Computer Applications in Engineering Education最新文献

{"title":"Computational Tools for Thermodynamic Teaching in Chemical and Metallurgical Engineering Courses","authors":"João Paulo Viana,&nbsp;Allan Amendola Santos,&nbsp;Rogério Navarro Correia de Siqueira","doi":"10.1002/cae.70055","DOIUrl":"https://doi.org/10.1002/cae.70055","url":null,"abstract":"<p>Thermodynamics is considered, among many engineering students, a very difficult branch of physics, especially regarding phase equilibria modeling, which usually often requires the use of commercial software and/or complex algorithms, rarely available for the general public. The present article provides new and simple Python numerical codes for calculating condensed phase equilibria, using pure metals and an alloy (Cu–Ni) as example, for a validity and consistency check, results were compared with both literature data and Thermo-Calc software's simulations. All parameters for computing pure metal's molar Gibbs energies and chemical activities for liquid and solid solutions were extracted from SSOL3 (SGTE) database. Regarding the pure metals (Bi, Cu, Nb, Ni, Pd, Pt, Sb, Ta, and Th), excellent agreement with literature values has been achieved in all cases, also including the solid–solid transition found for thorium. Regarding the Cu–Ni alloy phase equilibrium behavior, the proposed algorithm has also resulted in a quantitative agreement with literature experimental data and equilibrium liquidus temperatures and solid phase compositions calculated with Thermo-Calc software together with the SSOL2 database. The authors believe that the proposed algorithms could be of valuable use as teaching tools in metallurgical or chemical engineering courses, for example, through computational exercises to predict equilibrium conditions (transition temperatures, phase compositions) and or thermodynamic properties (molar Gibbs energy, enthalpy, and entropy), as exemplified by the activities proposed in the two exercise lists provided as Supporting Information. It is important to note that, although only metallic systems have been explored as examples, the same logic and thermodynamic principles can be applied to other phase equilibria problems involving inorganic condensed phases, for which accurate molar Gibbs energy models can be constructed or obtained from reliable databases.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the Impact of Mathematics Ancillary Courses on Computational Programming Intelligence of Computer Science Students Using Machine Learning Techniques","authors":"Emmanuel Chukwudi Ukekwe,&nbsp;Nnamdi Johnson Ezeora,&nbsp;Adaora Angela Obayi,&nbsp;Caroline Ngozi Asogwa,&nbsp;Assumpta Obianuju Ezugwu,&nbsp;Folakemi O. Adegoke,&nbsp;Jude Raiyetumbi,&nbsp;Bashir Tenuche","doi":"10.1002/cae.70054","DOIUrl":"https://doi.org/10.1002/cae.70054","url":null,"abstract":"<div>\u0000 \u0000 <p>Mathematics courses make up a good percentage of the undergraduate curriculum in the Computer Science and Engineering discipline. It is however important to ascertain how the mathematics courses impact the programming skills of the students. This article examines the impact of mathematics ancillary courses on the Computational Programming Intelligence (CPI) of Computer Science and Engineering students. Using the results of Computer Science students on mathematics and programming courses for seven (7) sessions, Random forest regression and K-means clustering machine learning models were used to study the relationship between the ancillary courses and their performance in programming courses. A Pearson correlation coefficient was computed to assess the linear relationship between five (5) Mathematics ancillary courses and the programming courses. A significant positive correlation (0.29, 0.27, 0.20, 0.10, and 0.09, <i>p</i> = 0.02) was obtained with Linear algebra having the highest and Mathematical methods the least. Consequently, variable importance results show that linear algebra had the highest impact on CPI while Mathematical methods had the least in the following order (29.11%, 20.39%, 19.80%, 17.15%, and 13.55%). Mostly female students of age range 19–20 were found to have been positively impacted more by the mathematics courses. A curriculum guide was presented based on the findings.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ArtEM: Enhancing the Understanding of Integer and Modular Arithmetic Through Interactive Learning Software","authors":"Violeta Migallón,&nbsp;Héctor Penadés,&nbsp;Jose Penadés","doi":"10.1002/cae.70053","DOIUrl":"https://doi.org/10.1002/cae.70053","url":null,"abstract":"<p>Integer and modular arithmetic is a fundamental area of mathematics, with extensive applications in computer science, and is essential for cryptographic protocols, error correction, and algorithm efficiency. However, students often struggle to understand its abstract nature, especially when transitioning from theoretical knowledge to practical applications. To address these issues, interactive educational software can play a crucial role in supporting the learning process. In this study, we present ArtEM (Integer and Modular Arithmetic), a Java-based tool that was developed to assist both students and instructors in exploring topics related to integer and modular arithmetic. ArtEM offers an intuitive approach, allowing users to experiment and to gain a deeper understanding of the subject. A survey conducted among students at the University of Alicante (Spain) assessed their satisfaction with ArtEM, which enabled a detailed analysis of key aspects, including its usability, content quality, and didactic effectiveness. These dimensions were crucial in evaluating how well ArtEM supports learning and enhances the overall educational experience. The results indicated that students found ArtEM user-friendly, providing a seamless learning experience. Feedback on content quality emphasised its relevance and clarity, while participants noted that the tool effectively supported their learning objectives. Data analysis also demonstrated that ArtEM improved students' understanding of complex concepts, encouraged independent learning, and contributed to improved academic performance. A remarkable improvement in students' ability to apply theoretical knowledge to practical scenarios was observed, underscoring the effectiveness of ArtEM in fostering deeper learning and better educational outcomes.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Artificial Intelligence Technology on Students' Computational and Reflective Thinking in a Computer Programming Course","authors":"Christian Basil Omeh,&nbsp;Chijioke Jonathan Olelewe,&nbsp;Ifeanyi Benedict Ohanu","doi":"10.1002/cae.70052","DOIUrl":"https://doi.org/10.1002/cae.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examined impact of artificial intelligent technology on computational and reflective thinking skills development in a computer programming course. With intact classes and a nonequivalent pretest-posttest group, the study adopted a quasi-experimental research design. Hundred and twenty second-year students studying computer science education and enrolled in computer programming courses (COS 201 and COS 202) at six universities in southeast Nigeria make up the study population. The study sample comprises of 75 females and 45 males' students. Findings showed that students worked collaboratively with aid of artificial intelligence technology to develop critical thinking skills, algorithm skills and problem-solving skills among others which are components of computational thinking. Also, students' academic achievement was seen to be significantly improved in programming knowledge and skills, and students' reflective thinking skills were also developed as a result of the intervention. The findings show that the use of problem-based learning experience is introspected with artificial intelligence technology is supported by the use of online learning platform is crucial in the development of CT skills. This study recommends that both context-based learning and problem-based learning introspecting with artificial intelligence technology which are innovative pedagogy enhance the development of CT independently but differ in specific domains.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Knowledge Gain and Retention in IoT Circuit Assembly Using Mobile Augmented Reality Technology","authors":"Meng Chun Lam,&nbsp;Hadi Bashar Khalid Hadi,&nbsp;Dahlila Putri Dahnil,&nbsp;Nur Asylah Suwadi,&nbsp;Nazatul Aini Abd Majid","doi":"10.1002/cae.70045","DOIUrl":"https://doi.org/10.1002/cae.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>Augmented Reality (AR) offers potential benefits in assembly training, yet there is a scarcity of research on knowledge retention when utilizing 3D model and animation overlays through Mobile Augmented Reality (MAR). This study investigates the influence of MAR applications, leveraging the signaling principle through 3D animated models, on knowledge gain and retention in a complex Internet of Things (IoT) assembly task. In this regard, this study developed a MAR framework and application to facilitate IoT assembly training. A comparative study was conducted with 40 participants, equally distributed between the MAR and paper manual groups based on prior knowledge and AR familiarity. The evaluation consisted of three phases: a pre-test, an immediate post-test, and a delayed post-test. Data collection involved knowledge tests, task completion time, error rates, usability and subjective feedback. Results showed significant knowledge gain in both groups, with the MAR group achieving a 21% increase and the paper group 15%. In terms of knowledge retention, both approaches were equally effective in helping users retain knowledge and improve task completion performance by reducing task completion time. Notably, the MAR group (0.5 error rate) made fewer errors than the paper group (1.35 error rate). Additionally, MAR demonstrated higher effectiveness based on Perceived Usefulness, Ease of Use, and the NASA Task Load Index. These findings suggest that while both methods support knowledge retention, MAR with better accuracy and usability, making it a valuable tool for IoT assembly training.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does Gender Influence the Learning Process of Computational Thinking in Secondary Education?","authors":"Lía García-Pérez,&nbsp;David Roldán-Álvarez,&nbsp;José M. Cañas","doi":"10.1002/cae.70050","DOIUrl":"https://doi.org/10.1002/cae.70050","url":null,"abstract":"<p>The gender gap in STEM disciplines both in the workplace and in university education is still a reality. Computational thinking (CT) is deeply related to these disciplines. CT is fundamental to problem-solving and critical thinking, skills that are core to STEM disciplines. CT involves skills like algorithmic thinking, pattern recognition, and abstraction, all of which are necessary for tasks in fields such as engineering, computer science, mathematics, and science. It is important to understand gender differences, if any, in the acquisition of CT skills, so that we can develop more effective teaching strategies for all. This study analyses the gender differences in the development of CT skills (measured with a standard test) in 149 students of a Spanish secondary school, in three courses between 12 and 14 years old, before and after classroom work sessions with a 3D robotics simulator. In the empirical evidence a relevant a priori gender difference was detected in the first course, likely related with prior experiences on computer programming, but boys and girls finish the first year with similar CT levels. Disparate results appear for second year (male students improve more) and third year (female students improve more) which demands further study.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subject: Join Us in Making CAE's 35th Anniversary Special Issue a Landmark Publication","authors":"Magdy F. Iskander","doi":"10.1002/cae.70051","DOIUrl":"https://doi.org/10.1002/cae.70051","url":null,"abstract":"","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Large Language Models Integration in Higher Education: A Case Study in a Mathematics Laboratory for Civil Engineering Students","authors":"Nikolaos Matzakos,&nbsp;Maria Moundridou","doi":"10.1002/cae.70049","DOIUrl":"https://doi.org/10.1002/cae.70049","url":null,"abstract":"<p>This study investigates the integration of large language models (LLMs) alongside computer algebra systems (CAS) in a mathematics laboratory for civil engineering students, examining their combined impact on problem-solving and inquiry-driven learning. The intervention was designed using the integrate LLMs alongside CAS (ILAC) approach, which structures the inquiry process into key phases, guiding students through exploration, hypothesis testing, and solution validation. Six structured activities were implemented and assessed using quantitative and qualitative methods. Findings reveal that LLMs enhanced conceptual understanding, clarified methodologies, and assisted with command syntax, while CAS ensured computational accuracy and result validation. Many students critically cross-verified LLM-generated results with CAS, though some relied solely on LLMs, highlighting the need for better guidance on tool usage. While LLMs fostered engagement, skepticism remained regarding their ability to address deeper mathematical deficiencies. The intervention led to moderate improvements in students' familiarity with AI tools, though its short duration and the use of general-purpose LLMs limited perceived usefulness. To maximize educational benefits, future implementations should consider longer interventions, advanced training in prompt engineering, and tailored AI solutions.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration and Application of Project-Based Teaching Integrating Scientific Research Achievements in Electronic Comprehensive Design Course","authors":"Hongxing Ma,&nbsp;Lin Li,&nbsp;Yun Ma,&nbsp;Xuan Liu,&nbsp;Shanglin Yang","doi":"10.1002/cae.70047","DOIUrl":"https://doi.org/10.1002/cae.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>Effective teaching content design, implementation methods, and a scientifically rigorous evaluation system are essential for cultivating students' innovative practical abilities and enhancing teaching quality in the electronic comprehensive design course. Addressing the challenge of ineffective integration between theory and practice in the undergraduate electronic engineering in Chinese universities, this study leverages the framework of engineering education certification to explore a project-based teaching model that integrates scientific research achievements into the electronic comprehensive design course. This study aims to solve the key limitations of traditional project-based teaching, specifically its disconnect from cutting-edge technology and lack of dynamic feedback, by constructing a “research feedback teaching” mechanism. A three-dimensional screening framework—comprising technical feasibility, teaching adaptability, and social demand correlation—is developed to decompose complex scientific research projects into progressive teaching tasks that span basic skills, unit design, and system engineering, thereby enabling a comprehensive, full-process electronic design practice. To ensure effective assessment, the study integrates process-oriented (45%), outcome-oriented (45%), and innovative-driven (10%) evaluation methods, complemented by inter-group peer assessments, multi-party reviews, and user experience feedback. This approach establishes an “evaluation-feedback-improvement” loop, effectively addressing the issue of feedback delays observed in traditional CDIO and NEET models. Empirical evidence from the teaching practice between 2018 and 2020 demonstrates that the adoption of this model significantly improve students’ overall performance (average score increased from 82.73 to 85.67, <i>p</i> &lt; 0.05), and enhanced their innovative thinking and interdisciplinary problem-solving abilities by 23%. Based on these findings, this study proposes further policy recommendations, including the establishment of a dedicated fund for scientific research into teaching, the incorporation of progressive project-based learning practices into engineering certification standards, and the development of a replicable closed-loop framework for engineering education reform.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Deep Learning Into Mechanical Engineering: A Systematic Review of Applications and Educational Implications","authors":"Geethika S. Kollu,&nbsp;Javeed Kittur","doi":"10.1002/cae.70048","DOIUrl":"https://doi.org/10.1002/cae.70048","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning (DL) is reshaping mechanical engineering by offering advanced capabilities for solving complex problems, particularly in fault diagnosis, predictive maintenance, and materials science. While conventional machine learning and physics-based approaches remain prevalent, DL models provide superior performance in terms of accuracy, automation, and adaptability. This systematic review investigates trends in DL applications within mechanical engineering from 2015 to 2024. An initial search using the query “deep learning AND mechanical engineering” across seven major databases—Google Scholar, Web of Science, IEEE Xplore, ERIC, Science Direct, Compendex, and Wiley Online Library—yielded 149 articles. After applying exclusion criteria (published before 2014, non-English, short or work-in-progress papers, not DL and/or mechanical engineering focus, or conceptual papers), 49 studies were selected for in-depth analysis. The results indicate that DL models improve prediction accuracy by 10%–35% over traditional techniques across various applications, including fault detection in rotating machinery and microstructural analysis in materials engineering. Despite notable gains, challenges persist related to data availability, computational intensity, and model interpretability. This review highlights the importance of addressing these limitations and recommends future research efforts toward improving model generalization, incorporating explainable AI techniques, and optimizing DL deployment under limited-data scenarios. Furthermore, the integration of DL with Industry 4.0 technologies—such as IoT, digital twins, and cyber-physical systems—presents a promising direction for real-time, intelligent decision-making in mechanical engineering systems. This review serves as a comprehensive resource for researchers and practitioners seeking to apply or advance DL methods in mechanical engineering contexts.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}