Computer Applications in Engineering Education最新文献

This work provides online learning modules and instructions on how educators can leverage these technologies to help students learn in a personalized online environment. In particular, we focus on Google Colab, and the features provided by the Gradio Python library to provide interactivity within these modules. The contributions of this work include: (1) Development of a teaching framework using Gradio/Colab that offers automated grading and feedback for both educators and students; (2) Design of a versatile proposal, accommodating beginners with a straightforward interface while addressing the needs of advanced learners; (3) Creation of a comprehensive set of examples tailored for teaching digital logic subjects, with adaptability for application in various computer science areas. (4) A classification of these example learning modules in terms of their learning level for the students; (5) A novel client-server approach based on Colab/Gradio, allowing teachers to manage the main notebook efficiently while providing a lightweight and reliable interface for students. The goal of this work is to further expose educators to the remarkable capabilities that cloud computing brings to online supplemental education, noting that large language models such as ChatGPT complement this work, in that chatbots will be able to guide students in these dynamic simulations.

Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open-source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data-generation module and a machine-learning model. The data-generation module produces a diverse and exhaustive data set for training and evaluating the machine-learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine-learning model demonstrates promising results with a prediction time of 634 ms, a log-loss reduction rate of 0.9848 and accuracy, precision, recall, and f1-score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open-source software development projects which have a direct impact on improving the quality of human life.

This study focuses on promoting women's participation in engineering education (WPEE), which is crucial for inclusive and innovative development in the engineering fields and key to achieving the United Nations Sustainable Development Goals (SDGs). Due to the global shortage of women engineers, there is a need. to find effective ways to increase WPEE. This study aims to identify key factors that influence WPEE, which should be prioritized in policymaking. By adopting a three-round Delphi survey and an improved fuzzy DEMATEL model, the findings reveal that the factors influencing WPEE are complex and multifaceted. Within the Chinese context, six factors, including hobbies and interest, employment expectation, parental occupation, incentive measures, social attitudes, and employment prospects, have been identified as key determinants of WPEE, exhibiting greater centrality and causality than others. This study not only provides empirical evidence from China but also introduces a novel approach to identifying key factors promoting WPEE, offering significant insights into global policy and practice.

The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time-consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad-hoc computer application for two-dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three-dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.

The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real-time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute-intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA-based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design-modeling-implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA-based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

Usability evaluation is a key element to ensure a positive user experience with any software and it is especially important in educational software tools where there are many different actors involved (lecturers, students, administrators, etc.). However, evaluating usability is not an easy task for nonexpert evaluators. To facilitate this evaluation task, this article presents a Methodology for Usability Testing (MUT) and a system (CALMUT) that assists nonexpert evaluators in the application of the methodology by automatizing the calculations and facilitating their interpretation. This can be very useful for learning and instructional designers but also to people involved in the decision of introducing or not a new educational software. To develop the proposal, a literature review of different usability metrics, methods, and systems was carried out first, followed by a selection and adaptation for novice usability evaluators. This article also presents a case study where lecturers tested the usability of an educational software following the proposal and shows that using MUT and CALMUT helps people without previous experience detect the main usability problems of educational systems before deciding whether to use them or not.

This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lacking digital skills. The study sought to answer several research questions, including the impact of online versus in-person teaching on preuniversity students and gender differences in Computer Science perception, and Computational Thinking skills performance. The study employed a four-phase methodology, consisting of pre- and posttraining measurements of Computer Science perception and Computational Thinking skills development through specific activities delivered in-person or online. The results indicate that in-person training is more effective for developing Computational Thinking skills, particularly at the secondary education level. Furthermore, there is a need to focus on maintaining girls' interest in Computer Science during primary school, as interest levels tend to decline significantly in secondary school. These findings have significant implications for Engineering Education in the context of digital transformation and the increasing importance of Computational Thinking skills in various fields of engineering. This study highlights the importance of developing Computational Thinking skills among preuniversity students and the need for effective training methods to achieve this goal and underscore the significance of investing in Engineering Education to prepare the next generation of engineers for the rapidly changing digital landscape.

Construction 4.0 promotes digital transformation through automation, robotisation, and the integration of systems and processes into digital environments, with direct links to real systems, using a wide range of technologies. The risk here is centred on having very advanced machines with people not prepared to use them. If the training is centred on teaching people, however, the risk is transferred to having overqualified equipment. In search of this balance, the study, analysis, and evaluation of human–machine interaction are crucial, as are correctly identifying the tools through which this interaction is achieved. Extended reality (XR), emerging technology within Construction 4.0, seems to be a tool that offers an environment conducive to achieving these interactions and meeting the objectives sought. In civil engineering, efforts have been directed towards the study and development of applications of XR experiences rather than the application of this technology in a transcendental way in civil engineering training. This research identifies developments in XR experiences and analyses their use, application methodologies, and training areas that include immersive training, as well as the relationship between XR and construction industry methodologies and technologies, such as building information modelling.

In this research, a microlearning strategy for Software Engineering supported by a mobile application was designed and implemented. The goal is to evaluate the motivation and learning outcomes in the specific context of Software Project Management, with the Scrum framework, in participants of a Software Engineering course at a Latin American higher education institution. An empirical investigation was conducted using a quantitative approach, a quasi-experimental design, and pretest–posttest measurements without a control group. A one-sample t-test for comparison of the means of a sample was used. Statistically significant differences were found between the theoretical and empirical mean of the variable motivation to learn in the specific context and the variable Stimulus for learning after interacting with the mobile application. The means were higher than the theoretical average of the scale, which suggests that the participants valued the mobile application positively. Regarding the learning outcomes of the Scrum framework, a paired sample t-test for comparison of means revealed an increase in posttest scores, although this rise was not statistically significant. Microlearning can increase the participants' motivation and promote learning in the specific context of Software Project Management. The mobile application has the potential to support microlearning since the participants felt highly motivated and agreed that its use facilitates learning, a key aspect of success in a microlearning strategy.