Computer Applications in Engineering Education最新文献

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.

Nowadays, many digital learning tools can be integrated into a course to enhance the teaching–learning process. It is a fact that technology is changing how students learn and how professors teach. The growing utilization of AI chatbots in education to support students' learning underscores the importance of identifying critical aspects related to students' acceptance of this technology. Teachers require a deeper understanding of how students perceive and respond to chatbot assistance, as well as methods to measure levels of satisfaction, engagement, and knowledge acquired while using these technological tools. The aim of this study was, on the one hand, to illustrate the integration of a chatbot into a course to assist students in solving specific learning activities. On the other hand, it aimed to analyze the correlation between the factors influencing university students' intention to use chatbots and their perceived usefulness. It is noteworthy that the learning activity integrated two technologies: an Integrated Development Environment and a chatbot, offering additional educational value to students. The Technology Acceptance Model was used to measure the students' perceived usefulness and attitude toward this digital learning tool. The findings of this study provided insights to faculty on how to integrate and use chatbots to enhance students' engagement and motivation. The results revealed that the perceived usefulness of chatbots does, in fact, influence students' intention to use them. Most of the students expressed satisfaction with having a chatbot to assist them in solving exercises. Students highlighted that one of the most significant benefits was the 24/7 chatbot's support availability and its user-friendly interface.

Software simulation programs and virtual reality (VR) have become powerful tools for several educational purposes, and recently, they were used in a wide range of applications. In cases of inaccessibility to labs, workshops, or industries, as happened before in the coronavirus disease 2019 pandemic, these tools could be effectively integrated with practical lab experiments. In this study, a computational fluid dynamics (CFD) simulation and a VR module were utilized to simulate heat transfer by conduction through various experimental cases. The CFD simulation results were obtained using ANSYS/FLUENT software. Meantime, the experimental data were obtained by carrying out three experiments of heat conduction with different heat transfer rates through simple, composite, and different cross-sectional area bars. At last, the experimental procedure and devices used were virtually constructed using SolidWorks software as three-dimensional models, which were then extruded into VR and augmented reality models. It was found that the simulation results closely align with the experimental ones, and the temperature profile in both cases has the same behavior with small differences, which indicates the validity of the developed module to be used as a simulation of the actual experiments. In addition to improving knowledge of heat transfer principles, this combination of simulation and VR technology advances many Sustainable Development Goals (SDGs), including advancing quality education (SDG 4) and innovation in higher education (SDG 9). Additionally, this method assists in achieving the course learning objectives by mimicking real-world lab experiments, guaranteeing that students graduate from the course with the required information and abilities.

This paper introduces Education Platform for Equivalent Resistance (EDUPERES) and Gamification of Equivalent Resistance (GAMERES), two different parts of a new computer aided education platform with interactive and imaginative design and game elements, which have been created during 4 years. In Vocational School of Transportation of Eskişehir Technical University, electrical circuit analysis is a compact course which exhaustively yet quickly covers all the DC and AC circuit concepts. During the course, it is observed that some students have difficulties in some basic capabilities such as calculation of equivalent resistance of either a standard or a compact circuit, dividing currents, and so on. More sadly, this inability spreads to the rest of the semester. This study aims to overcome this issue through a smart interactive education platform and gamification. The first part, that is, EDUPERES visualizes and animates the solutions of electrical circuits. To make the students exercise circuit questions, it provides an interface which simulates the comfort of using paper and pencil. This platform is powered by a smart algorithm to watch almost all of the actions of the students and to give prompt warnings. It allows students to transform the shapes of compact circuits during circuit reduction. The second part, that is, GAMERES imaginatively converts the circuit questions into action, competition, shooter, puzzle, and maze games. Via box plots and Mann–Whitney–Wilcoxon nonparametric tests, it is shown that EDUPERES and GAMERES can significantly increase students' learning. The success of EDUPERES and GAMERES is discussed with regard to previous studies and gamers' motivations.

Digital societies require professionals in the Technology and Engineering sectors, but their lack, particularly of women, requires a thorough understanding of this gender gap. This research analyzes the beliefs and opinions of university engineering students about the gender gap in their professional fields by means of a community detection algorithm to identify groups of students with similar belief patterns. This study leverages a community detection algorithm to analyze the beliefs of 590 engineering students regarding the gender gap in their field, together with a correlational and explanatory design using a quantitative paradigm. A validated questionnaire focusing on the professional dimension was used. The algorithm identified three student communities, two gender-sensitive and one gender-insensitive. The study uncovered a concerning lack of awareness regarding the gender gap among engineering students. Many participants did not recognize the importance of increasing the representation of professional women, maintained the belief that the gender gap affects only women, and assumed that men and women are equally paid. However, women show a higher level of awareness, while men perceive the gender gap as a passing trend, which is worrying. Students recognize the importance of integrating a gender perspective into university and engineering curricula. It is worrying that many students doubt the existence of the gender gap and that both genders lack knowledge about gender gap issues. Finally, community detection algorithms could efficiently and automatically analyze gender gap issues or other unrelated topics.