. In this regard, several current studies have optimized performance and cost by implementing SVM in hardware, particularly on Field-Programmable Gate Array (FPGA) as they are suitable for dealing with challenging embedded system constraints. Consequently, undergraduates must be familiarized with this approach to be more competitive when entering the labor market in the real industry. Therefore, it is imperative to create teaching alternatives that provide practical knowledge about SVM, not only from a software perspective, but also on the design and modeling of hardware architectures that describe their structure and can be implemented in a specific device. With this aim in mind, this study presents a learning-by-doing educational approach for teaching the principles of SVM by promoting their modeling, implementation, and evaluation on FPGAs. Furthermore, the results derived from an empirical evaluation on 55 undergraduates from the Universidad Tecnológica de la Mixteca, México, provided evidence that the proposed approach can stimulate the development of skills required in the labor market related to the design and modeling of hardware architectures and, at the same time, allows students to undertake design challenges involving SVM and reconfigurable logic.