Computer Applications in Engineering Education最新文献

{"title":"Big Data Research on Personalized Learning in Computer Education: A Thematic Evolution Analysis","authors":"Youyin Mo,&nbsp;Jie Zhang,&nbsp;Yan Mou","doi":"10.1002/cae.70159","DOIUrl":"10.1002/cae.70159","url":null,"abstract":"<p>This paper presents a systematic review of the literature on personalized learning in computer education from 2014 to 2024, using a thematic evolution analysis to uncover the origins and emerging hotspots in this field. The review shows that the research hotspots in personalized learning present an apparent trend from early interventions focusing on academic warning systems and performance prediction, to technology-enabled intelligent tutoring and resource recommendation, and then to the development of a range of competencies for computational thinking. Furthermore, the emerging application of novel technologies, such as generative AI, is advancing personalized learning toward an even more intelligent and human-AI collaborative stage. Finally, the paper summarizes the existing challenges in methodological transparency, integration of educational praxis, and ethical balance, and highlights potential avenues for future research and practice.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217073","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":"A Problem-to-Code Teaching Framework for Technology-Enhanced Database Programming in Engineering Education: A Mixed-Methods Study","authors":"Hung-Yi Chen,&nbsp;Ying-Chieh Liu,&nbsp;Tiffany Chiu","doi":"10.1002/cae.70163","DOIUrl":"10.1002/cae.70163","url":null,"abstract":"<div>\u0000 \u0000 <p>In engineering education contexts, students in database programming courses within the College of Informatics and Engineering often encounter challenges when transforming problem statements into executable code in technology-enhanced programming environments, which often diminishes their self-efficacy. To address this issue, this study introduces the Problem-to-Code Teaching Framework (PCTF), an instructional model that integrates the Function/Pattern-Oriented Teaching Method with supporting scaffolding activities. The PCTF was implemented in an 18-week PL/SQL course with 45 undergraduates at a university of science and technology in Taiwan, conducted in a technology-enhanced environment using Oracle 19c databases and SQL IDE tools. Using a convergent mixed-methods design, programming self-efficacy was measured at three time points and analyzed with linear mixed-effects regression, while 16 semi-structured interviews captured students' perceptions of conceptual, procedural, and feedback scaffolds. Results indicated a steady increase in programming self-efficacy across the semester, with the compensatory effect among students starting at lower levels. Deep learning approaches showed a strong, positive association, whereas the surface approach was not reliably associated. Qualitative findings indicated that multilayered scaffolds were perceived as supporting confidence and persistence by clarifying problem abstraction and solution modeling, structuring the problem-to-code conversion process, and providing timely feedback. Overall, the PCTF represents a context-bounded yet structurally transferable, technology-enhanced instructional framework that bridges problem analysis and code implementation, contributing to technology-integrated data-centric engineering programming education.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217072","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":"Adaptive Deep Reinforcement Learning for Optimizing Teacher Professional Development Path","authors":"Mingjun Shi,&nbsp;Jue Wang,&nbsp;Haojie Yu,&nbsp;Neelam Mughees","doi":"10.1002/cae.70164","DOIUrl":"10.1002/cae.70164","url":null,"abstract":"<div>\u0000 \u0000 <p>Ensuring equitable access to cybersecurity expertise has become increasingly critical, considering the growing complexity of digital threats. As educators are tasked with delivering instruction in areas such as computer fraud prevention and network security, there is a pressing need for adaptive, data-informed professional development systems that can support individualized learning paths. To address this challenge, this study proposes an adaptive deep reinforcement learning framework for optimizing personalized teacher development path planning in cybersecurity education. Two enhanced models are introduced: an improved Deep Q-Network (DQN) that integrates a multi-layer perceptron, a cubic dynamic reward function, and an adaptive exploration strategy; and a PER-D3QN model that combines dueling double deep Q-learning (D3QN) and prioritized experience replay (PER) to mitigate <i>Q</i>-value overestimation and accelerate convergence. Experimental evaluation using real-world teacher data demonstrates that the improved DQN achieved average performance scores up to 0.36, compared to 0.054–0.068 for the traditional DQN. Moreover, the PER-D3QN model outperformed the ERDQN baseline, attaining an average reward of 4.738 versus 2.021, and an average score of 2.799 after 6000 training rounds, compared to 1.946 for ERDQN, indicating that network update speed has also been significantly improved. This research not only helps to enhance teachers' professional knowledge and technical application ability in the field of network security, but also provides scientific methodological support for educational institutions to ensure that they are aligned with changing security threats. Furthermore, this study emphasizes the importance of interdisciplinary cooperation and encourages experts from computer science, education, and psychology to work.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217071","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":"Artificial Intelligence Literacy: Scientific Impact of LearningML Software","authors":"Pablo Dúo-Terrón,&nbsp;Juan David Rodríguez-García,&nbsp;Gregorio Robles-Martínez,&nbsp;Antonio José Moreno-Guerrero","doi":"10.1002/cae.70162","DOIUrl":"10.1002/cae.70162","url":null,"abstract":"<p>Creating with artificial intelligence (AI) is fundamental to AI literacy through effective teaching methods and programmes. The aim is to suggest strategies for AI literacy education using LearningML software, based on machine learning, which allows users to create artificial intelligence models to recognise text and images without the need for programming knowledge. The study method is based on a systematic review of different databases that have integrated LearningML since 2020, their authors, countries, affiliations, keywords, associated resources, objectives, study methods, and conclusions, in order to determine the impact of the LearningML tool for integrating and developing AI literacy in teachers, students, and any user. The results were 48 documents that position LearningML software as a resource that can be integrated into curricula to promote AI literacy from primary education (K-8) to university, and even for any citizen. The main conclusions position this software in STEM fields, such as medicine, which recommend this software for understanding the fundamentals of AI. This tool helps address the challenge of preparing citizens for the future and making decisions about the use of AI.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216770","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":"MATLAB Smart Assignment Grader (MSAG) for Consistent, Adaptive, and Fair Grading of Coding Assignments","authors":"Peter L. Bishay","doi":"10.1002/cae.70158","DOIUrl":"10.1002/cae.70158","url":null,"abstract":"<div>\u0000 \u0000 <p>Although grading is one of the most time-consuming things teachers do, it gives students feedback on how hard they worked to complete an assignment and provides teachers with an evaluation of how well students understood the course material. It is relatively difficult to grade coding assignments since it involves reviewing student-written computer code, giving each student individualized feedback, and allocating partial credit in a fair and consistent manner. Because of its relative simplicity and the abundance of tools and command libraries available, MATLAB is being used in many colleges across the world. In 2018, MathWorks introduced the “MATLAB Grader” system, which allowed instructors to design their own assignment questions requiring students to write a script or a function. This system has many attractive features, such as the ability to allow multiple attempts, with grading done instantly online after each submission to help students improve their code for the next attempt. This paper introduces the MATLAB Smart Assignment Grader (MSAG) system, which includes features absent in MATLAB Grader, such as adaptive grading, plagiarism suspicion flagging, and a partial credit option. To foster the advantages of both grading systems for consistent, adaptive, and fair grading of coding assignments, this paper also proposes an approach to integrating MATLAB Grader with MSAG.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216851","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":"Fusing LabVIEW and Machine Learning: A Project-Based Approach for Teaching Industrial Condition Monitoring","authors":"Xin Xu,&nbsp;Hongli Li,&nbsp;Chengliang Pan,&nbsp;Ruhao Gao,&nbsp;Xiaotian Lin,&nbsp;Tengda Zhang,&nbsp;Biao Wang,&nbsp;Shuangbao Shu,&nbsp;Juan Cheng,&nbsp;Haojie Xia","doi":"10.1002/cae.70160","DOIUrl":"10.1002/cae.70160","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid evolution of Industry 4.0 necessitates that engineering education equips students with skills that bridge traditional instrumentation and modern data-driven analytics. This paper addresses this need by presenting a comprehensive project-based learning module that fuses LabVIEW-based virtual instrumentation with machine learning (ML) for teaching industrial condition monitoring. Implemented in a senior-level undergraduate course, the module tasks student teams with diagnosing the health of an industrial fan. Using the NI ELVIS educational platform instrumented with an accelerometer, students acquire real-time vibration data. A key innovation is the seamless integration of LabVIEW, used for data acquisition and visualization, with a Python-based Convolutional Neural Network (CNN) model, which classifies the fan's condition (normal, minor, or severe malfunction) and rotational speed. The technical implementation achieved high classification accuracy (exceeding 95% on test data) and low inference latency (approximately 0.1 s), demonstrating the feasibility of real-time ML deployment. Pedagogically, the project provided an authentic, interdisciplinary learning experience, enhancing student understanding of vibration analysis, sensor integration, and the practical application of deep learning. The module successfully demonstrates a scalable framework for incorporating AI into engineering laboratories, effectively preparing students for roles that require synthesizing physical system knowledge with intelligent algorithm deployment.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135970","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":"Optimizing Engineering Education in the Maldives: A Data-Driven Analysis of Key Barriers and Statistical Visualization Using R Programming","authors":"Salavutheen Noortheen,&nbsp;Ramchand Vedaiyan,&nbsp;Sivakumar Thankaraj Ambujam,&nbsp;Nafeena Abdul Munaf,&nbsp;Asadi Srinivasulu,&nbsp;Gokul Thanigaivasan","doi":"10.1002/cae.70154","DOIUrl":"10.1002/cae.70154","url":null,"abstract":"<div>\u0000 \u0000 <p>Engineering education is a cornerstone of national development in the Maldives, driving technological progress and supporting essential infrastructure growth. However, the steady decline in student enrolment in engineering programs has become a growing concern. This research examines the key factors influencing students' interest in engineering by analyzing responses from students, faculty, and the public. Using data analysis techniques such as descriptive statistics, correlation, regression, and predictive modelling, a total of 48 factors were evaluated, of which 15 emerged as the most influential. These include financial support, career awareness, and the relevance of the curriculum to industry needs. The predictive model demonstrated strong reliability, achieving 75% accuracy, an F1-score of 80%, and an <i>R</i>² value of 0.89. The analysis showed that financial barriers (mean = 4.76 ± 0.98), limited awareness (95.6%), and insufficient industry exposure (87.6%) were the most significant challenges. Other issues, such as inadequate employer sponsorship, job market uncertainty, and gender imbalance, also contribute to low enrolment. Based on these insights, the research recommends introducing scholarship programs, updating curricula to reflect current industry standards, and strengthening collaboration between academia and employers. The findings offer practical guidance for policymakers to make engineering education in the Maldives more accessible, relevant, and capable of preparing a skilled workforce for future national needs.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139134","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":"Correction to “A Project-Based Learning Approach: Designing MATLAB-Aligned Mixed-Signal Circuit Components With Open Source Tools”","authors":"","doi":"10.1002/cae.70152","DOIUrl":"10.1002/cae.70152","url":null,"abstract":"<p>Yu, X., Xie, L., Guo, Z., Wang, A., &amp; Lu, Z., “A Project-Based Learning Approach: Designing MATLAB-Aligned Mixed-Signal Circuit Components With Open Source Tools,” <i>Computer Applications in Engineering</i> 34, (2026): e70141.</p><p>The authors' swapped the affiliations 2 and 3 from:</p><p>²School of Electronic and Information Engineering, Soochow University, Jiangsu, China | ³Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang Province, China” to</p><p>²Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang Province, China | ³School of Electronic and Information Engineering, Soochow University, Jiangsu, China”.</p><p>In addition, on page 1, line 36, abbreviations: “ANA, anti-nuclear antibodies; APC, antigen-presenting cells; IRF, interferon regulatory factor.” was incorrect.</p><p>This should have read: “EDA, electronic design automation; IC, integrated circuit; PBL, project-based learning.”</p><p>We apologize for this error.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129952","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":"Designing an Interdisciplinary Artificial Intelligence Curriculum for Engineering: Evaluation and Insights From Experts","authors":"Johannes Schleiss,&nbsp;Anke Manukjan,&nbsp;Michelle Ines Bieber,&nbsp;Sebastian Lang,&nbsp;Sebastian Stober","doi":"10.1002/cae.70151","DOIUrl":"10.1002/cae.70151","url":null,"abstract":"<p>As artificial intelligence (AI) increasingly impacts professional practice, higher education requires new frameworks for integrating AI competencies into degree programs. At the same time, systematic approaches to designing domain-specific AI programs are underexplored in research. This study evaluates the development of a novel undergraduate AI engineering program (210 credits, seven semesters) using formative evaluation through curriculum mapping and focus group interviews with 19 experts (educators and industry representatives), examining perceived quality, consistency, practicality, and effectiveness. Three key findings emerge: First, the conceptual program that the developed interdisciplinary AI curriculum is expected to be effective, practical, and positively validated by educators and industry. Second, educators who participated in the design process show greater ownership and systemic understanding than nonparticipants, revealing how participatory approaches could shape quality perceptions in interdisciplinary contexts. Third, while stakeholders view the interdisciplinary structure as a strength for employability, they identify practical challenges that need to be considered when implementing the program. Overall, the study contributes both a validated transferable reference model for AI engineering programs and the first understanding on the impact of participatory design in interdisciplinary contexts, advancing scholarship on AI education, and providing practical guidance for institutions developing domain-specific AI programs.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139133","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":"Immersive Gamified Training Simulations for Visualization of Structural Maintenance With Virtual Reality","authors":"Elliott Carter,&nbsp;Joel Friesen Walder,&nbsp;Paul Mensink,&nbsp;Ayan Sadhu","doi":"10.1002/cae.70156","DOIUrl":"10.1002/cae.70156","url":null,"abstract":"<p>Identification of damage and key structural elements is vital to the monitoring and management of civil engineering projects, education, and training. However, practical inspection training is often constrained by cost, safety risk, and limited access to real structures, which reduces opportunities for repeated practice and feedback-rich learning. To address these constraints, recent research has explored virtual reality (VR) in civil engineering to deliver immersive training for infrastructural inspections and reduce reliance on in-person field trips and site visits. Despite the many advantages of VR as a learning tool, its adoption in civil engineering education remains limited. As a result, many engineers-in-training receive limited opportunities to practice realistic inspection workflows that combine defect recognition with structural health monitoring (SHM) interpretation. This paper presents a novel VR-based educational tool designed to teach visual damage identification and structural condition assessment through immersive, scaffolded simulations. In this research, users explore a photorealistic 3D bridge reconstructed through drone-based photogrammetry, annotate multiple damage types, and interact with embedded virtual sensors displaying multi-year structural data collected from real-world instrumentation. Unlike traditional approaches, the system integrates gamified scoring, real-time feedback, and both qualitative and quantitative analysis tasks into a single, performance-tracked learning experience. A classroom study with graduate students evaluated the tool's impact on learner motivation and confidence using a structured motivation model and a validated engineering self-efficacy scale, demonstrating measurable improvements in damage assessment skills. This study advances the educational use of VR in civil engineering by combining interactive infrastructure scans, authentic sensor data, and experiential learning to offer a compelling, cost-effective alternative to traditional field-based inspection training.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135986","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}