Computer Applications in Engineering Education最新文献

The teaching of parallel programming in undergraduate engineering programs poses challenges related to high cognitive load and limited student engagement. This study presents a pedagogical strategy aimed at facilitating meaningful learning through a reduction in problem domain complexity and active learning techniques. The proposed approach was implemented in a core course on multiprocessor programming in an undergraduate Computer Engineering degree. Three well-known problem patterns were selected to guide students through different parallel implementations (OpenMP, PThreads, and MPI). This problem reduction strategy enabled scaffolded learning experiences while minimizing the cognitive barriers typically associated with high-performance computing education. The approach was designed to promote student motivation and autonomy through guided discovery, hands-on sessions, and peer interaction. Results from student feedback and course outcomes suggest that this methodology improved comprehension, confidence, and engagement. The article discusses the implications of using reduced problem domains and active learning for teaching parallelism in engineering education, and proposes a replicable framework for similar contexts.

With the rapid advancement of the renewable energy industry, hydrogen fuel cells, known for their high energy conversion efficiency, low pollution, and minimal noise during operation, remain at the forefront of industrial research. Consequently, the production processes and performance testing methods of hydrogen fuel cells have become essential components in the curricula of relevant educational institutions. This study develops a virtual simulation engineering education software based on Unity3D, specifically designed to facilitate learning for students and educators in secondary vocational, higher vocational, and tertiary institutions regarding hydrogen fuel cell performance testing. By leveraging existing preparatory materials, the software integrates core professional knowledge, such as the working principles and system structures of hydrogen fuel cells. The theoretical and practical teaching content was determined based on course design requirements. Furthermore, the development process was outlined, including software design, model and scene construction, implementation of interactive functions, software testing, and deployment. Evaluation of the software's functionality and educational effectiveness revealed positive feedback from both teachers and students. The software enhanced students' understanding of hydrogen fuel cell performance testing, improved teaching and learning efficiency, and contributed to greater educational equity.

As online education rapidly grows, traditional engineering education faces challenges such as limited resources, lack of interaction, and insufficient personalized support. This study proposes an Intelligent Teaching Assistant (ITA) system integrated with AI, applied in a “Dual-Teacher” online model, to enhance teaching effectiveness and student experience in engineering courses. This study assesses the ITA system's application in the “Dual-Teacher” model. We hypothesize that the system can improve students' learning experience, engagement, self-efficacy, and academic performance by providing personalized support, real-time Q&A, and emotional feedback. This study designed the ITA system architecture based on an engineering student needs survey and developed the “ChatZJU” ITA system, which was tested in the “Computer Architecture” course. A total of 80 third-year undergraduates were randomly assigned to the experimental group (“Dual-Teacher” model with ITA system support) and the control group (traditional teaching model). Data were collected through questionnaires, academic performance records, and engagement metrics, and were analyzed using statistical methods analysis. The experimental group showed significantly improved learning experience, engagement, and academic performance. The ITA system's personalized learning paths, Q&A, and emotional support enhanced motivation and participation. The ITA system effectively addresses the limitations of traditional online courses, improving student outcomes. Future research will focus on refining algorithms, expanding applications, and enhancing emotional support features.

The design and implementation of seismic structures in architecture are essential for enhancing safety and accessibility during earthquakes. However, many Indonesian architecture students lack the skills to design and analyze earthquake-resistant buildings. This gap is especially concerning given Indonesia's high vulnerability to seismic events, where building safety and resilience are critical. This paper examines the introduction of instructional simulations in an undergraduate architecture program with a specialization in earthquake-resistant design, utilizing Rhino-Grasshopper software. The teaching methods focus on improving earthquake resistance through advanced computational modeling, with an emphasis on structural optimization and performance-based design. Applied to second-year students, this approach led to 80% of them effectively incorporating simulation tools into their project designs. This method offers a comprehensive framework for integrating earthquake-resistant design into architectural education, providing a practical guide for students in earthquake-prone regions like Indonesia.

Recent studies suggest that working with ChatGPT can generate more creative outcomes than humans alone. However, does ChatGPT retain its creative edge when humans have access to alternative information sources, such as Google Search or a human peer. This study addressed this question through a quasi-experiment with 230 Chinese university students in three groups (the human–ChatGPT group, the human–Google group, and human–human dyads) and a mathematical creativity task. The results showed that the human–ChatGPT group generated the most flexible and fluent solutions, while the human–human group produced the most original solutions in solving mathematical creativity tasks. The human–human group accurately assessed their actual performance, while the human–ChatGPT group overestimated it, and the human–Google group underestimated it. Furthermore, the study revealed that the human–Google group encountered greater difficulties, invested more effort, and reported lower levels of interest compared to the human–human and human–ChatGPT groups. Students found Google less useful than ChatGPT and their human peers. Similarly, students also found Google less effective than ChatGPT and their peers in enhancing self-efficacy. These findings highlight the benefits of human–human and human–ChatGPT co-creation in fostering mathematical creativity and call for further research on how to combine human inspiration with ChatGPT support to enhance it.

Program Outcomes (POs) are critical for engineering program accreditation, yet traditional evaluation methods often lack objectivity, consistency, and timely feedback. While machine learning (ML) has been applied to predict general student success, its use for predicting PO attainment levels from early academic data remains underexplored. This study introduces an integrated framework for computer engineering programs, combining a systematic PO assessment model with ML-driven prediction. The assessment model quantifies PO attainment rates (POAR) from weighted course assessments, mappings between Course Learning Outcomes (CLOs) and POs, CLO-assessment relationships, and student grades. Using these POARs, various ML techniques were trained on historical data from 327 graduates, utilizing their grades from 25 early-semester courses and graduation POARs. Our findings demonstrate that POARs can be successfully predicted from this early data, achieving a mean absolute percentage error around 5%. Consequently, this study presents a scalable and objective tool that (1) provides a systematic framework for POAR measurement; (2) offers an effective ML model for predicting graduation POARs of students; and (3) delivers data-driven insights for proactive student support, timely interventions, and evidence-based curriculum optimization, thereby supporting continuous program improvement and accreditation efforts.

When performing mathematical modeling, engineering education primarily focuses on understanding first principles to represent a phenomenon or process. With the advent of Machine Learning (ML), data-driven approaches to mathematical models have disrupted and challenged these traditional teaching/learning approaches. Data interpretability captures different dimensions, since engineers seek accurate predictions, causation, and analyze the interaction effects of process variables when modeling. While the effects of interaction effects have been previously taught using regression techniques, complex datasets might require employing alternative methods to precisely capture the complexity and nonlinear behavior. In this study, we present the conscious design of a novel teaching and learning approach for data-driven modeling, using a case study of the degradation of lithium-ion batteries to illustrate the interaction effects in modeling. We have selected there different interaction effects approaches when modeling: a regression model, exploratory data analysis, and ML. A validation and preassessment of the proposed teaching strategy were conducted to enhance the preparation and implementation of an in-class session, including strategies for its classroom integration. Our approach is innovative within the undergraduate engineering education context, since it introduces and highlights the significance of interaction effects to enhance students' abilities to interpret data, and think critically. This approach is totally reproducible, may be applied across other engineering disciplines, and has practical implications that could lead to its potential assimilation and utilization in industry.