©American Society for Engineering Education, 2025 International Coral Reef Research Experiences for Community College StudentsIntroductionCommunity colleges are evolving from their traditional roles of providing a two-year experienceor a technical education into institutions capable of offering not just associate degrees, but careerprograms, professional and continuing education, language, and equivalency programs andbeyond [1], [2], responding to the changing needs of communities and their economies.However, research practices are not inherent to the community college model and are rarelyincluded as a component in student training or capstone experiences. Additionally, coral reefscience is considered an
CS education. We recommend educatorsguide students in leveraging custom, context-specific assistants to improve learning and developcritical AI application skills.IntroductionLarge Language Models (LLMs) enable educational platforms to support students throughadvanced tools with real-time personalized feedback, guidance, and engagement mechanisms.By employing methods like retrieval-augmented generation (RAG), LLMs are increasingly ableto overcome challenges related to scalability and handling unexpected or unforeseen inputs, asare often experienced with intent-based chatbots [1]. RAG-powered assistants demonstratesignificantly improved performance in terms of response accuracy, adaptability, and studentsatisfaction [2].This study examines
. ©American Society for Engineering Education, 2025 Lessons Learned: 35 Years of Impact of the Leonhard Center for the IntroductionThis Lessons Learned paper presents the 35-year history of The Leonhard Center for the Enhancement ofEngineering Education [1]. The Leonhard Center is a teaching and learning center dedicated to theenhancement of engineering education within the College of Engineering at Penn State University.Established in 1990 by an alumnus of the university, William and Wyllis Leonhard, the Leonhard Center hasthe mission of catalyzing and supporting the enhancement of teaching, learning, and assessment at Penn StateUniversity to deliver world-class engineering education [1]. The Leonhard Center was the first of its kind tobe housed
support the United States inremaining a strong economic and global competitor [1-3]. However, through analysis of nationaldata sets, approximately only half of the students who enter a STEM major will graduate with aSTEM degree [4].Recent research examining the reasons why students leave STEM disciplines show that theytypically leave for non-technical reasons including poor teaching, curriculum overload, limitedadvising and support, or a rejection of the competitive culture in many STEM disciplines [7-10].In more recent years, studies have continued to document the same factors influencing attritionin STEM degrees as well as student’s lack of self-efficacy, failure of the material to capturestudent interest, overly competitive grade structures
is more active. Phrases such as instructors “delivering a lecture,” courses “contain”or “cover” content, and students “grasping a concept” all point to thinking about learning asgaining an object. As Sfard [1] and others observe, we can also view learning as participating ina practice. This metaphor aligns with engineering mindsets, wherein we often care less aboutwhat students know, and more about what they can do with their knowledge. This shift inmetaphor suggests that multiple approaches to learning may be needed for different subjects.How do other common metaphors, such as learning as lighting a fire and planting a garden,influence how we teach? Drawing on Ingold’s anthropology of lines [2], we outline howmetaphors such as learning as
built into the policy. Thispaper provides a review of how five states are evaluating their teacher capacity to offer computerscience, including their calculations and the opportunities and limitations associated with theapproaches. The ultimate goal of this work is to provide robust and flexible guidance to otherstates to ensure that any policy is well planned and supported to promote equitableimplementation.IntroductionAs states increasingly recognize computer science (CS) as essential for preparing students for thedigital future, the push to make CS a graduation requirement has unveiled a significantchallenge: the shortage of qualified CS teachers, especially in high schools. Teacher shortagesare a universal problem [1], [2], [3] and are
Figure 1: Loginchanged, the solutions are well known online. The students simply follow the solution manualformulas and swap in their random values. These platforms are also inflexible for the instructors,they are unable to modify the problems, only select which are to be used. Finally, and perhapsmost importantly, they are very expensive. A subscription to one of these services usually costsmore than $100 per semester per student which can be prohibitive, especially if several classes areusing them in the same semester. The current trend in education is toward Open EducationalResources (OER) to make education affordable for everyone. This homework platform and anaccompanying OER Microelectronics textbook allows our students to study
constraints are often immov-able. The paper concludes by suggesting future directions for constraint-driven embedded systemsprojects, emphasizing the potential of this method to continually create novel, challenging learningexperiences in the face of rapidly evolving technology.1 IntroductionEmbedded systems education often struggles to balance theoretical knowledge with practical, en-gaging projects. While microcontroller-based projects are common, they frequently lack the scaleand complexity that mirror real-world engineering challenges. Additionally, with the success ofMaker Spaces and the popularity of many of these projects, finding interesting projects that havenot already been covered deeply on the web is difficult. This paper proposes an
, attached to a cantilevered frame secured to a 135’ LMS irrigation pipe. In this paper, we present asummary of the students’ approach to managing expectations via detailed calculations, modeling, andscaled prototypes for a community partner whose vision included reliance on future infrastructure to beused in a novel and unexpected way.IntroductionCommunity engaged learning (or service-learning) enhances student education by linking theory topractice and classrooms to communities [1][2]. Partnering with community organizations contextualizesengineering, broadens perspectives on who engineers can be and serve, and supports diverse studentretention, particularly for those motivated to create impact [3].Well-structured service-learning fosters deep
have someversion of a generative AI chatbot to interact with their clients that are available 24 hours a day,7 days a week, and 365 days a year.Artificial intelligence can be defined from the Merriam-Webster Dictionary as “the capability ofcomputer systems or algorithms to imitate intelligent human behavior” and generative AI can bedefined as a type of AI technology that generates content such as text, images, audio, and video.A chatbot is a computer program that uses a large language model (LLM) to simulate aconversation with human users, typically through text [1]. Therefore, a generative AI chatbot isa type of artificial intelligence system designed to engage in human-like conversations bygenerating text-based responses dynamically rather
and experiences in a specific direction, butthey also help people quickly identify what skills and courses students are expected to have for aspecific engineering major. While most modern engineering work is done through trans-disciplinary teams whose skills may overlap [1], students are still expected to choose a specificmajor that often connects them to a specific engineering department, coursework, andrequirements. Because of how much this decision can shape a student’s experiences in college,students often seek help and advice in finalizing this decision. There exists a collection ofresearch that examine how students select engineering and an engineering major (e.g., [2], [3]),which has helped develop exploration activities for students
approach and learning style of a textbook, and regenerateproblems algorithmically to give students unlimited opportunity for practice and mastery [1].Similarly, ALEKS by McGraw Hill is another digital platform that allows instructors to buildassessments and track student performance. However, the key difference between the two is thatALEKS uses an adaptive learning approach, requiring students to demonstrate mastery of a topicbefore progressing to the next. ALEKS uses machine learning rooted in Knowledge SpaceTheory to create and continually update a detailed map of each student's knowledge. It identifies,in real time, whether a student has mastered a specific topic and if they are ready to learn it. Thisapproach is to keep students engaged
sustainability into engineeringeducation. A new course is recommended to prepare engineering students for the globalized field,covering cultural, ethical, and practical aspects of global engineering.IntroductionGlobal education serves as a formidable catalyst in shaping the trajectory of a sustainable futurefor our planet. This report meticulously examines the multifaceted ways in which global educationinitiatives play an instrumental role in cultivating environmental consciousness, instilling socialresponsibility, and fostering cultural awareness. The narrative underscores the harmoniousintersections between these initiatives and two foundational frameworks: The United NationsSustainable Development Goals (SDGs – Appendix 1) and the Grand Challenges
. ©American Society for Engineering Education, 2025 Democratizing the Analysis of Unprompted Student Questions Using Open-Source Large Language ModelsAnalyzing student questions can help instructors make informed pedagogical improvements byproviding a better understanding of student thinking. In past literature, the analysis of studentquestions (SQs) has primarily been conducted using taxonomic categorization [1]. Thesetaxonomies focus on various aspects of learning. For instance, utilizing taxonomies based onBloom’s taxonomy [2] can reveal what cognitive levels students are utilizing or struggling with.On the other hand, the taxonomy proposed by Scardamalia and Bereiter in 1992 [3] can be usedto determine how familiar
complementary direct-write nanolithography process that utilizes thermalscanning probe lithography (t-SPL) to generate nanopatterns [3-6]. Table 1 compares thevarious nanolithography techniques and highlights the advantages (+) and disadvantages (-)for each technique. From Table 1, it shows that there is not a “perfect” nanolithographytechnique for educational purposes, but that t-SPL is the leader in being able to seenanopatterning in real time and in a cost-conscience manner but at the expense of not beingindustrially relevant. Focused-Ion Thermal Electron Beam Maskless Layer Parameter Beam
engagement, and talent development activities. It is expected that thecombination of all these elements implemented will increase their self-efficacy, solidify theiridentity as engineering professionals, and impact their persistence toward degree completion [1-5]. The funded scholars are supplemented by the inclusion of additional students who can receiveall SEED services other than scholarships. These additional students are selected from the groupof scholarship applicants and are positioned for consideration for future funding through theprogram should there be attrition in the pool of funded SEED scholars.An important objective of the project is for SEED scholarship recipients and non-funded guestscholars to participate in carefully scoped and
approvals and 510(k) clearances) artificial intelligence(AI) and machine learning (ML)-enabled medical devices by January 2014 to over 1,000 bySeptember 2024 [1], machine learning has seen explosive growth in biomedical engineering(BME). Besides AI/ML-enabled medical devices which focus on biomedical signals andimaging, ML is actively influencing BME research in areas such as drug design [2], tissueengineering [3], biomaterials [4], and medical diagnostics [5]. AI/ML-based products, especiallyin large language model (LLM)-based chatbots, are quickly integrated into the currenteducational environment [6]. Although initial investigations of using these chatbots such asChatGPT or Perplexity AI in an academic context seemed underwhelming [7
to provide engineeringstudents with an interactive learning experience. Previous studies have shown (1-5) the efficacyof teaching students with an active learning approach versus a more traditional lecture setup,with a number of approaches already available, such as simple active discussion, think-pair-share, flipped classrooms, etc. Our approach is differentiated by the inclusion of hardware to addboth a visual aid and an opportunity for hands-on experimentation while keep the costs lowenough for a classroom setting. Learning with a hands-on, interactive approach is supported bysocial cognitive theory (SCT) (6-8) and information processing theory (8). Unlike earlier viewsof learning theory, which simply posit that the key to learning is
strategies, the academic persistence of S-STEMscholars and their career transition into STEM workforce.Table 1 provides a quick overview of the placement of 11 scholars in engineering positions andsome unique experiences they have had during the academic program. Internships and researchopportunities are discussed in subsequent sections along with specific observations and lessons.Table 1. List of S-STEM Scholars and their Placement in STEM fields upon Graduation Scholars Graduation Internships Research Current Date Opportunities Placement Scholar 1 ED May 2022 MSOE Raider Additive Manufacturing Provisur
Engineering Education Faculty Member at the University of Michigan Ann Arbor.Dr. Lisa R. Lattuca, University of Michigan Lisa Lattuca, Professor of Higher Education and member of the Core Faculty in the Engineering Education Research Program at the University of Michigan. ©American Society for Engineering Education, 2025 ECR-EDU Core Research: (Mis)alignment between ME course content and student career intentionsIntroduction and BackgroundMechanical engineering (ME) follows a similar curriculum across American institutions [1], [2],including topics such as mechanics, thermodynamics, and material science [2]. Design coursesand technical electives offer some opportunity for additional topics
mixed-method evaluationsusing surveys, focus groups, and retention rate analysis. The primary objectives focus onenriching online technology courses with VR technology to increase attraction and persistence,redesigning course materials for immersive environments, and strengthening engagement andretention through gamified experiential learning. In addition, the project investigates thecorrelations between student perceptions of proficiency, engagement, and outcomes in VRcourses. The survey results, discussed in this paper and illustrated in Figure 1, reveal positivetrends in student engagement and perceptions of equity, highlighting VR's potential to scaleonline STEM education.As Peter Drucker famously stated, "The best way to predict the future
for them and their peers.Introduction The sciences and engineering disciplines are often characterized by the lack of humaninteraction and a personalized relationship between students and professors [1], [2]. Thesepractices result in students switching majors or dropping out of college. The aspect of studentengagement through high-impact practices as an effective tool for retention calls for effectiveand well-planned research activities, where undergraduates feel involved and included as part ofthe research community [3], [4]. One common strategy is undergraduate research experiences(UREs), where undergraduate students engage in research activities [5]. URE programs providestudents with a further understanding of how knowledge is
, Integral,Power Series, and Function ConceptsAbstractPower series is a concept that requires knowledge of extensive calculus sub-conceptual knowledgethat includes rate of change and antiderivative knowledge and the pedagogical efforts to measureconceptual understanding of STEM students’ is recent ([1]-[9].) If and only if (Iff) is one of thepedagogical techniques introduced in [10] to analyze calculus questions and educators areencouraged to use this technique to structure questions. In this work, we utilize iff methodologyintroduced in [10] and analyze empirical data collected at a university located on the Northeasternside of the United States. The research received Institutional Review Board Approval (IRB) tocollect written and interview data
computing designed forstudents with a bachelor's degree (or higher) and little to no background in computing.Technology is among the world's fastest-growing economic sectors, with some of thehighest-paying jobs. Yet the current trajectory of the tech talent pipeline falls far short of meetingthis demand. Many groups (for example, women, African-American/Black, Hispanic/Latinx,American Indian/Alaskan Native, and people with disabilities) have historically been excludedfrom this opportunity [1] and [2]. There is a high demand for employees in the computing field,but entry into this field can be challenging. Our graduate certificate in computer science (CS)aims to bridge this opportunity gap by leveraging the unique backgrounds and experiences
with Disability Act [1] requires educational institutions to provideaccommodations to students with diagnosed physical, mental, and psychological disabilities.Accommodations for students with physical disabilities may include braille texts, accessibleclassrooms, or transcripts. Mental disabilities such as learning disabilities, ADHD, or autism mayhave accommodations such as extended time for tests and assignments, reduced distractions, andnote-taking services. More excused absences, extended time, and a reduced course load mayaccommodate psychological disabilities such as depression, anxiety, and bipolar disorder. [2]Accessing accommodations in higher education is not straightforward. Many students remainunaware of available resources and
, is pivotal to addressing the challenges of modern infrastructuredevelopment. However, these fields face a persistent talent gap, particularly amongunderrepresented groups. Addressing this issue requires targeted educational interventions thatcombine theoretical learning with practical exposure to career opportunities [1].Summer camps have proven to be effective platforms for engaging students in STEM disciplines,offering hands-on learning experiences and fostering early interest in technical fields [2]. Thisstudy evaluates the outcomes of a one-and-a-half-week summer camp designed to spark curiosityand encourage career aspirations in transportation and STEM-related domains. Through pre- andpost-surveys, the program assessed shifts in
their best time and lengthier exams may be givenfor the courses that require it. Since the study is conducted at a regional teaching campus of amajor state university, many students are working adults with full time jobs and in certain cases,full time families. This approach has also helped those students to stay on the course for theiracademic journey.IntroductionIn this study, the previous framework developed for online exams in Blackboard [1] will befurther advanced to make the online exams more randomized. In the previous work, detailedexamples were provided for three courses in mechanical engineering (Dynamics of machines,Machine design, Vibrations) and one general engineering course (Software tools). In thoseexamples, numerical values
Education, 2025 Re-Designing Fluid Mechanics to Integrate Experiential Learning with Videos and Workshops1. Introduction and BackgroundEngineering programs are well known for their low retention, high attrition rates, and lack ofdiverse participation [1]. According to the American Society for Engineering Education(ASEE) the average retention rate for engineering students in the USA is approximately 50%[2] with around 60% of engineering students changing majors or leaving the universitybefore graduation [3]. These problems are not unique to the USA. According to the Centerfor Research and Information of the Israeli Parliament (Knesset), as of 2022 only about 19%of all undergraduate students in Israel are
, explaining that the initially silent think step is deliberately included toencourage participation by quieter participants. Through two cycles of think-pair-share, thegroups considered two questions: • Question 1: What do new engineering faculty need to know about inclusive teaching at your institution? • Question 2: How can a welcome academy convey this content actively, compellingly, and effectively?Participant responses were recorded by the facilitator on flipcharts provided by the meetingorganizers, and have been reproduced verbatim in Boxes 1-2, then sorted to list the notes in alogical order for presentation. During this brief workshop, participants generated a preliminary syllabus (Box 1) and
positions. After two yearsof working as engineers and completing technical, design, and professionalism credits, studentsgraduate with a B.S. in Engineering. Participants in this study are a part of the IRE STEMScholars program, which helps financially support low-income, high achieving students for theirBell Academy semester, and provides additional mentorship and career development supportresources through to graduation. This program supports a diverse population of individuals ontheir pathway to graduation, with a range of backgrounds and experiences [1].This work will notfocus solely on low-income experiences, but rather the more nuanced identities and experiencesof the students [2].Engineering Identity and BelongingEngineering identity is