preparing them for future collaborative work environments. This studyadvocates for further exploration of tailored prompts and objectives to maximize the potential ofmind mapping as an educational tool across various contexts.IntroductionMind mapping, as a tool for organizing and visualizing ideas, has long been recognized for itsability to capture mental association schemes and explore latent dimensions and connections.This process not only fosters clarity in understanding the relationships between concepts but alsohighlights the unique aspects of the order and quantity of ideas produced [1]. By preserving thenetwork structures, mind mapping enables learners to engage in the mapping activity andorganizing ideas through visual, semantic, and
videos. We also o6er a tour to a class 100/1000 cleanroom facility at theMicro/Nano Technology Center. Student survey results show that the camp has increasedstudents’ interest in studying and pursuing career in semiconductor or related field.1. IntroductionThe semiconductor industry is a key driver of innovation and economic growthworldwide. Given their central role in modern technology, semiconductors have far-reaching implications for industries and fields that touch nearly every aspect of our lives.However, there is a significant semiconductor talent shortage in United States [1]. Thus, itis important to provide semiconductor education broadly to prepare future workforce. The paper introduces a chip camp we created at the University of
improving the retention of under-performingstudents, but these tools are too labor-intensive for faculty to apply in large introductory courses.Additionally, many struggling students are limited by non-cognitive factors such as fear offailure, social anxiety, and general overwhelm. There is a need for large-format, scalableinstructional tools that both engage students in course material and address non-cognitive factorsin an appropriate way.This Work In Progress will present the effects of a remedial intervention, the “reflectiveknowledge inventory”, at improving student outcomes in Calculus 1. In the intervention, studentsimprove their exam score by submitting a “reflective knowledge inventory”. Expert learnersknow that new skills are best built
percent require FYS enrollment [1], [2], [3]. Studies found that FYS is bestembedded within the general education requirements (university core), and FYS courses ingeneral show increased benefit to all students above those students who do not take a FYS course[2], [3]. Moreover, at-risk students such as first-generation and male students, students of color,and conditionally admitted students saw greater benefits with higher level FYS credit loads. And,when analyzing persistence and progress toward degree all students benefitted the most withthree credit-hour FYS courses [3].It is important to know that students most influenced by current FYS courses fall into thegenerational category, Gen Z, and are typically described as between ninth grade and
engineering studentsvia small focus groups, large group in-class activities, and faculty interviews. The earlyfindings presented in this Work in Progress paper serve as a contribution towards thewider conversation about engineering judgment. Specifically, we aim to expand theconversations such that engineering judgment becomes a more mainstream and explicittopic within engineering curricula. We envision that this model will drive the 1development of subsequent tools and teaching resources which will further support theuse of the model in a diverse set of courses, disciplines, and institutions.Phase 1: Developing the modelThe first phase of this project was to
engineeringat the professional level (licensure) in the 21st century,” [1]. The introduction concludes that,“the EnvE BOK is not intended to be prescriptive, but instead to be directional, forward looking;and more of a compass than a detailed road map,” [1].In 2018, the Academy formed a volunteer task force to, “evaluate if changes are needed to the2009 Environmental Engineering BOK, and if necessary, propose a process to prepare the 2019Environmental Engineering BOK2,” [2]. The results from this volunteer task force were sharedwith the leadership of the Academy and the leadership of the Association of EnvironmentalEngineering and Science Professors (AEESP, or Association).In 2021, a panel discussion of the EEBOK1 was organized by the Environmental
reality demonstration was successfully performed for all projects. Three examplesof these projects and their outcomes were analyzed and presented: 1 - A geodesic dome for Marshabitation; 2- Dynamic Dolly; 3- Exofit Biomedical Device.Students evaluated the course design, including the XR prototype demonstration, was moreengaging. The evaluation of projects was less subjective, and the course design was moreinclusive than lecture-based courses. However, 54.4% expressed that this course required moreworkload than the traditional lecture-based courses.Keywords: Extended Reality, Virtual Reality, Augmented Reality, Engineering Education,Mechanical Engineering, Design, Biomedical Engineering. Body of the
findings to share with the research community and solicitfeedback as we continue our study. IntroductionSense of belonging (SB) is one of a number of ways to the fundamental human need for socialbonds and connections [1], [2]. Although SB has been defined and theorized in diverse ways, thisconstruct is distinguished as a subjective feeling that “persons feel themselves to be an integralpart of that system or environment” [3, p. 173]. Within education and educational psychology,ensuring that students develop SB within diverse educational settings and with the subject ofstudy has been considered crucial for their success [4]–[7]. Despite SB’s importance and growingattention, non-majority students (i.e
, ventilation and air purification. Filter standards andfilter testing technologies were discussed. ASHRAE and OSHA guidance concerning healthyindoor air quality (IAQ) was covered. A low-cost air quality sensor was installed in theclassroom that streamed data to the internet. Students were assigned projects utilizing this sensorand the neighboring outdoor sensors, which triggered interest in citizen science.1. IntroductionAir quality has been a subject of college education in engineering for many years, often includedin environmental engineering programs, which are frequently integrated with civil engineering.Civil and environmental engineering departments exist at leading institutions such as Berkeley(https://ce.berkeley.edu/), Stanford (https
. Preliminary findings indicate that homeschoolers made progress in all learningobjectives: apply terminology and concepts, defining the system, identify interactions, and createmodels of the system. The collaborative participation of parents and researchers in implementingthe STEM experience fostered a learning environment that enabled homeschoolers of differentages to collaboratively develop their systems thinking. This study contributes to engineeringeducation research by providing insights into the development of systems thinking among pre-college students within the homeschooling system.IntroductionSystems thinking is a fundamental aspect of engineering education [1]. The challenges engineersface are not isolated entities but are part of complex
detection, online learning, federated learning,distributed learning, and adversarial learning. We present learning outcomes and results usingsurveys and assessments. The developed DARE-AI modules help train the next-generation STEMworkforce with knowledge of integrated cybersecurity and AI that is expected to help not only tomeet evolving demands of the US government and industries, but also to improve the nation’seconomic security and preparedness.1 IntroductionArtificial Intelligence (AI) and Machine Learning (ML) have revolutionized numerous fields,enabling computers to learn from data, recognize patterns, and make autonomous decisionswithout explicit programming 1,2,3,4 . Unlike traditional rule-based programming, ML algorithmscontinuously
workforce for the future. I. INTRODUCTION AND BACKGROUND In this era where fossil fuel usage continues to rise despite the growth of renewable energyoptions worldwide, the holistic need for reducing greenhouse emissions is more critical thanever [1]. It is a well-accepted scientific fact that climate change, global temperature rise, andCO2 emission levels are interconnected. Over the past century, the Earth's average surfacetemperature has steadily increased, primarily due to a surge in greenhouse gases, which is anoutcome of human activities such as the increased use of fossil fuels, deforestation, andindustrial processes. As an alternative to fossil fuels and to solve the problems of climatechange, crucial
Engineering to realize the NAE’s vision forEngineering in the 21st century: “Continuation of life on the planet, making our world moresustainable, secure, healthy, and joyful,” [1]. Each student in the GCSP develops their ownpathway in the program to gain experience in research, interdisciplinary, entrepreneurship,global, and service-learning activities and coursework, all focused on an overarching GrandChallenges theme (Sustainability, Security, Health, or Joy of Living). Though all the GCSPs inthe GCSP Network are guided by the same framework and program outcomes, referred to as theGCSP Competencies (Talent, Multidisciplinary, Viable Business/Entrepreneurship,Multicultural, and Social Consciousness) [2], each GCSP has its own specific
construction management(CM) students with the goals of (1) understanding the impact of natural disasters on MH; (2)investigating the importance of integrating MH knowledge and skills into disaster managementpractices to promote a holistic, effective, and well-being-focused approach, including equippingthe DMW with these skills; (3) identifying the MH resources that are most beneficial forsupporting communities and prioritizing their MH and well-being during disaster management;and (4) exploring the importance of incorporating disaster management education, including MHcomponents, into civil engineering and construction (CEC) curricula. The results of this studyhighlight the critical importance of equipping the DMW with MH knowledge and skills to
objective measures, show that the Datastorm challenges helpstudents grasp content better and help them improve their soft skills within the context of teamwork.The authors also present feedback from faculty showing the Datastorm challenges’ impact on thequality of information delivery and real-time evaluation options available to Computer Scienceinstructors.IntroductionComputer Science and computing-based majors at the university level face a variety ofchallenges.One significant issue is low student engagement. Many students are unwilling to invest the effortneeded to grasp complex concepts and develop the demanding skills required in ComputerScience [1, 2]. This is a widespread issue with even international surveys reporting lowerengagement rates
and valued professional roles of engineers among engineering students in FinlandIntroductionEngineering as an endeavor is thousands of years old, and engineering as a profession ishundreds of years old. Yet, many engineering students lack a clear understanding of whatengineers actually do. Descriptions of engineering practice tend to emphasize technicalproblem solving and design [1], and value creation in engineering is often perceived asresulting from technological innovation [2]. Interviews and field observations amongpracticing engineers show that some engineers “tend to hide the social dimension of theirwork behind a technical facade” [1]. Faulkner sees this as a manifestation of a broadercultural phenomenon, which
setting.I. Introduction:Cognitive learning theory such as cognitive load theory [1] and more recently fuzzy trace theory[2] suggest that learners differ in their epistemological beliefs and attitudes, and factors such asage and gender influence them. However, the popular instructional models adopted in highereducation are built with the belief that the learners are uniform [3]. This unfounded belief canadversely affect their learning. Hence it is important to understand how the epistemologicalbeliefs and attitudes evolve over the years and whether gender influences them.This study is undertaken to find the differences in the epistemological beliefs and attitudesamong learners in a set of higher education programs that are offered to a wide
thecultural profiles of engineering students and professionals, especially with the proper applicationof established frameworks and models. Grounded in Hofstede’s cultural value model, this workseeks to characterize personal cultural orientation (PCO) profiles of FYE students via latentprofile analysis. We surveyed over 1,700 FYE students at a large Midwestern University withSharma’s 2010 PCO instrument. Data were processed via latent profile analysis with three steps:1) conducting confirmatory factor analysis; 2) clustering data using weighted factor loadings andevaluating potential results via model fit statistics; and 3) interpreting the final chosen resultbased on PCO profiles and demographic data. The findings reveal five distinct cultural
learners.IntroductionRecent advances in artificial intelligence have revitalized interest in personalized learning (PL).In particular, large language models (LLMs) have emerged as a promising tool to tailoreducational content to the needs of diverse learners in both K-12 and higher education [1].Although PL has been widely researched for its potential to optimize student engagement andimprove learning outcomes [2], its implementation often remains limited by constraints on real-time customization in either computer-based or in-person interventions. With modern LLMs,educators and researchers now have the tools to move beyond static resources or rule-basedadaptive tutors towards more dynamic systems that can customize learning materials on demand[3]. This shift not
values, indicating avoidance of directly teaching or assessing them. Facultywith 6–10 years of teaching experience were more likely to express discomfort with teachingvalues directly, while older and younger faculty appeared more comfortable addressing theseoutcomes.1. IntroductionEngineering education occupies a critical role in preparing students for both professional successand societal impact [1]. Engineers hold a position of significant power and privilege in society,influencing the allocation of resources, opportunities, risks, and harms across diverse socialgroups [2]. This responsibility necessitates an educational approach that extends beyondtechnical proficiency to include the development of ethical and values-based competencies [3].In
instructional designs in the context of evidence-based learning principles [1-4].The Importance of STEM EducationSTEM (Science, Technology, Engineering, and Mathematics) education is regarded as animportant factor in building and maintaining a nation's workforce, economy, competitiveness,and security in the modern world. [5], [6]. However, in the United States, there are more STEM-related jobs in the government and private sector than trained individuals to fill those jobs [7],[8]. One way to help fill this gap may be through STEM education at the high school levelbecause STEM education has been shown to promote interest in future STEM college degreesand careers [9], [10]. For instance, many students who enter into a major in STEM-related fieldsat the
digestible to K-12 students and broaden the impact of this initiative.IntroductionBioengineering, synonymously referred to as biomedical engineering, first developed as a fieldin the 1950s when engineers in academia developed an interest in biomedical challenges [1]. Asthe field matured and established its own identity, academic programs were gradually developedwith emerging guidelines for curricula. For an institution to receive ABET accreditation for abioengineering program, the curriculum must include (1) application of engineering principles,life sciences, and relevant mathematics, (2) exploration of biomedical dilemmas, (3) analysis andsynthesis of biomedical engineering devices, and (4) performance of biological measurementsand explication of
, many students still donot understand the full breadth of problems engineers solve. Studies continue to highlightcommon misconceptions about engineering work including gender stereotypes about engineeringand erroneous concepts about the nature of the engineering profession [1][2][3]. Unfortunately,these misconceptions are driving the U.S. towards a large talent gap such that the number ofengineering jobs that need to be filled in the future will outpace the number of engineeringdegrees awarded [4].For those students who eventually decide to pursue engineering, studies have indicated that whenhigh school students, especially first-generation students, choose engineering, their reasons rangefrom having a curiosity and interest in the subject
operationalized them for theengineering design context. While not intended to be exhaustive, these categories, listed below,served as a framework for guiding both the intervention and analysis. Their primary aim was toencourage students to examine several ways in which unconscious processes influence and biasdecision-making. 1. The Views of the People Around Us: How people may be influenced by their social environment and how it may be difficult to not conform to majority views. 2. Human Error and the Limits of Our Perception: The impact of “blind spots” in our perception due to limitations around what we are allowed or able to observe. 3. Internal Beliefs and Biases: This includes heuristics, quick assumptions our mind makes
and reconnect students in grades 7-14with in-person, hands-on activities in computing. The objectives were to: 1. Facilitate stronger identification of professional pathways in computing. 2. Facilitate stronger connection with the campus. 3. Educate those who may have a peripheral interest in computing as to the: a. Range of computing disciplines and professions. b. Real nature of computing. Our anecdotal observation is that present-day students are far more computer and technology literate as users of computer applications and technology, but have a surprisingly poor understanded of how computers work, are connected, and their information managed
pursue graduateeducation. Overall, this paper introduces a replicable methodology for analyzing curricula anddemonstrates its application through a case study of one institution’s computing programs.1 IntroductionThe rapid evolution of the job market, driven by artificial intelligence (AI) and automation, along-side shifting economic demands, underscores the need for an adaptable education system. Al-though educational institutions strive to equip students with the necessary knowledge for success-ful careers, many graduates struggle to land jobs that match their qualifications, even with the highdemand for tech talent. A 2024 study conducted by Hanson et al. [21] found that approximately37% of students in fields such as computer science (CS
inthe data that it is presented to and uses that knowledge to make predictions on data that it has neverseen before. With machine learning, computers do not need to be explicitly programmed to solvea problem. Machine learning utilizes data and algorithms and statistical models to solve a problemusing inference instead of instructions. A simplified machine learning flow is shown in Figure 1.Figure 1: Machine learning flow. Algorithm is trained on training and makes predictions on test data. 2Machine Learning Types Machine learning has three main categories, supervised learning, unsupervised learningand reinforcement learning (Figure 2). Supervised machine learning is a type of machine
grade.IntroductionIntroductory STEM (science, technology, engineering, and math) courses typically have highattrition rates. For STEM bachelor’s degree students in the United States, 48% leave STEMbefore completing their degree. They either switch to another major, or exit college beforeearning a degree [1]. This is of significant concern, as demand for skilled professionals in STEMis high, and attrition reduces the number of graduates available to fill these roles. STEM fieldsare critical for innovation and economic growth, and a lack of STEM talent impacts a country’sability to compete globally [1][2][3][4]. Research has shown that (among other factors),students’ belief in their own competence, how interesting or enjoyable they find tasks, and howmuch is required of
” [1]. Thisfollow-up will offer an additional three years of data related to course content, course materials,student demographics, and grades. Student’s progress and performance in future math coursesand performance in continuing in engineering courses will be evaluated over 2019-2022.Notably, the last two years evaluated in this study (2021 – 2022) represent a fully in-personexperience compared to the hybrid cohort of 2020.The first-year engineering math curriculum at Clemson University was designed to help studentsunderstand the relevance of basic math skills in engineering and strengthen mastery ofprerequisite math learning outcomes to improve preparedness for engineering. While engineeringprograms and professional industries expect
. Butcher, Cornell University ©American Society for Engineering Education, 2025 Exploring the Efficacy of Generative AI and ChatGPT in BME Instructional Labs: A Case Study on GABA Receptors and Synaptic PotentialsIntroductionThroughout history, new technologies have challenged traditional practices. From Google'simpact on education [1, 2] to MOOCs' rise and fall [3-6], each technology brings potential gainsand losses. The education sector has been no exception to the challenges brought about by thesenew technologies. Just as one challenge is understood, another requires the education sector toadapt, understand, master, and grow. The advent of high-capacity computing brought aboutartificial intelligence (AI) and