seasonality.The paper aims to identify key risk periods and the impact of seasonal changes on roofers. Theobjective of this study is threefold: (1) to examine whether roofing accidents occur at differentrates during different seasons; (2) to identify temporal characteristics of roof-related constructionaccidents for the past few years; and (3) to check if these fluctuations are statistically significant.Statistical methods including chi square tests were applied to determine the significance of thesefactors.The results indicated that accidents occurred more frequently in specific time periods,particularly during the morning hours, with a significant peak in the summer months. Thefindings underscore the importance of tailored safety interventions during
development, andpreparation of MSFC facilities for testing or simulation. Designs include...test stands, test beds,load reaction and application structures, load line components,…, flight hardware mockups andsimulators, hardware support stands and dollies, personnel access stands, lifting and handlinghardware, and tooling used to facilitate the fabrication and/or assembly of flight/non-flighthardware…” [1]. The branch is split up into three teams: Structural Design, Piping Design, andStress Analysis that each plays a significant role in accomplishing these design collaborativetasks [2].The structural design team includes a group of mechanical and aerospace engineers who usecomputer-aided design to design 3-dimensional models. These models are created
facilitates further implementation of theentrepreneurial mindset in broader civil engineering and construction courses.Keywords: entrepreneurial mindset, civil engineering, case study1. IntroductionIn recent years, efforts have been directed at the inclusion of the entrepreneurial mindset inengineering curricula. This increasing interest reflects a growing belief in the need for engineersto have, besides technical skills, an entrepreneurial mindset that leads to enhanced innovativecompetence, flexibility towards market dynamics, and value creation in their professional work[1]. An entrepreneurial mindset incorporates attitudes, beliefs, and thought processes thatinfluence motivation to involve the future engineering workforce in opportunities to
last couple centuries, storiesseemed to have been removed from learning and instead, content and knowledge has been centeredin the classroom. This can equate to the dull presentation of facts, figures, and formulas, strippedof any relationship to the world, let alone to the imagination.In a period of time where the world seeks engineering innovation, common teaching practicesseem to stifle creativity in the classroom. Professional organizations like ASCE emphasize thatcivil engineering programs need to prepare their graduates to face unique problems in theworkforce that will require innovative solutions [1-3]. However, these same students likely spenta number of years not exercising this creativity.The following paper documents a work in
Enhancement, Sustainability TrainingIntroductionThe building sector, responsible for approximately 38% of global greenhouse gas emissions, is atthe forefront of decarbonization efforts aimed at mitigating climate change [1]. As urbanizationaccelerates and energy demands rise, the need for innovative strategies to reduce emissionsbecomes increasingly urgent. These challenges extend beyond operational energy efficiency toencompass the full lifecycle of buildings, including materials, construction processes, and end-of-life considerations [2]. Simultaneously, the digital transformation of the design, planning, andconstruction industries is reshaping the approaches used to address these challenges [3].Emerging digital tools, such as Building Information
practices and support efforts to create more inclusive and equitable environments forfinancially disadvantaged students. The findings will guide future research and initiatives aimedat reducing institutional barriers and fostering the success and retention of low-SES students inengineering.IntroductionEngineering is often hailed as a pathway to innovation and social mobility, yet its accessibilityremains unevenly distributed, shaped by enduring systemic inequities. Wide-scale barriers rootedin social, cultural, and economic disparities have long shaped the field, disproportionatelymarginalizing women, racial and ethnic minorities, and many other underrepresented groups. [1].Despite efforts to diversify the engineering workforce, these populations
participants achieving their communication goals and80% noting increased task efficiency. The application’s intuitive design also received a 90%positive rating for usability and interface clarity. Comparative analysis with traditional PECSbooks shows that PictoConecta provides a slight performance advantage, particularly for usersrequiring less support.Keywords: Autism Communication, planning activities, Mobile Application, Pictograms,Technology Acceptance Model, Artificial Intelligence (AI)IntroductionAutism Spectrum Disorder is a developmental disability characterized by deficits in socialinteraction or communication and the presence of restricted interests or repetitive behaviors [1].According to the World Health Organization (WHO), it is estimated
. Using descriptive statistics, students' beliefs and actualcourse performance were explored. Findings showed that having small interventions promoting agrowth mindset in the classroom can enhance and improve students' self-confidence and helpthem tackle and face challenges in a positive light. The study concludes with somerecommendations for similar courses in engineering.Introduction This WIP paper explores how having a growth mindset in science, technology,engineering, and mathematics (STEM) is essential as it is shown to impact student performanceand help them tackle challenging tasks [1-3]. In higher education settings, students often entertheir first-year engineering programs with different levels of spatial skills. Spatial
multiple instructors. Along with instructor surveys, data is presented based onfinal exam performance that highlights the value of a graphical approach.Background - Graphical Approaches to ThermodynamicsIntroductory engineering thermodynamics courses typically begin by providing two approachesto finding thermodynamics properties: the ideal gas law and property tables. Although notionalillustrations of thermodynamic processes and cycles on pressure-volume and temperature -entropy diagrams are common, obtaining thermodynamic properties graphically is notemphasized. For example,well established texts [1] and [2] use simplified phase diagrams tographically illustrate and explain cycle analysis, however they only introduce graphical methodsto obtain
, design for manufacturing, and engineering education. ©American Society for Engineering Education, 2025 Translating Evidence on Asset-based Pedagogies into Engineering Education PracticeIntroductionIn this evidence-based practice full paper, we describe an inventory of asset-based strategies co-produced by study participants and researchers in an ongoing, multi-year research project at alarge, public, land-grant, Hispanic-Serving Institution. Asset-based approaches emphasizestudents' inherent strengths, lived experiences, and cultural identities as foundations forcultivating inclusive learning environments as well as promoting skill development amongstudents [1], [2]. Despite promising
[1]. Sustainable energy sources have steadily increased in usage, butdevelopment and adoption of sustainable technologies is still far behind necessary levels to meetemission reduction goals by 2050 [2],[3]. In addition to developing and using sustainabletechnologies, reducing energy consumption has been discussed as an opportunity to decreasereliance on non-renewable sources and emissions. Engineers are typically at the forefront of thesetechnological developments, indicating that the next generation of sustainable energy technologywill need sustainability and energy literate engineers. While sustainability literacy has expandedwithin engineering domains [4]-[7], energy literacy is understudied in engineering. However, fortechnological
numbers of studentswith anxiety, depression, and other limitations to mental health and wellness (MHW) [1], [2].Despite the growing frequency and awareness of MHW issues for students, few instructors aretrained to address these problems in the classroom [3], [4], [5]. Resources from universitycounseling centers [6] typically focus on acute crisis management and do not address morechronic issues. For example, requesting “wellness checks” from first responders (frequently lawenforcement officers) may not be appropriate for a disengaged student who fails to attend classor submit assignments. Such students are still clearly struggling with personal problems. Facultycannot and should not take on additional roles as counselors or therapists. However
teacher went from being a novice in engineering tounderstanding that problem solutions require multiple iterations. Furthermore, we discovered thatthis teacher was already infusing some translanguaging practices in her class environment.Implications of this work include a better understanding of how elementary teachers navigate thechallenge of teaching engineering to students and how these teachers specifically plan for,scaffold, and include the engagement of their multilingual students within these lessons.IntroductionMultilingual students comprise 10.1% of US students, and this number is projected to increase[1]. Often emergent English speakers are not afforded the same opportunities as monolingualEnglish speakers due to deficit-oriented
(IPEC) Competencies [1]: (1) Respect, (2)Commitment, (3) Transparency, (4) Communication, and (5) Justice. Students answer a series ofquestions surrounding their mutual expectations for each other in these categories and thus setthemselves up for a clearer understanding of the people they are working with and, most importantly, thetools for individual governance. Furthermore, teams were asked to conduct two 360-degree feedbackevaluations of each other, which are performance-based assessments. One evaluation was performedmidway through the project and another at the end of the term to ensure they developed the desiredteamwork skills to successfully and equitably finish their projects.While this intervention had been modestly successful for the
Computer Science faculty member familiarwith the use of various AI tools, and a student researcher familiar with both technical writingconventions and statistical analysis.BackgroundThere is a growing body of literature on using AI as a tool supporting assessment. Working atAalborg University, Lindsay and Jahromi [1] explored using Natural Language Process (NLP) toassign pass/fail grades to a 2000-word reflective essay. The researchers were motivated to use AIbecause of the labor-intensive nature of grading the essays, which they calculated as “well over500 hours of pass/fail summative assessment work within a very short timeframe” for their 1500students who completed the task. Of the 1500 submissions, Lindsay and Jahromi used 80% ofthe data as a
” to help us conceptualize the variations amongstthe students in our department, which were derived from interview data with two cohorts of graduatingseniors. These personas have three levels: 1) Origins, to understand variations in students’ backgrounds,2) Identities, to explore variations in student interests and motivations, and 3) Trajectories, to explorevariations in what students hope to do with their engineering degrees. We intend to use these personaswithin the department to help faculty support non-traditional or “alternative” identities and pathways inengineering. We also intend to use them to help students better articulate what kind of engineers theywant to be and to recognize themselves as full members of the engineering
, as it helped formulate study guides bysynthesizing student-inputted equation sheets or created practice problems that mimicked examquestions.IntroductionArtificial intelligence (AI) emerged in computer science in the 1950s and has since undergonesignificant development. There are many distinct flavors of AI, such as fuzzy logic, geneticalgorithms, knowledge-based systems (rule-based), inductive learning (automatic knowledgeacquisition rule-based), and neural networks, to name a few [1]. Many of these AI programs arerule-based (i.e., follow “if-then” logic based on a knowledge base) with the provision to updatethe knowledge base in an automatic and/or informed manner. Others, such as artificial neuralnetworks, learn patterns from training
systems that enhanceintelligent transportation networks' safety, reliability, and efficiency. Future directions includeleveraging edge computing and advanced AI architectures to improve decision-making processesand achieve Level 5 autonomy.IntroductionAutonomous vehicles rely ponderously on computer vision systems to interpret theirenvironments and make real-time decisions. As these systems become more integrated intotransportation, ensuring their accuracy and reliability is crucial [1]. A significant challenge inautonomous driving is detecting and segmenting objects such as pedestrians, vehicles, and trafficsigns in complex environments [2]. Errors in object detection can undermine the safety andreliability of autonomous systems, potentially
leadership development and institutionaleffectiveness. The findings contribute to academic discourse and provide practicalrecommendations for fostering leadership excellence in higher education, ensuring sustainablegovernance and positive societal impact.Keywords: Higher Education Institutions, leadership styles, managers’ development,governance, organizational models, institutional effectiveness.INTRODUCTIONThe organizational structures of Higher Education Institutions (HEIs) can be understood throughthree primary models: mechanistic, organic, and anthropological (Figure 1) [1], [2].Misalignment in the application of these models often results in governance deficiencies. Forinstance, the mechanistic model, which operates as a technical system
increasingly essential. As industries and workplaces continue to adopt advancedtechnologies, particularly artificial intelligence (AI), the demand for professionals equipped withthese skills has intensified [1]. Generative AI (GenAI) tools, which are transforming varioussectors, offer the potential to revolutionize educational methodologies by fostering these criticalskills among students. These tools, such as ChatGPT, can provide adaptive learning experiences,real-time feedback, and interactive problem-solving opportunities [2], [3]. While the integration of AI into educational environments promises to create morepersonalized, engaging, and effective learning experiences, its potential impact on durable skilldevelopment remains underexplored
embedCS education more deeply into the core course of study for K–12 students. CS skills are nowessential in preparing students for future opportunities and navigating a world increasinglyreliant on rapidly evolving technologies [1], [2], [3]. Students equipped with these skills possesssignificant advantages in both the job market and broader societal contexts [2], [4]. As the needto equip students with computer science skills intensifies, many states find themselves at acritical juncture. Initiatives aimed at voluntarily incorporating computing education into schoolcurricula have plateaued and often fall short of addressing persistent disparities in participationbased on gender, race/ethnicity, and income. To broaden access and promote equity
avoid using advanced mathematical concepts in theircourses.1. Introduction In engineering students’ early college experience, math courses often pose a point ofstruggle. While some students see them as a “gateway to engineering”, others view them as“gatekeepers” [1]. Math courses, particularly, calculus-focused courses, are often perceived withboth scaffolding and litmus properties, which on one hand, prepare students for their higher-levelSTEM education and, on the other, filter them out [1], [2]. However, the effects of filtering arefelt more prominently than those of scaffolding [2]. Hence, these math courses often lead to highdropout rates in the initial years and continued challenges in the later years through application-related
essential problem-solving skills, includinganalytical thinking, logical reasoning, and mathematical modeling. This course also equipsstudents with the analysis skills required to test potential solutions during the design of an electricalsystem.Circuit analysis is challenging for students because it involves abstract concepts and complexmathematics. The interconnected nature of circuits means that each component influences theentire system, making it difficult to understand. This complexity can pose a high intrinsic cognitiveload on students, potentially overwhelming their working memories and impeding the necessaryformation of schemas in long-term memory [1, 2]. For this reason, problem-solving practice is thekey to developing and mastering
and the curriculum. IntroductionTo increase the participation of women and people of color in computer science (CS), UCLAdeveloped the ECS curriculum 1 for use in the Los Angeles Unified School District. ECS servesboth as a high school introductory CS curriculum and a professional development (PD) programfor educators. ECS is built on three core principles: CS content comprehension, inquiry-basedlearning, and educational equity 2 . This approach ensures that CS education is accessible andengaging for underrepresented students, fostering a supportive and equitable learningenvironment 1 . With the help of multiple National Science Foundation grants in the 2010s, ECSspread to many other regions of the
Design in Biomedical Engineering”) has supported a number of similar clinicalimmersion experiences across the nation. Several of these, as reported over the past decade, arediscussed here with a focus on implementation methods and efficacy in an effort to motivate theprogram structure discussed in Section 3.Programs offer a wide range of co-curricular engagement opportunities and methods ofconnecting the clinical observation experiences to biomedical engineering practice. Sing, A., etal., developed a program focused on needs finding and problem identification during clinicalvisits as part of a senior level biomechanics course [1]. Kadlowec, J., et al., developed a summerclinical immersion program to teach needs finding and provide a pipeline for
development of educational videos is not astraightforward process nor is there one correct approach. Rather, it is a journey of evaluating theeducational goals and embracing the ability of video to transcend time and space to bringengineering to life.1. IntroductionEducation has evolved significantly from traditional textbooks and chalkboard lessons to moretechnologically involved engagements. Although the majority of these experiences arecommunal such as using PowerPoint slides or live demonstrations in a classroom, there isincreasing presence of individual educational experiences such as virtual reality and videoplatforms.Studies shown college students and educators are heavily using video platforms such as YouTubeas an educational resource with
. Zhu is a member of the Board of Directors for the Association for Practical and Professional Ethics (APPE). His research explores how culture influences the cultivation of globally competent and socially responsible engineers, as well as the ethical development and deployment of AI and robotics. ©American Society for Engineering Education, 2025 Shattering the Bamboo Ceiling: Asian American Student Perceptions of Engineering LeadershipIntroductionWhile engineering is often perceived as a highly technical field, “non-technical” professionalskills, such as leadership, have become central to preparing undergraduate engineering studentsfor careers in industry [1]. As more engineers
and develop curriculum around AI literacy. With these in place, practitioners caneffectively develop and implement educational systems that leverage AI’s potential in areas suchas immediate feedback and personalized learning support. This approach can enhance the qualityof students’ educational experiences while preserving the integrity of the learning process with AI.1 Introduction and BackgroundArtificial intelligence (AI) chatbots have emerged as a growing resource in educational settings.Advances in large language models (LLMs) have enhanced AI chatbots’ ability to understand andrespond to academic queries, driving their increased adoption in educational settings and sparkinggreater research interest. Open online models such as OpenAI’s
part of active learning principles. These principles suggest that whenstudents are actively engaged with their learning, they are more likely to understand the conceptsintroduced to them in class [1]. In general, the more involved the student is in the learning process,the greater their knowledge acquisition and cognitive development are [2], and the more theyengage in critical thinking processes such as analysis, synthesis, and evaluation [3]. Additionally,Biggs [4] states that the more motivated students are, the more they adopt a deep learning approach.He claims that one way to resolve the gap in students’ understanding is to involve them in activitiesthat are engaging and require high levels of cognitive reasoning from them [1], [4
development.KeywordsRobotic swarm, biologically inspired resilient systems, experimental platform, multi-disciplinaryengineering, systems engineering STEM outreach1 IntroductionThe characteristics of biologically inspired swarm algorithms are derived from naturalphenomena such as complex collective interactions between ants, bees, birds, fish etc. [1, 2, 3]These algorithms have provided innovative methodologies to solve problems pertaining tocomplex systems in the real world that require a high level of computation. Yuce B. et al. [4]utilized the concept of foraging behavior honeybees to propose a new Adaptive NeighborhoodSite change and Site Abandonment (ANSSA)-based optimization algorithm and tested it withmultiple benchmark functions. E. Sahin [5] presents a