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
asking them togenerate a high-level description of learning activities that met standards for both disciplines.Four humans rated the LLM output – using an aggregate rating approach – in terms of (1) whetherit met the CS learning standard, (2) whether it met the language arts learning standard, (3)whether it was equitable, and (4) its overall quality.Results: For Claude AI, 52% of the activities met language arts standards, 64% met CS standards,and the average quality rating was middling. For ChatGPT, 75% of the activities met languagearts standards, 63% met CS standards, and the average quality rating was low. Virtually allactivities from both LLMs were rated as neither actively promoting nor inhibiting equitableinstruction.Discussion: Our
work has realized the impact of industry-sponsored projects on the students' self-efficacy,in which students on industry-sponsored teams showed larger increases in self-efficacy comparedto university-sponsored [1]. This work aims to closely examine students' self-efficacy by utilizingthe EDSE survey to understand trends amongst cohorts, and understand influencing factors forsuccess.1.1. Capstone Design Capstone Design is a course that students commonly take during their final year ofundergraduate studies in engineering disciplines. This course is typically structured to bridge theworld of education and real-world application [2]. Overall, this course serves as the culminatingexperience for students at the end of their college career
for Industrial Applications. Previously, he had Job experience in the field of Sourcing & Procurement, Production, and Supply chain in different companies in Bangladesh for more than seven years. His GitHub account is https://github.com/Atik1219. and email is atik.kuet.09@gmail.com.Selim Molla, University of Texas at El Paso ©American Society for Engineering Education, 2025Development of Digital Laboratory Modules Using Computer Simulation for Enhanced Learning Experience in Manufacturing Education 1 AbstractThe complexity of modern manufacturing
solved a graded activity. • Reactor Design was taught with video lectures before class, and class time was used for instructor-led example problems along with occasional, multiple-choice conceptual questions. • Process Control was taught with video lectures before class, and the professor worked an example in class before the students worked a graded problem in groups during class.All three courses had traditional homework, exams, and design projects. We surveyed the entireSpring 2024 class of 17 students in Fall 2024 to assess two items: 1) the student preferences forthe various teaching methods, and 2) the differences between faculty intentions and studentperceptions regarding the teaching methods used in each class
performance andengaging in field-related extracurricular activities influencing the final metric. One detail thatis often cited as a key factor is post-graduation or career success[1], [2]. But research lookinginto the preparedness of early post-grads has raised some concerns, with the Institute ofEngineering and Technology reporting that up to half of engineering students graduatewithout enough of the technical or interpersonal skills required by potential employers[3].This leaves three groups in a tough situation: companies looking to hire who need to quicklyupskill new employees[4], Higher Education Institutions (HEIs) who have to rapidly adjustcurriculums to meet the ever changing demands[5], and, most importantly, new graduateswho must take on
outcomes.Among various methods for conveying this material, recorded videos by instructors are often apreferred resource for students. However, not all videos can be designed similarly; some areintended for topic descriptions, while others instruct techniques using practical examples. Giventhe crucial role these videos play in student learning and outcomes, it is important to understandstudents' perceptions of the benefits of video materials in a flipped classroom setting.In this paper, we present a case study of a flipped programming course where students wereintroduced to two types of videos: 1) concept videos in which the instructor explainsprogramming concepts and 2) coding videos that feature the instructor demonstrating theseconcepts through live
and engineering, thermo-fluids engineering, and microfluidic technology. ©American Society for Engineering Education, 2025Assessing the Impact of Makerspace Workshops on Breaking Academic SilosThrough Cross-Disciplinary CollaborationI. IntroductionAs the world confronts increasingly complex global challenges from climate change and publichealth crises to rapid technological advancements, academic institutions worldwide arerecognizing that preparing future engineers requires more than traditional, siloed curricula [1],[2]. Contemporary engineers must possess an expanded skill set that combines deep technicalexpertise with strong communication, ethical reasoning, and collaboration skills, enabling themto address
for the professor or teaching assistant to be able torespond to in a timely manner.One of the initial uses of AI for supporting teaching expanded the use of the Piazza Q&Aplatform by Georgia Tech [1] (named Jill Watson) and then Stanford University [2]. In this latterwork an AI tool was trained to address student inquiries in a core computer science course using1500 questions and answers archived from Piazza. The questions were categorized as beingrelated to a course policy, related to homework or some other assignment, or about a conceptualquestion. The results showed that the bot did very well at answering policy questions, faredrelatively well on assignment questions, but struggled with addressing conceptual queries, eventhough a
known for many years [1], [2], [3]. This gap has persisteddespite pedagogical and curricular changes, such as PBL, CDIO, capstone courses, and thebroader integration of professional skills into engineering education [4], [5], [6] [7], [8].Additionally, research documents the dissatisfaction of many early career engineers with theircareers [1], [9], [10], and their frustrations mirror those of their employers: they did notanticipate the integrated nature of professional skills in modern engineering work. Much of thisdissatisfaction, then, can be attributed to not just a “readiness gap” but also to an “expectationgap,” meaning that many engineering students have an unclear or mistaken vision of their futurework [1], [9], [11], [12]. Despite the
learners, canfoster a more personalized learning experience. A key aspect of this is targetedfeedback, which plays a vital role in student development. This study presents astrategy that enables instructors in chemical engineering courses to create bespokeproblem sets and solutions tailored for their students. Ethical AI use and intellectualproperty contributions are discussed extensively in the text. The issues consideredwere (1) bias in AI-generated problem statements; (2) academic integrity andplagiarism; (3) data privacy and student information; (4) openness and explanation;(5) intellectual property and copyright; and most importantly, (6) the general frameworkfor ethical use of AI in engineering education.This approach leverages Python
-chatbot),offer a scalable model for integrating emerging technologies into CEM curricula. These findingshighlight the potential of structured AI education in preparing future construction professionalsfor a technology-driven industry.IntroductionThe construction industry is experiencing a rapid digital transformation, with AI emerging as apivotal technology for enhancing efficiency, accuracy, and decision-making processes [1]. As theindustry evolves, there is a growing need to prepare future construction professionals with theskills necessary to leverage AI technologies effectively. While AI adoption in constructioncontinues to accelerate, there remains a significant gap in CEM education regarding practical,hands-on experience with AI
, and from psychology. The overarching goal of the course was to develop aninterdisciplinary understanding of the necessary balance between the needs of society andengineering design. It explicitly addresses four societal impact outcomes in ABET Criterion 3:public health and safety impacts of design, ethical decision-making, collaborative productivity,and effective communication with diverse audiences [1]. This course is supportive of theEngineering One Planet (EOP) program of the American Society for Engineering Education(ASEE) [2]. In addition, the importance of making design decisions in economic, environmental,and societal contexts is emphasized from the perspectives of engineering and physical andmental health.IntroductionA new technical
3.9 million in 2025 andfall to 3.5 million by 2037 due to declining birth rates [1]. Perhaps more importantly, fewer highschool students are choosing to attend college, with the rate of college-bound high schoolgraduates falling from 70% in 2016 to 61.4% in 2023, the lowest level in three decades [2].While overall retention and graduation rates are important, a deeper dive into the factorsaffecting graduation and retention is important if colleges and universities are to help studentswho are retained and graduate at lower rates than their peers. Many factors have been examinedfor their effects on retention and graduation rates including gender, ethnicity, high schoolpreparation, performance in engineering preparatory classes, especially math
high schools increasingly integrate engineering into their curricula [1], introductoryengineering courses are often where students first become acquainted with the foundationalprinciples of engineering. As such, these courses aim to shape students’ initial impression ofengineering and excite them about it [2], [3]. Beyond exposing students to engineering,introductory engineering courses are typically structured to establish an academic environment,develop critical study skills, instill the engineering culture, and promote camaraderie among peerstoward success in subsequent coursework [4], [5], [6], [7].Introductory engineering courses have been demonstrated to boost students’ retention rates withintheir academic track [8], yet attrition
engagement [1]. Yet,civic education is increasingly enhanced through the integration of technology and design-thinking methodologies, fostering student engagement and critical thinking. Project RISEconsiders civic education the process of enabling students to have civic knowledge, civic skills,and civic dispositions and actions [2]. Civics education, within the context of Project RISE, is theactive, informed, and justice-oriented participation of individuals in their communities anddemocratic institutions. It encompasses the development of civic knowledge, skills, anddispositions that enable individuals to critically analyze societal challenges, collaborate acrossdisciplines, and employ problem-solving frameworks—such as engineering design thinking