Computer Applications in Engineering Education最新文献

{"title":"Hidden Cost of Mutation Testing on Auto-Grader","authors":"Rifat Sabbir Mansur,&nbsp;Clifford A. Shaffer,&nbsp;Stephen H. Edwards","doi":"10.1002/cae.70091","DOIUrl":"https://doi.org/10.1002/cae.70091","url":null,"abstract":"<p>Mutation testing (MT) is a powerful technique for evaluating the quality of software test suites. MT introduces faults or “mutations” into the code and checks whether the tests then fail as appropriate. While MT is known to be more effective than code coverage as a measure of test quality, its computational cost makes it challenging to deploy in educational settings. In this paper, we show the effects of this computational demand on an auto-grading system when MT was used in a junior-level Data Structures and Algorithms (DSA) course. Through a comparative study spanning semesters with and without MT, we observed a noticeable increase on the auto-grader's processing time and feedback turnaround time (about 30–50 s, which represents roughly a tripling in per-submission processing time) for students whose projects are graded with MT. This additional load raises concerns that it might overload the server, causing delays for students in other courses. However, with suitable mitigation strategies in place, the only measurable impact on other students was a higher variance in feedback turnaround times during peak use. One such mitigation strategy is the use of a local MT plug-in which helped to reduce the total number of submissions to the auto-grader. Overall, we find the effects on server load from a carefully chosen set of mutations combined with moderate use of local MT to have an acceptable computational cost on the system load while improving student test suite quality.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317239","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":"AI-Based Prediction of Program Learning Outcomes for an Engineering Undergraduate Degree","authors":"Fahad Hassan Zaman,&nbsp;Junaid Imtiaz,&nbsp;Maryam Iqbal,&nbsp;Ayesha Waqar Mir","doi":"10.1002/cae.70092","DOIUrl":"https://doi.org/10.1002/cae.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>Human advancement hinges on the capacity to acquire knowledge and engage with complex ideas. Education, therefore, plays a pivotal role in shaping cognitive and societal growth. However, the increasing commercialization of education has raised significant concerns regarding declining academic standards, reduced student performance, and escalating unemployment. To address these systemic challenges, this study proposes a machine learning-based framework for predicting and evaluating Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) in an undergraduate engineering context. The proposed model analyzes historical academic records to investigate the influence of midterm and final assessments on overall grade performance and CLO/PLO attainment. Results indicate that CLO 1 has consistently achieved approximately 90% success over the past 2 academic years, a trend expected to persist based on predictive insights. These findings offer actionable guidance for academic departments to implement targeted interventions, such as scenario-based evaluations, to enhance student learning outcomes. By leveraging Python-based machine learning techniques, institutions can scale their data-driven assessment strategies and reinforce evidence-based educational practices. This study contributes to the growing field of AI-enhanced education, offering practical implications for improving academic quality and institutional decision-making.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316845","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":"Students' Conceptual Explanations of Neural Networks Enabled by Extended Reality Learning: A Multiple Methods Approach","authors":"Miguel A. Feijoo-Garcia,&nbsp;Yiqun Zhang,&nbsp;Yiyin Gu,&nbsp;Alejandra J. Magana,&nbsp;Bedrich Benes,&nbsp;Voicu Popescu","doi":"10.1002/cae.70084","DOIUrl":"https://doi.org/10.1002/cae.70084","url":null,"abstract":"<p>This study examines the use of extended reality (XR) in helping students with conceptual comprehension of artificial intelligence (AI) concepts, specifically neural networks (NNs) and handwritten digit recognition. Using a multi-methods approach, this study assesses student performance and understanding of such concepts. Student participants (<i>N</i> = 29) engaged in an XR environment designed to teach NNs and completed in-lesson assessments consisting of multiple-choice questions and open-ended questions. Quantitative data were analyzed using the <i>k</i>-means clustering method to classify performance levels based on the accuracy of the answers. The elbow approach determined the number of clusters, and the average silhouette score showed the cluster quality after clustering. Qualitative data underwent thematic analysis to identify challenges in handwritten digit recognition. Results showed that the accuracy of the students' responses ranged from 17% to 100% and could be classified into three groups, and that factors like handwriting clarity, digit placement, and writing style significantly impacted the accuracy of handwritten digit recognition. The findings suggest the potential of using XR for supporting learning and engagement in studying AI concepts. Future research is encouraged to apply XR across various education levels and explore broader AI concepts. This study contributes to the literature on applying XR in computer science education by providing insights into how XR can enhance conceptual comprehension of complex AI concepts like NNs.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316687","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":"Harnessing Artificial Intelligence for Advancements in Electrical Engineering: A Systematic Literature Review of Applications, Challenges, and Future Trends","authors":"Michelle Vy Diep Nguyen,&nbsp;Javeed Kittur","doi":"10.1002/cae.70083","DOIUrl":"https://doi.org/10.1002/cae.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) is increasingly recognized as a vital tool in electrical engineering, offering automation, error reduction, and enhanced accessibility. However, its adoption has lagged compared to other fields, highlighting a need for a comprehensive examination of its applications and challenges. This study systematically reviews AI applications in electrical engineering, classifying research findings to uncover progress, challenges, and opportunities. It aims to identify trends, gaps, and implications to guide future research and practical applications. A systematic literature review (SLR) was conducted, analyzing studies published between 2014 and 2024. Fifty-seven publications meeting inclusion criteria were categorized into five themes: AI algorithms, power engineering, smart grid technologies, electric vehicle systems, and AI integration. The review revealed growing interest in AI applications within electrical engineering, with a significant rise in publications, particularly from China. AI algorithms demonstrated broad applicability and versatility across various domains, highlighting their potential for innovation. Additionally, there is a considerable opportunity for developing and applying frameworks to test AI innovations in electrical engineering. AI integration in electrical engineering has advanced significantly in areas such as power engineering, smart grid technologies, and electric vehicle systems. However, substantial untapped potential remains, particularly in developing frameworks for testing AI innovations. This review underscores the importance of global research efforts and identifies promising directions for advancing AI applications in electrical engineering research and practice.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224280","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":"A MATLAB GUI-Based Calculation Platform for Soil Arching Effect to Assist Teaching and Learning in Soil Mechanics","authors":"Cheng-Shuang Yin,&nbsp;Han-Lin Wang,&nbsp;Liu-Mei Wei,&nbsp;Cheng-Ji Gao","doi":"10.1002/cae.70089","DOIUrl":"https://doi.org/10.1002/cae.70089","url":null,"abstract":"<div>\u0000 \u0000 <p>The soil arching effect is a key concept in soil mechanics education. It is widely recognized as an important principle in geotechnical engineering, characterized by stress redistribution due to relative soil displacement, which impacts the safety and stability of geotechnical structures. Despite advances in classical theories and numerical methods, the complexity of models and formulas still presents significant challenges for students and engineers in understanding and application. To address this challenge, this study introduces a practical and educational solution by developing a computer-aided calculation platform for the soil arching effect, designed by Hunan Provincial Engineering Research Center of Advanced Technology and Intelligent Equipment for Underground Space Development in Hunan University, aimed at enhancing soil mechanics education through an intuitive MATLAB graphical user interface. The primary contribution of this study is the development of a platform that integrates seven theoretical models, enabling users to calculate key parameters, such as the soil arching ratio, by inputting soil properties and unloading width. The platform features real-time data visualization and interactivity, allowing users to easily select models, input parameters, and obtain results quickly, thereby facilitating comparative analysis across different theoretical frameworks. Compared to conventional teaching methods, the platform simplifies complex calculations and deepens students’ understanding of the soil arching effect. Results from student surveys indicate a remarkable improvement in comprehension and analytical skills, with high satisfaction regarding the platform's usability and educational value.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223848","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":"Students' Application of Abstract and Systems Thinking Skills for Modeling Software Systems","authors":"Paul J. Thomas,&nbsp;Alejandra J. Magana","doi":"10.1002/cae.70086","DOIUrl":"https://doi.org/10.1002/cae.70086","url":null,"abstract":"<p>Software modeling is an essential practice in software engineering, requiring the application of both abstract thinking and systems thinking. Despite its importance, there is limited empirical research on how these cognitive skills are enacted during the modeling process. This study investigates how undergraduate students apply abstract and systems thinking while constructing software models using Unified Modeling Language (UML). Employing a case study approach with think-aloud protocols, the research is framed through the lens of epistemic forms and games to analyze student reasoning. Six students who had completed a second-year systems analysis and design course participated in the study. Thematic analysis of their modeling sessions revealed how abstract and systems thinking were enacted through structural, functional, and process-oriented epistemic games. Two distinct modeling sequences—structural-before-behavioral and behavioral-before-structural—were identified, each associated with different cognitive strategies. Chronological visualizations were developed to illustrate these modeling paths. Key contributions of this study include a novel integration of epistemic games into modeling analysis, a detailed characterization of student modeling behavior, and actionable recommendations for instructional scaffolds to support the development of modeling proficiency in computing education.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223853","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":"Development of a No-Code Machine Learning Model Builder for Predictive Analytics in Education","authors":"Mohammed Jibril","doi":"10.1002/cae.70088","DOIUrl":"https://doi.org/10.1002/cae.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>Machine learning (ML) has the potential to enhance educational predictive analytics, but its adoption is limited by the programming expertise required to develop models. Traditional ML tools require coding skills, which makes them inaccessible to educators and researchers without computational backgrounds. Existing no-code platforms lack affordability and accessibility. This study addresses this gap by developing and validating a no-code ML builder to enable non-programmers to build, evaluate, and deploy ML models. Design and development research approach was adopted in the study. It utilizes Python-based tools such as Streamlit and scikit-learn. The tool underwent expert validation and comparative performance testing against Google Colab using datasets from Kaggle, consisting of 5000 and 2392 student performance records. The results show that the no-code ML builder, which is accessible at nextml.streamlit.app achieved a predictive performance comparable to coded models. A minor performance gap was observed in some algorithms, with Logistic Regression achieving an accuracy of 63.88% compared to 73.28% in Google Colab. Experts in educational technology and computer science rated the tool highly for usability, with mean scores ranging from 4.33 to 4.57. 71% of evaluators found it suitable for educational datasets, and 56% endorsed its ability to handle students' data sets. The study concludes that the tool bridges the accessibility gap in the application of ML in education while maintaining competitive model performance. It recommends that Institutions adopt no-code tools. Future research should focus on incorporating more complex algorithms.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223909","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":"Engineering Students' Experiences With ChatGPT to Generate Code for Disciplinary Programming","authors":"Camilo Vieira,&nbsp;Jose L. De la Hoz,&nbsp;Alejandra J. Magana,&nbsp;David Restrepo","doi":"10.1002/cae.70090","DOIUrl":"https://doi.org/10.1002/cae.70090","url":null,"abstract":"<div>\u0000 \u0000 <p>Large Language Models (LLMs) are transforming several aspects of our lives, including text and code generation. Their potential as “copilots” in computer programming is significant, yet their effective use is not straightforward. Even experts may have to generate multiple prompts before getting the desired output, and the code generated may contain bugs that are difficult for novice programmers to identify and fix. Although some prompting methods have been shown to be effective, the primary approach still involves a trial-and-error process. This study explores mechanical engineering students' experiences after engaging with ChatGPT to generate code for the Finite Element Analysis (FEA) course, aiming to provide insights into integrating LLMs into engineering education. The course included a scaffolded progression for students to develop an understanding of MATLAB programming and the implementation of FEA algorithms. After that, the students engaged with ChatGPT to automatically generate a similar code and reflected on their experiences of using this tool. We designed this activity guided by the productive failure framework: since LLMs do not necessarily produce correct code from a single prompt, students would need to use these failures to give feedback, potentially increasing their own understanding of MATLAB coding and FEA. The results suggest that while students find ChatGPT useful for efficient code generation, they struggle to: (1) understand a more sophisticated algorithm compared to what they had experienced in class; (2) find and fix bugs in the generated code; (3) learn about disciplinary concepts while they are also trying to fix the code; and (4) identify effective prompting strategies to instruct the ChatGPT how to complete the task. While LLMs show promise in supporting coding tasks for both professionals and students, using them requires strong background knowledge. When integrated into disciplinary courses, LLMs do not replace the need for effective pedagogical strategies. Our approach involved implementing a use-modify-create sequence, culminating in a productive failure activity where students engaged in conversations with the LLM encountered desirable difficulties. Our findings suggest that students faced challenges in trying to get a correct working code for FEA, and felt like they were teaching the model, which in some cases, led to some frustration. Thus, future research should explore additional forms of support and guidance to address these issues.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223946","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":"Transforming IoT Skill Development in Engineering Education: The Influence of Augmented Reality-Based Learning Environment","authors":"Lav Soni,&nbsp;Ashu Taneja","doi":"10.1002/cae.70087","DOIUrl":"https://doi.org/10.1002/cae.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional methods for teaching Internet-of-things (IoT) in engineering education often lack interactivity and hands-on engagement, limiting skill development. This study explores Augmented Reality (AR) based learning environment as a solution, enabling students to visualize real-time data flows, interact with virtual components, and configure IoT systems in a risk-free setting. Using tools like Unity 3D, Blender, Vuforia SDK, and Arduino IDE, an AR-based framework is developed and tested against traditional methods. The results show increased student engagement, knowledge retention, and skill acquisition, with a System Usability Score of 82.00%. The paper presents the framework's design, usability assessment, and comparative evaluation, highlighting AR's potential to enhance IoT education. It is observed that the proposed AR-based framework improves the skill retention by 36% over the traditional method. Further, the performance comparison of proposed method with traditional method is evaluated in terms of students' engagement, learning speed, and user satisfaction. In the end, the limitations of proposed study are addressed, and the future directions are presented.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223947","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 Artificial Intelligence in Higher Education to Enhance Teaching and Learning","authors":"Gollapalli Tejeswara Rao,&nbsp;Nagula Suhasini","doi":"10.1002/cae.70085","DOIUrl":"https://doi.org/10.1002/cae.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of artificial intelligence (AI) in higher education represents a transformative shift in the way teaching and learning are approached, offering unprecedented opportunities to enhance educational outcomes. One significant issue is the potential for bias in AI algorithms, which can perpetuate existing inequalities if not carefully managed. The objective of this study is to explore and evaluate the integration of AI in higher education to enhance teaching and learning processes. The study aims to identify the most effective AI tools and strategies for improving educational outcomes, assess their impact on student engagement and achievement, and provide actionable recommendations for educators and institutions. To effectively assess the integration of AI in higher education, a multifaceted data collection approach is essential. To ensure the successful integration of AI tools in higher education, a structured implementation plan is crucial. Enhancing teaching and learning involves a comprehensive approach that includes meticulous data collection, rigorous data analysis, strategic implementation and continuous improvement. The implementation phase requires thoughtful planning and execution, with a focus on refining AI systems based on feedback and performance metrics to ensure they effectively support educational goals. The findings show that AI integration in education has improved average grades to 88%, increased retention rates to 85%, and achieved 92% in content customisation and implementation using Python software. The future scope for integrating AI in higher education includes developing advanced AI tools that offer personalized and adaptive learning experiences, enhancing predictive analytics for student performance and retention, and fostering innovative pedagogical approaches through AI-driven insights.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"33 6","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181652","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}