Computer Applications in Engineering Education最新文献

Serious games can play a crucial role in developing competencies for the job market, offering an innovative and engaging approach to learning. This study uses FLIGBY to develop employability skills among computer engineering graduates. FLIGBY is a serious game that simulates a virtual company environment where players make strategic decisions, emphasizing the principles of flow and positive psychology. Immersion in realistic scenarios provides practical experience, contributing to the development of market-ready skills. A quantitative methodology based on descriptive statistics and hypothesis testing was adopted to measure the development of competencies for the job market using the Systems Engineering Competency Framework. The results show that competence development occurs mainly in the professional and managerial dimensions. In contrast, there is no development of core and technical competencies. Furthermore, the perception of the development of these competencies occurs mainly for students with more years of professional experience. The experiential nature of FLIGBY allows users to develop practical knowledge, promoting adaptability and resilience. This gamified approach accelerates the learning curve, preparing individuals for real-world challenges in the computer engineering workplace.

There are several initiatives underway to improve the learning of software developers. These attempts include the integration of GitHub into software engineering classes, the creation of learning management systems, gamification approaches, and collaborative learning platforms. These initiatives have demonstrated promise in boosting students' collaborative growth and cooperation abilities, emphasizing their potential influence on improving learning experiences in practical areas. Books, on the other hand, remain basic in education, but their physical size limits their ability to explore all practical elements of a topic in depth. This limitation requires more research and application of theoretical information in real-world circumstances. In this work, we address the issue of limited space in traditional books that frequently prevents complete presentation of practical elements of a topic. To address this issue, we propose an application that improves the reading experience and accelerates the learning process. To anticipate themes, we use a combination of latent Dirichlet allocation (LDA) algorithms and a generative pre-trained transformer. First, utilizing LDA to find potential topic keywords inside the text and then leveraging generative pretrained transformer to predict topic names based on the LDA produced keywords. In addition, a query builder module produces and executes queries depending on the current page's topic, obtaining real-world issues from Stack Overflow. The system classifies results by query-title similarity, question-answer ranking, and content quality before displaying them to users. This bridges the gap between theoretical knowledge and practical application. We illustrate the usefulness of suggested tool using simulations, comparison with existing tools and user studies. The majority of users provide favorable comments and find it interesting and helpful for improving the learning process.

The world around us is evolving due to Industrial Revolution 4.0. This revolution is driving the present-day education, that is, Education 3.0–creative education towards Education 4.0, that is, industry-centric education. The present academic environment in India is in the process of developing an educational ecosystem, which will incorporate equal access to education for all stakeholders and promote critical thinking, digital literacy, global awareness and scientific reasoning to name a few. Accordingly, it has become prudent to develop and validate a mechanism for assessing the various steps being taken to realign students to Education 4.0. This study involved 300 undergraduate, postgraduate, and PhD students from eight different schools across diverse program and course outcomes, who were asked to respond to a questionnaire on their own accord. The Pearson product-moment correlation test was used to statistically analyse the obtained responses. The test results showed that the questions were logical and valid, with a significance level of 0.0115. Furthermore, the reliability of the mechanism was also assessed using Cronbach's α score. This study indicated that three aspects—learning preferences, contribution of learning to knowledge, skills, and personal development, and time spent on learning and nonacademic activities—exhibited high reliability However, two aspects—the emphasis on mental functions and reading and writing—indicated to be of moderate reliability. Thus, it could be said that the questionnaire developed in this study was both reliable and valid for assessing the mechanism being undertaken to realign students to Education 4.0.

This manuscript proposes a methodology for teaching undergraduate engineering students fundamental concepts of artificial intelligence and robotics applicable in Industry 4.0 using a project-based learning strategy. A low-cost prototype of a two-degrees-of-freedom robotic arm was designed and implemented for monitoring applications, integrating the Internet of Things and artificial intelligence technology. Inverse and direct kinematics are applied in the robotic arm design, enabling the execution of desired trajectories by the robot's end effector. The robot, capable of following user-programmed trajectories, uses two servo motors with a 180° mobility range, integrated into a 3D printed structure made from polylactic materials, whereas the programmable logic was accomplished using an ESP32 microcontroller. Furthermore, the robot is controlled through a MATLAB GUI (Graphical User Interface), designed to obtain detailed process information such as activity status, trajectory type, and quality. The results demonstrate the feasibility of integrating various cutting-edge technologies to teach fundamental concepts of Industry 4.0. The proposed methodology could allow educators to design a robotics course where students are motivated by practical experience implementing impactful technologies beyond the academic realm.

Seeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the Decision Tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.

Software Engineering is a discipline frequently reflected in training strategies. This discipline requires certain levels of abstraction to achieve the competencies and skills necessary for the professional development of future software developers. The software industry increasingly demands that professionals in this discipline have social and human skills to achieve highly productive teams. Therefore, these teams should respond to such demands in a world with increasing dependence on technology and the development of software products. Traditional pedagogical strategies often need help adapting to the new generations of software engineers and responding in a limited way to the demands of teaching processes related to this discipline. This article evaluates a gamification-based strategy designed for the Software Engineering course at a Latin American higher education institution. This course addressed software project management as a training objective. Such a strategy was designed with a gamification-based model to influence the productivity of software development teams. The results of using the model show its efficiency and usefulness as a guide for implementing new strategies based on gamification that considers social and human factors (SHFs) to intervene in the productivity of software development teams. The challenges designed in the proposal presented managed to promote SHFs in the participants, according to the analysis of the prepared case study. According to these results, the factors considered relate to skills and experience in managing software development projects, motivation, and communication. The activities executed by the participants in the context of the case study strengthened the human side of the team and allowed its growth to achieve its objectives.

In an era where technology continually reshapes the landscape of professional practice, it has become relevant to equip engineering students with advanced computational skills beyond programming. This article presents a novel discipline-based framework designed to integrate advanced computational skills into engineering education. Responding to challenges such as the disconnection between computational abilities and domain-specific knowledge, and student demotivation due to overwhelming technological challenges, this study aims to validate the impact of the framework on domain learning, computational skill acquisition, and perceived future utility. Implementing a case study approach, we explore the development of high-performance computing skills within a project-based learning context in Civil Engineering. Results indicate significant improvements in students' understanding of both computational concepts and the engineering domain, evidenced by enhanced self-perception and positive Technology Acceptance Model outcomes. The framework facilitated a meaningful connection between computational skills and professional applications, as seen in students' project reflections. Despite the promising results, the necessity for instructors to possess and impart computational knowledge is highlighted as an important factor for successful integration. This study contributes to educational computing research by providing a scalable approach to embedding advanced computational skills in engineering curricula, addressing existing educational challenges, and suggesting directions for future research.

The launch of Generative Pretrained Transformer (ChatGPT) at the end of 2022 generated large interest in possible applications of artificial intelligence (AI) in science, technology, engineering, and mathematics (STEM) education and among STEM professions. As a result many questions surrounding the capabilities of generative AI tools inside and outside of the classroom have been raised and are starting to be explored. This study examines the capabilities of ChatGPT within the discipline of mechanical engineering. It aims to examine the use cases and pitfalls of such a technology in the classroom and professional settings. ChatGPT was presented with a set of questions from junior- and senior-level mechanical engineering exams provided at a large private university, as well as a set of practice questions for the Fundamentals of Engineering (FE) exam in mechanical engineering. The responses of two ChatGPT models, one free to use and one paid subscription, were analyzed. The paper found that the subscription model (GPT-4, May 12, 2023) greatly outperformed the free version (GPT-3.5, May 12, 2023), achieving 76% correct versus 51% correct, but the limitation of text only input on both models makes neither likely to pass the FE exam. The results confirm findings in the literature with regard to types of errors and pitfalls made by ChatGPT. It was found that due to its inconsistency and a tendency to confidently produce incorrect answers, the tool is best suited for users with expert knowledge.

Recently, the growing demand for computational fluid dynamics (CFD) skills in industry has highlighted the importance of their incorporation into university academic programs at both the undergraduate and graduate levels. However, many academic programs treat CFD tools as a “black box” in which users simply enter data without fully understanding the inner workings of the software or its application in real-world situations. Therefore, in the context of a civil engineering program in Chile, a novel approach combining problem-based learning (PBL) with CFD was introduced into the curriculum of a fluid mechanics course to foster crucial competencies. This comprehensive methodology allows students to acquire fundamental theoretical knowledge that is directly related to specific problems in the classroom. Subsequently, students measure relevant variables in the laboratory, ultimately using these data to build computational models for comparing and contrasting reality with simulations. To gauge the effectiveness and impact of this PBL strategy, both quantitative analysis of student performance and qualitative analysis through surveys were conducted. The results reveal a significant improvement in student performance with the implementation of the PBL methodology, alongside a positive perception among students regarding its implementation. This underscores its benefits for learning, motivation, and academic performance. Additionally, the implementation of PBL was found to enhance both theoretical and practical understanding of concepts related to fluid dynamics and CFD simulation.