Computer Applications in Engineering Education最新文献

This paper presents a method for teaching quantum computing for engineering students of the Master's degree in Quantum Computing at the International University of La Rioja. The approach is designed so that students can acquire the basic concepts of quantum computing in a context in which they have no knowledge of quantum mechanics. The focus is therefore on the mathematical, physical and, above all, computational knowledge acquired by the student in engineering. Theoretical and practical concepts of quantum computation are introduced, as well as the most important quantum algorithms. The student is also taught to work with a real quantum device. The method was successfully tested using the IBM Quantum platform, which consists of real quantum devices and simulators where students were able to test the acquired knowledge and implement the learned algorithms. In a context where quantum computing-related degrees and subjects are starting to emerge, the aim of this work is to help not only future generations of engineers to study quantum computing, but also those responsible people for implementing such studies.

This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open-ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands-on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self-defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem-solving skills that can be applied in real-world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real-world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project-based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.

This article explores the use of adaptive learning platform (ALP) data to conduct early identification and provide support to students who have a low-performance outcome (C or lower) in a numerical methods engineering course. The data from assigned ALP lessons for two semesters was used to create decision-tree models to identify students who would benefit from advising and tutoring support. In the following two semesters, low-performing students were identified early in the semester and provided with support, and their performance was compared to their peers. The best-performing prediction model achieved an accuracy of 85% in predicting low-performing students in the third week of the course. The support included weekly one-on-one tutoring and advising sessions. Although only 23% of the identified students accepted support, they scored one-third a letter grade better than those who did not. Additionally, students who received support were invited to participate in a focus group at the end of the semester. Positive outcomes reported included improved understanding of course material, higher academic performance, advice on learning strategies, and guidance on non-course-related topics like internships and employment. Although most students valued receiving personalized invitations, a few felt singled out as low-performing. Students acknowledged the significance of individualized support, gave advice on how to word the invitation emails, and made helpful suggestions for improving help sessions, particularly in terms of personalization and recognizing their heavy academic workload.

There is a growing need to apply advanced control techniques in industrial processes, and the state space provides modern tools for solving these requirements. Engineers and scientists must be prepared to offer solutions for the industry according to the advances, requiring proper knowledge acquired from their careers in Universities. However, courses in state-space control theory in some educational institutions lack the practical experience to implement the controllers designed by students and only use simulations to enhance the understanding of this theory. Therefore, this paper proposes a didactic strategy for applied control learning within the framework of the state-variable control course through individual, portable, and low-cost modules. The method is based on the building of two types of electronic modules that allow students to perform a self-practical laboratory. In addition, classroom action research is applied, considering a purposive sampling of students. The research also considers theoretical and practical interventions to explain to students the use of each electronic module and the requirements for performing the tests. An evaluation tool is designed to assess the knowledge of the students before and after the intervention. The results show how students improve their performance in control theory and achieve more advanced concepts of control implementation. The first test showed that of all students enrolled in the course, only 5% obtained good grades. After the first practical intervention, up to 49% of the students obtained good grades. The final practical intervention allowed more than 80% of the students to achieve high grades.

With the popularization of engineering education professional certification, the goal of engineering education is more and more oriented to students' comprehensive ability, which also makes formative assessments more important in the process of talent training. As for a course, not only the assessment results but also the learning process of students should be paid more attention. By setting up rich and colorful process learning tasks, students can better achieve the course objectives. However, for the development of process learning, two problems require more attention. One is how to evaluate students' enthusiasm and learning effect and give an effective early warning, and the other is to analyze the effect of different learning tasks on students' final achievement of learning goal. In this study, on the basis of process learning historical data, a student process data evaluation and analysis model based on random forest is established. The data coming from Superstar Learn Platform is the process learning data of more than 300 students in a University. The analysis results show that appropriate curriculum design, course assignments and chapter tests have a significant impact on promoting students to achieve the course objectives. Besides, the timeliness of students' completion also plays a significant role. This analysis model can help students correct their learning attitude, help teachers better understand students and adjust teaching plans.

Higher Education programming courses usually present high levels of student failure and drop-out rates. Given this context, the use of educational video games is proposed as a strategy to increase the students' motivation and engagement, thus helping diminish such rates. However, there is a lack of empirical studies examining such effects, especially when they are proposed outside the formal curriculum (i.e., extracurricular) and when the students are enrolled in different Higher Education bachelors. This paper presents a cross-sectional study following a between-subjects design with 315 students (168 assigned to the experimental condition, 147 to the control condition) enrolled in one of the following bachelors: B.S. in Computer Science (where programming is a core subject), and B.S. in Statistics (where it is not). The study spanned two consecutive academic years. The outcomes were evaluated through a pre-/post-test schema and comparison of final course results to measure the effect on learning (objective assessment) and a survey to get the students' perceptions (subjective assessment). In addition, the level of participation was analyzed and compared between bachelors, considering the optional nature of the activity. Results show statistically significant differences in learning outcomes between the students in the experimental condition and those in the control group, without clear differences between Bachelors (the results are positive for both). In the subjective assessment and participation, the results are also positive, but, in this case, statistically significant differences between bachelors have been observed. These positive outcomes suggest its potential applicability to other Higher Education and Engineering courses.

The aim of this study is to better enable students to grasp the complex material science involved in engine casting, address various engineering challenges, gain precise control of the casting process, strengthen their practical skills, and enhance the effectiveness of teaching engine forging. Firstly, on the basis of real engine forging production, complete 3D modeling of each production line is carried out using the 3dsMax software. Then, the finished models are imported into the virtual simulation software Unity3D to arrange production layouts and perform interactive operations. Lastly, a user interface is designed for the built scene, and the software release is completed. Feedback from users indicates that the software designed in this project serves as a valuable reference and educational tool for students learning about engine casting and related knowledge. However, the complexity of the actual production process was simplified, and due to certain hardware and resource limitations, there remains a gap between the simulation and real production. Future updates and improvements to the software will be made using technical methods based on the feedback received.

The landscape of programming education is undergoing a transformative shift in the era of AI and machine learning. This research delves into the role of GPT-4, a state-of-the-art language model, in solving intermediate-level programming problems, focusing on the renowned LeetCode platform. For this, the work employs different programming problems from two LeetCode contests, providing a comprehensive evaluation of GPT-4's capabilities. The results reveal intriguing patterns in the model's behavior. Initial attempts, when provided with all inputs simultaneously, exhibit high accuracy, but subsequent attempts show consistent fluctuations, rarely surpassing the accuracy of the first attempt. Upon closer examination, a distinct pattern emerges in GPT-4's problem-solving approach, where the model iteratively refines its solutions, incorporating corrections in subsequent attempts. However, the lack of a historical context for past attempts raises questions about the model's attention span and its ability to rectify mistakes. Notably, GPT-4 consistently fails on the same test case with the same generated output, suggesting a potential limitation in addressing specific challenges. But, on leveraging human assistance to AI tools, the observations and patterns from the incorrect codes can be drawn and required adjustments to rectify the codes can be made. A direct result of this is observed in the increased success rate in problem-solving by students, rising from 68% in the moderate learning stage to 92% in the advanced learning stage. Hence, the presented work proposes a human-supervised methodology to leverage the AI-assisted code generation and employs that in improving the effectiveness of AI-assisted teaching–learning process.