Computer Applications in Engineering Education最新文献

Electromagnetics is a core course in the undergraduate electrical engineering curriculum that entails the study of electric and magnetic fields. Students, usually, perceive it as a challenging course since it requires them to build mental models of the spatial and time-varying electric and magnetic fields that cannot be seen by the naked eye. Moreover, the mathematics used in this subject is quite complex and abstract and can further compound the students' difficulties in connecting abstract math with real-life industry applications. To address these challenges, the author designed an undergraduate electromagentics course, which utilized certain pedagogical techniques to enhance the learning experience of the students. These pedagogies include extensive usage of MATLAB simulations, animations, and videos during the course that helps students visualize and conceptualize abstract concepts, usage of a comprehensive course-wide equation sheet, and highlighting the connection of the theory with real-life applications and industry jobs. This paper presents some of the simulations designed by the author as part of this course. Moreover, the equation sheet is also reproduced and access to its $��\; �L��\; ����A�����\; �T��\; ��E���\; �X�$ source is provided for the benefit of other instructors and students. The paper also presents results from the end-of-semester students' survey to measure the efficacy of these pedagogical techniques.

The further reform and development of engineering education courses can help improve the teaching quality of higher education institutions, implement the fundamental task of talent cultivation, and provide important support and channels for cultivating high-quality and supportive engineering technical talents. The traffic system simulation course is an applied science closely related to engineering technology. It mainly cultivates senior engineering technical talents engaged in research and development of transportation engineering, analysis of road traffic characteristics, and transportation planning and design. Considering the characteristics of the traffic system simulation course, this study puts forward the course teaching design based on computer virtual reality software. Combined with the training objectives of applied talents in the traffic system simulation course, this study focuses on how to reform the teaching content and teaching mode with the help of Vissim computer simulation software. The course teaching design refines and optimizes the teaching content by the use of computer software. The use of Vissim software in the course enriches the teaching content and methods and stimulates students' interest in learning. Through the guidance of active exploration and self-directed learning for students, the traffic system simulation course reform has created a more positive and effective learning environment for students, which helps to cultivate their exploratory spirit and innovative ability. The implementation of the course teaching reform has gradually improved students' learning initiative and ability to master course content, as well as their practical problem-solving abilities. Good teaching results have been achieved.

The expanding software industry has increased the demand for proficient programming professionals. However, more than technical expertise is needed for successful software development; code quality assurance and effective teamwork are also indispensable. Unfortunately, recent graduates often exhibit deficiencies in programming, code quality assurance, and teamwork skills, highlighting the need for improvements in these areas. In response to these challenges, this study presents ProgEdu4Web, an innovative Automated Programming Assessment System designed to evaluate code quality and teamwork proficiency in web programming. The system integrates automatic source code evaluation tools for static code analysis, ensuring timely feedback on coding quality issues. To assess the effectiveness of ProgEdu4Web, a questionnaire and hypothesis model was employed, surveying a cohort of 111 students enrolled in web programming classes. The study's findings demonstrate that the system significantly alleviated instructor workload while motivating students to enhance their web programming with good code quality and teamwork abilities.

In the ever-evolving landscape of 21st-century education, this research seeks to understand the challenges of on-time graduation for students in two related computing majors. In particular, we focus on the Universidad Católica del Norte computing engineering programs in Chile, specifically the “Computing and Informatics Civil Engineering” (ICCI) and “Computing and Informatics Execution Engineering” (IECI) programs. We developed a machine-learning-based model using random forests to predict delays in submissions of the final report of graduation projects, the key step in the graduation pipeline of the analyzed students. We had access to a data set comprised of 209 students in the period from 2013 to 2017, out of these students, only 111 completed all their graduation requirements. Thus, we focused on this subset of students for the analysis. Our analyses of results indicate that individual advisors minimally contribute to predicting timely or late submissions, emphasizing the need for a holistic approach. In contrast, the specific major, graduation modality, and time in the program play crucial roles, with GPA emerging as the most influential factor (24.06%). Notably, the “Professional Work” modality exhibits a moderate positive correlation with late submissions, contextualized by students' employment commitments. The study's predictive model offers actionable insights for educators and administrators, identifying at-risk students and advocating for personalized support strategies. This research contributes to the ongoing dialogue on enhancing educational outcomes by integrating data-driven approaches tailored to diverse student profiles.

To adapt to the new direction of engineering education reform in China under the background of new engineering, and to promote the transformation and upgrading of intelligent manufacturing professionals, a virtual simulation practice teaching system based on Internet technology is constructed. Relying on “online + offline” resources, experimental modules are designed, a course resource library is built, a digital cloud platform is developed, and a “One Body, Two Wings” virtual simulation teaching and training system is established. Practice has shown that the virtual simulation system has the advantages of immersion, interaction, imagination, and reality, which can meet the basic needs of users and provide a safe and immersive experimental operation experience. The comparative data on practical effectiveness shows that virtual simulation teaching has significantly improved students' theoretical knowledge, practical skills, and innovative ability, making graduates highly desirable by enterprises. The virtual simulation system not only enhances user experience, but also improves students' learning effect, professional cognitive ability, and professional practice ability. The system provides strong technical support for the cultivation of intelligent manufacturing professionals, and is an efficient and practical tool in the field of teaching and training, with broad application prospects.

Coulomb's law and Gauss's law are two fundamental laws in electrostatics that involve electric charges and electric fields. These concepts that are not visible physically in real life can be challenging for students to understand, especially when it involves complex charge distributions. This paper presents a MATLAB-based graphical user interface (GUI) to visualise the concept of Coulomb's law and Gauss's law as well as a serious snake game to reinforce users' knowledge. This MATLAB GUI features a diverse range of 3-dimensional (3D) visualisations illustrating electric field attributes such as electric force, charge distribution and electric flux. These visualisations dynamically adapt to user-defined parameters, offering a rich and varied exploration of the electric field's characteristics without confining the scope to a specific count of models. In Coulomb's law section, the GUI plots the electric forces in 3-D for dipole, three-point charges, and unlimited charge. The Gauss's law section consists of windows for illustration of the fundamentals of Gauss's law for the electric field, and the illustration for the application of Gauss's law in finding the electric field for a point charge, infinite line charge and infinite plane charge. The serious snake game developed in this work allows students to engage in quizzes related to Coulomb's law and Gauss's law to serve as a tool for reinforcing their learning outcomes. The MATLAB-based GUI was chosen as the platform due to its excellent visualisation capabilities, ease of programming and capability to act as a powerful tool to enhance the learning of Coulomb's law and Gauss's law for students.

This study proposes and demonstrates how computer-aided methods can be used to extend qualitative data analysis by quantifying qualitative data, and then through exploration, categorization, grouping, and validation. Computer-aided approaches to inquiry have gained important ground in educational research, mostly through data analytics and large data set processing. We argue that qualitative data analysis methods can also be supported and extended by computer-aided methods. In particular, we posit that computing capacities rationally applied can expand the innate human ability to recognize patterns and group qualitative information based on similarities. We propose a principled approach to using machine learning in qualitative education research based on the three interrelated elements of the assessment triangle: cognition, observation, and interpretation. Through the lens of the assessment triangle, the study presents three examples of qualitative studies in engineering education that have used computer-aided methods for visualization and grouping. The first study focuses on characterizing students' written explanations of programming code, using tile plots and hierarchical clustering with binary distances to identify the different approaches that students used to self-explain. The second study looks into students' modeling and simulation process and elicits the types of knowledge that they used in each step through a think-aloud protocol. For this purpose, we used a bubble plot and a k-means clustering algorithm. The third and final study explores engineering faculty's conceptions of teaching, using data from semi-structured interviews. We grouped these conceptions based on coding similarities, using Jaccard's similarity coefficient, and visualized them using a treemap. We conclude this manuscript by discussing some implications for engineering education qualitative research.

This article presents the LIZGAIRO project, which aims to promote students’ understanding of fundamental engineering concepts and their ability to apply them to solve real-world problems through project-based learning. The project was run over a 3-year period, with students from the telecommunication degree at the Universidade de Vigo. The Scrum framework, an agile project management framework, was used to promote effective teamwork, active participation and ownership among students. The article describes the effectiveness of the project and its impact on students' learning success, delivered project quality and overall skills acquisition. After implementing LIZGAIRO, both teachers and students reported a positive impact on student learning, with high levels of essential skill acquisition and project quality. Although learning the framework fundamentals takes time for both teachers and students, the adaptability of Scrum and the use of continuous assessment were found to be beneficial.

High academic failure and dropout rates in engineering courses are significant worldwide concerns attributed to various factors, with academic performance being a critical variable. This article provides a methodology to estimate the performance risk of students in engineering schools. Risk analysis is a strategy to evaluate academic success, which provides a set of methods to analyze, understand, and predict student outcomes before enrolling in specific majors or challenging college courses. This article develops a methodology to estimate fragility curves for students entering an engineering course. The fragility function concept, borrowed from the earthquake engineering field, estimates the likelihood of success in a course, given relevant student metadata, such as the grade point average, thus comprehensively addressing student performance variability. A student academic success prediction model enables instructional designers to make informed decisions. For example, fragility curves can help achieve two goals: (i) assessing the population at risk for a course to take actions to improve student success rates and (ii) assessing a course's relative difficulty based on its fragility function parameters. We demonstrate this methodology through a case study comparing the relative difficulty of two engineering courses, Statics and Solid Mechanics, at a university in Colombia. Given that Statics serves as a prerequisite for Solid Mechanics, deficiencies in the former can significantly impact student performance in the latter. The case study results reveal that Solid Mechanics poses a higher risk of academic failure than Statics, underscoring the importance of a strong foundation in prerequisite courses.

The actual open-source hardware and software tools offer a rich set of options for developing didactic tools to improve the teaching–learning process of various areas of engineering. Using low-cost sensors, in conjunction with free software development tools, allows the creation of educational interfaces and platforms offering very acceptable performance and precision that help the student to become familiar with the basic principles that govern professional equipment. In this work, we propose a low-cost system for data acquisition specially designed to improve the learning experience of experimental mechanics. To achieve this purpose, we use open-source software and hardware tools to create a piece of educational equipment that is fully configurable for different sensors. We present the experimental results of two case studies: the vibration analysis of a rotor-bearing system using acceleration signals and a free-vibration study using a xylophone and a low-cost microphone. The proposed platform helps authors to complement a 4-month course named Vibration, intended for mechanical engineering students. The students who participated in the study demonstrated an improvement in their comprehension of vibration theory and modal analysis using the finite element technique. The feedback from students indicates that 84% of the participants are highly motivated to learn more about vibrations and experimental mechanics.