Computer Applications in Engineering Education最新文献

This paper describes a methodological study carried out between 2018 and 2022, at Rey Juan Carlos University, focused on the subject monitoring and control systems within a master's program in Industrial Engineering. The study proposes an innovative teaching strategy using problem-based learning and project-based learning methodologies. The projects undertaken are based on Internet of Things (IoT) systems aimed at enhancing weather stations, services and facilitating real-time decision-making. Inspired by our experience in the development of Industry 4.0 projects, we have designed a methodological strategy for this subject that focuses on providing students with the necessary knowledge and skills in the field of Control and Monitoring Systems and the IoT to develop real monitoring and control systems. The approach emphasizes interdisciplinary problem-solving, with students working collaboratively in stable teams. Throughout the 16-week course, tasks of increasing complexity are completed, resulting in the development of a complete system. The practical approach of the course and its relation to real applications motivates students, resulting in better performance. The acquired techniques and skills from the course are broadly applicable across engineering disciplines.

This systematic literature review explores the impact of innovative teaching approaches on student motivation and academic achievement in online blended learning. A thorough search of five electronic databases for studies published between January 2009 and May 2023 yielded 1468 records. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population, Intervention, Comparison, Outcome and Study-design (PICOS) frameworks as the basis for the eligibility criteria, 47 studies were eligible and reviewed. The findings revealed that the effects of motivation were influenced by various factors, such as the blended course design, instructor's support, learning environment and the student's characteristics. The common innovative teaching and learning techniques and tools which advanced better teaching and learning were found to be interactive lessons, the use of virtual reality technology, artificial intelligence, project-based learning, inquiry-based learning, jigsaw, cloud computing, flipped classroom, peer teaching, peer feedback, crossover teaching and personalised teaching. These techniques positively and significantly affected motivation and academic achievement. Furthermore, results also suggest that educators should carefully consider the needs and preferences of their students when designing their courses and curricula to motivate and support students to achieve their full potential. Based on these findings, instructor support through innovative teaching and learning is vital to sustaining meaningful, innovative interactions that motivate students and promote better academic achievements in innovative online blended learning. Therefore, this study proposed a framework that illustrates that when students are well motivated, they develop personal and academic qualities such as interest, confidence, belonging, cooperation and trust in the educational experiences, resulting in better academic achievement.

Thermodynamics is a branch of physics of high importance for engineering applications but is usually considered by most students as a rather obscure field, full of abstract concepts. Therefore, simple algorithms, which can exemplify the use of thermodynamic principles for practical situations, should be viewed as valuable teaching tools with large applications in engineering undergraduate courses. This point served as motivation for the present work, which proposes an alternative and simple computational approach for solving chemical equilibrium problems via successive reaction quotient calculations, both for single and multireactional systems. The code was written using MATLAB software; its fundamental theory was explained through a step-by-step approach and applied to both Shift and Boudouard reactions. Comparisons with ASPEN HYSYS and HSC Chemistry simulations corroborate its versatility and thermodynamic consistency. The full script is available in its entirety at the as supporting information together with the necessary text (.txt) files. Also, a user guide was provided to help students to replicate the results presented in the article.

Professional training presents a significant challenge for organizations, particularly in captivating and engaging employees in these learning initiatives. With the ever-evolving landscape of workplace education, various learning modes have emerged within organizations, and e-learning stands out as a prominent choice. This increasingly cost-effective and adaptable solution has revolutionized training by facilitating numerous learning activities, including the seamless integration of educational games driven by cutting-edge technologies. However, incorporating serious games into educational and professional settings introduces its own set of challenges, particularly in quantifying their tangible impact on learning and assessing their adaptability across diverse contexts. Organizations require a consistent framework to guide best practices in implementing e-learning combined with serious games in professional training. The primary objective of this research is to bridge this gap. Rooted in the methodology of Design Science Research, it aims to provide a comprehensive framework for creating and assessing adaptive serious games that achieve desired learning and engagement outcomes. The overarching goal is to enhance the teaching–learning process in professional training, ultimately elevating student engagement and boosting learning outcomes to new heights. The proposal is grounded in a review of literature, expert insights, and user experiences with Serious Games in professional training, considering learning outcomes and forms of adaptation as essential characteristics for developing or evaluating Serious Games. The result is a framework designed to guide learners toward improved learning outcomes and increased engagement. The proposal underwent evaluation through triangulation, involving focus groups and expert interviews. Additionally, it was utilized in the development and assessment of a Serious Game, offering new insights and application suggestions. This experiment provided an evaluation of the framework based on real courses. In summary, this investigation contributes to the development of evidence-based approaches for the effective use of Serious Games in professional training.

Computational thinking (CT) has been promoted worldwide by educational systems and is an essential skill for technological citizens. Various strategies have been planned and developed to help in introducing, improving, and delivering CT. One of the strategies is by creating and developing the supporting tools for CT learning. In this article, educational robotics (ER) is chosen as the focus tool to support CT learning. Each CT and ER has a massive field of study. There are various available reports determining the suitability of CT subject integrated with ER for students' learning. However, all students do not develop similar style of learning and thinking. There is difference in their personal traits. There is a lack of research that designed CT learning through ER specifically based on student's preferences. Besides, it resulted in a challenge to determine the suitability of CT and ER for different kind of preferences. Therefore, this study aimed to develop an adaptive learning (AL) framework for students to deliver learning of CT through ER. The framework consists of three submodels: domain model, student model, and adaptation model. One case study is defined, which is learning the introductory level of CT through ER (CTER). At the end of the study, it can be observed that the AL framework produced positive results in performance and perception for various student categories. It was noted that students utilizing the AL framework had superior understanding of CTER. Individually or collaboratively, all students who applied or did not apply the AL framework in studying the CTER introduction had positive learning outcomes.

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.