of the COVID-19 pandemic, to the current year’s team concentrating onimplementing sensors in the hand and refining the ergonomics of the existing design. The paperwill also include student & faculty reflection and discussion of the faculty facilitation needed forsuch a service-based project and how engineering educators can consider implementing suchprojects into their programs.IntroductionInterdisciplinary team-based projects in engineering education are an approach to experientiallearning which can provide students with a diverse learning opportunity to work closely withindividuals from different disciplines [1, 2, 3]. Some of the benefits of participating on aninterdisciplinary team include unique solutions to solving complex problems
data science micro-credential have unique opportunities to improve critical super-skills, including writtencommunication, project management, iterative thinking, and real-world problem-solving.THE NEED FOR DATA ACUMENEngineering disciplines are increasingly adopting and integrating data science into their problem-solving and experimental approaches [1-3]; yet few engineering programs directly integrate datascience and visualization into their curriculum. In an effort to address this need and respond tothe NASEM report on Data Science for Undergraduates, which calls on institutions to increase“data acumen” through “a range of educational pathways,” [REDACTED] School ofEngineering and Applied Sciences launched an undergraduate micro-credential
National Science Foundation and National Leadership Grants for Libraries. ©American Society for Engineering Education, 2023 Cultivate the Problem Exploration Skills for Biomedical Innovation George Tan1*, Sampa Halder1, Luke LeFebvre2 1 Department of Industrial, Manufacturing and Systems Engineering Texas Tech University, Lubbock, TX 79409 2 School of Information Science University of Kentucky, Lexington, KY 40506 *Corresponding author: george.z.tan@ttu.edu
Reform and Research Activity. She obtained a Ph.D. in English Literature from Chiba University in 2002. Her current main research interests are: 1) how including humanities courses in an engineering education curriculum can help students to gain flexibility, and an appreciation of equity, and a greater richness of ideas; 2) finding and solving the systematic issues impacting the effectiveness of engineering education, specifically in the context of project-based learnings; and 3) assessing the impact of interdisciplinary engi- neering project-based learnings. Below are her recent presentations at international conferences: WERA 2022, APAIE 2022, IIAI DSIR 2021, IIAI DSIR 2020, WERA 2019. She obtained the Outstanding Paper
, and its capacity to combine expertise andcompetencies from various disciplines, including computer science, electrical engineering,mechanical engineering, and mathematics. Robotics covers a wide range of fields and promotesthe development of critical thinking skills such as problem solving, systematic reasoning,abstraction and generalization, as well as collaboration and communication [1, 2]. This growinginterest in robotics has been accompanied by the development of accessible open-sourceplatforms, such as Arduino and Raspberry Pi, which enable both novice and expert users to createelectronic projects, from simple LED displays to complex robotic systems. This has resulted inthe creation of several commercially available educational robotic
certain procedure to follow and a right answer to be calculated. Sometimestechnology such as the online learning management system was used to distribute and collectexams. The same strategy used in the individual course was usually the same manner used forexams.Literature ReviewPast research on this subject has primarily focused on the effectiveness of testing methodsinstead of the stakeholder’s views on methods. Some of the known advantages and disadvantagesare listed below, in Figure 1, from views brought up by the participants as well as specific pointsoutlined in congruent research papers [1, 2]. Table 1: Summary of Advantages and Disadvantages Advantages Disadvantages Reduce
Learning is a form of AI machine learning that has gained a great deal of recognition in thepast 10 years in a wide range of areas such as medical diagnosis, quality assurance, defectdetection, face detection, autonomous vehicles, and many others. Deep learning networks,however, typically require large training databases of labeled images and often requirespecialized hardware and high-level software expertise. Techniques, such as transfer learningand the proper choice of software tools can mitigate some of these requirements. This paperdescribes a new, project-based course module to introduce deep learning and computer vision toundergraduate multidisciplinary engineering students in a robotics design and applications courseusing MATLAB software.1
separating this course out is twofold: 1. To remove the computationalrequirements for Intro to Engineering Design so it is accessible to any student at the college, and2. To have students gain another transferrable skill early in their academic career.When designing the introductory courses for this program, we also considered the local K-12landscape, as we draw many students from these schools. Students can receive college credit forour Intro to Design class if they complete an introductory course through the Project Lead theWay high school curriculum and a capstone project at their high school. Based on expressedinterest by the high schools, we are offering a section of our first-year MatLab and CAD coursesin the evening in addition to the more
success seminars. This paper will share theexperience of the Center’s first year. Communication was a huge component of the EngineeringSuccess Center’s effort due to the wide distribution of students across learning modalities andphysical locations. Analysis of effective means of student engagement and the impact ofattendance on presentations/seminars due to remote offerings will be discussed. Tutoring wasoffered as a hybrid option to broaden the range of students participating and engaging with theservice. This paper emphasizes the lessons learned and the external evaluation conducted at theend of the Center’s first semester.1. IntroductionIn 2021, San Francisco State University was awarded a National Science Foundation (NSF)Hispanic
systems, ii) formulation andimplementation of advanced model-based robot control algorithms using classical and modern controltheory, and iii) programming and performance evaluation of robotic systems on physics engine robotsimulators. Course evaluations and student surveys demonstrate that the proposed project-basedassignments successfully bridge the gap between theory and practice, and facilitate learning ofcontrol theory concepts and state-of-the-art robotics techniques through a hands-on approach.1 IntroductionControl theory is a key foundation in the fields of robotics and engineering and is an essentialsubject in both undergraduate and postgraduate engineering curricula. It provides a mathematicalframework for analyzing and designing
).Various types of content analyses were conducted based on how these programs are described ontheir websites; differences among the program name groups were not identified but the corporawere too small for robust analysis. Overall the paper provides enhanced understanding of thegoals and curricula of these non-disciplinary engineering degree programs. This may be helpfulas programs consider suitable names for non-specialty engineering degrees.BackgroundThere is a need for students to “gain the confidence and competence required to enter anincreasingly complex and diverse engineering industry” [1]. A recent ‘Engineering 2035’ effortin Australia [2, p. 34] “foresaw greater diversity of engineering work” characterizing it as“increasingly complex and
theprimary course to fulfill criterion 4, which requires students to be ready for engineering practicebased on the courses completed earlier. In addition, the capstone course also supports criterion 3,which requires students to show their ability to design a system, component, or process to solve aproblem [1].Significance of multidisciplinary aspects in the capstone experienceMultidisciplinary is one of the most important aspects of capstone experience [2-3]. Studentsfrom different majors can help each other to accomplish the project. Each student could learnabout disciplines other than their own area, an essential aspect of the industry nowadays. Thiswould also help students get motivated to work further on the project [4]. When looking at real
(COEIT) at the University of Maryland BaltimoreCounty (UMBC). These students, known as teaching fellows, not only have an impact on theirengineering and computing peers (students who they taught), but also develop skill sets in anunconventional way giving them new routes into academics and industry. Many in their third,fourth or fifth year of their program, perform, act and behave as potential future faculty (leadtheir own discussion/class, grade papers, hold office hours etc). In a recent case study [1],students discussed their consideration going into faculty positions and found this programincreased their efficacy in both their professional and technical competencies.In the commitment to teaching and innovation excellence, UMBC decided to
includecommunication skill development throughout their curricula. ABET-accredited engineeringprograms, for example, must demonstrate that graduates can communicate effectively with arange of audiences (Student Outcome 3) [1]. Furthermore, over 75% of AACSB-accreditedbusiness programs include oral communication as a learning goal [2]. Oral presentation skills inparticular have been recognized as a great need for graduates since the 1990s [3] but researchthat focuses on oral communication, except for in ESL and EFL classrooms, tends to be sparse.Effective oral communication cannot just be “studied” but requires practice. In higher education,this practice often comes in the form of delivering oral presentations (e.g., PowerPoint/seminars,posters) in the
assessmentsand formative assessments. Summative assessments evaluate learning at the end of instruction andare essentially the grade. On the other hand, formative assessments occur during the learningprocess. Assessment practices can play a significant role in students’ learning experience [1].Assessment influences learning in several ways including providing self-confidence andmotivation to students, encouraging both active and passive learning styles, evaluating andreinforcing learning, and emphasizing what is important and providing feedback [2]. However, thequality of assessment is based on the practices employed by instructors [3]. Good practices forassessment integrate the three key elements of teaching and learning including active learning
implementation, testing details, experiences gainedand future work.1. IntroductionAutonomous vehicles have gained considerable interest in recent years due to their potential fordisruption. As they advance in capability and increase in adoption, studies have shown autonomousvehicles can reduce accidents and traffic congestion [1]. The race to achieve full autonomy isundoubtedly here and many companies are taking part in it. Although the closest purchasable fullyautonomous vehicles are those with adaptive cruise control (ACC), there are now driverless, fullyautonomous commercial vehicles on the roads in the United States. Waymo [2], a subsidiary ofGoogle entirely focused on self-driving, recently released a driverless ride-hailing service inPhoenix
education research, conducted as a collabora- tive partnership involving engineering and education faculty, postgraduate and graduate researchers, and K-12 educators, has: (1) created, implemented, and examined over 100 standards-aligned robotics-based science and math lessons and (2) developed, practiced, and examined research-guided pedagogical ap- proaches for science and math learning using robotics. He received NYU Tandon’s 2002, 2008, 2011, and 2014 Jacobs Excellence in Education Award, 2002 Jacobs Innovation Grant, 2003 Distinguished Teacher Award, and 2012 Inaugural Distinguished Award for Excellence in the category Inspiration through Lead- ership. Moreover, he is a recipient of 2014-2015 University
, and ability to function effectively on a team, with the latter twolearning outcomes scoring lowest in the pre-surveys. In addition to the survey analysis,lessons learned and recommendations for effective online education are discussed. Asonline education becomes more popular and in some cases more necessary, it is importantto understand the impact on engineering education, particularly in situations of forceddistance education. This study provides insight into the challenges that come withemergency online instruction and could drive decisions on priorities for in-personlearning environments.IntroductionHigher education in an online learning environment has been shown to be at least as effective asface-to-face, is appreciated by students [1, 2
technologies, they need people with particular kindsof competencies (Aldrich, 1979). In this paper, we draw from our experiences to provide an1 Authors listed in alphabetical order with equal contribution. Corresponding author: Marina Dias mvbdias@amazon.com2 All authors are affiliated with Amazon.com, Inc.example of a multi-disciplinary team conducting talent management research within the techworkforce of the 21st century, and describe some of the typical roles one may find at similar techteams that engineers and engineering educators may join.Talent management research refers to research on the people that make up organizations. Atypical employee life cycle is illustrated in Figure 1 below. An employee journey begins whenthey are recruited and
, Attitudes, and Perceptions of Oral Engineering ExamsIntroductionWhile it is commonly known that verbal communication and presentation skills are highlydesirable by employers, many engineering students’ technical learning is assessed primarilythrough written examination means. In the department of Integrated Engineering (IE) atMinnesota State University, Mankato, verbal exams are a fundamental formative and summativeassessment method of checking students’ understanding [1]. While the goals for verbalexamination are common throughout the program, this paper aims to compile the individualphilosophies, approaches, attitudes, and perceptions of faculty within the department who giveverbal examinations on a regular basis
last century to communicate information abouta student’s learning [1]. However, the widespread introduction of the A-F grading scale led tounnecessary stress and harm to students [2], questions about the accuracy and validity of thegrading system [3], and a sudden shift from learning to earning enough points to get a desiredgrade [4]. Furthermore, traditional grading schemes can further amplify the effects of biases andpolicies that negatively impact students from underserved groups [5]. Despite these concerns, theA-F grading system is still largely used in educational institutions today [1], and the question stillremains: Is there a way to communicate student learning without leading to unnecessary harmand a lack of intrinsic motivation? To
only deepens understandingbut also enhances innovation as students learn to navigate and manipulate the interfacebetween digital and tangible realms. The workshop at ASEE 2023 [1] was crafted with these imperatives in mind, aiming to bridgethe gap between theoretical constructs and their real-world applications. It focused on PBL, aneducational approach that fosters critical thinking, problem-solving, and collaboration throughcomplex and challenging projects that reflect the ambiguity of real-life scenarios. Hands-onactivities were not merely ancillary; they were central to the learning process, ensuring thatparticipants could apply theoretical principles in a tangible setting, reinforcing their learningthrough direct experience. The
perceptions of the ethicaldimensions of using LLMs, the individual results of each survey were not linked. The pilot studywill lead to a more comprehensive study of student attitudes and use of LLMs.IntroductionIdeas about artificial intelligence (AI) have moved from theory to reality over the last century.Some credit science fiction author Isaac Asimov [1] with establishing the discipline via the ThreeLaws of Robotics in his 1942 short story “Runaround.” Others consider the birth of AI to be at a1956 conference at Dartmouth College [2]. Since then, AI development has surged off and on.The recent public release of free large language models (LLMs) such as ChatGPT hasaccelerated both dialogue about AI and use of it for a wide variety of tasks. Even
career, however, many lower-divisionprograms exclude hands-on projects, and are solely based on basic sciences courses such as mathand physics. Within the first and second years of engineering curricula, many programs report ahigher attrition rate [1-2] and a drop due to a variety of factors including difficultiesunderstanding concepts, classroom climate, and a lack of interest [1-3]. Experiential learningcourses offered during the lower-division years of an engineering program is one proposedsolution to increase retention.Experiential learning has demonstrated many proposed benefits such as increasing studentmotivation, allowing students to gain fundamental technical skills, and improving students’teammate and collaborative skills [4-7
academicpreparedness and performance [1]. Soria and Horgas [2] found that, post pandemic, 39% ofcollege students are experiencing clinically significant anxiety and 35% of students areexperiencing clinically significant depression. At this time, post-pandemic, the ramifications toengineering education are still being discerned.This work attempts to begin to understand in what ways engineering faculty perceive theirteaching to have changed and to what extent course policies have evolved post-pandemic. Inparticular, given rising mental health concerns, have faculty instituted pedagogies and policiesthat are more compassionate? Compassionate pedagogy has been put forth as an approach foraddressing the challenges of the pandemic with practices such as providing
as a collaborative tool in a rapidly evolving academic environment.IntroductionThe field of engineering education is witnessing a transformative era with the advent of user-friendly Artificial Intelligence (AI) tools [1] [2] [3]. The introduction of this shift into mechanicalengineering and material science disciplines could significantly enhance traditional instructionalmethods, leveraging the vast capabilities of AI [4] [5]. The integration of AI tools like GPT-4 andDALL·E 3 represents a pioneering effort in this direction [1]. This paper aims to explore anddocument the impact of AI on the pedagogical landscape of material science education.At the core, this study investigates how AI tools can aid in the creation of student assessments
Paper ID #41900Beyond Exhibits: Exploring Bio-Inspired Education Robots in Museums forSTEM EnrichmentDr. Lydia Ross, Arizona State University Lydia Ross (she/her) is an assistant professor for the Division of Educational Leadership in the Mary Lou Fulton Teachers College at Arizona State University. Her research broadly centers on issues of equity, access, and inclusion in K-12 and post-secondary education, focusing on STEM. Specifically, she aims to understand 1) how students access educational systems and opportunities, 2) student experiences within educational systems, and 3) fostering professional development (PD
of Kentucky aims to fostercollaboration among students in engineering and computer sciences. As interdisciplinaryinteractions are limited as students move to upper-class courses, the project aims to develop andsustain collaboration between mechanical and chemical engineering lecturers for junior levelcourses. The enhanced presence of multidisciplinary collaboration could overcome the knowledgefragmentation of a specialized engineering curriculum and be a better representation of theworkplace.IntroductionEngineering programs are structured based on employability, the fourth industrial revolution, andsustainability. Students need to understand and solve complex problems based on context andtheir ability to connect multiple disciplines [1
contribute to advancing engineering andtechnology. This paper will report the details of the developed course, implementation steps, andstudent feedback.1 Introduction Brain-computer Interface (BCI) in its non-invasive form is a new science that detects patternsin the human brain’s signals and uses the result for different applications. The BCI was introducedto the research society by UCLA computer science professor Jacques Vidal in 1973 and hedescribed BCI as “any computer-based system that produces detailed information on brainfunction” [1]. BCI is also defined as “a system that measures central nervous system (CNS) activityand converts it into artificial output that replaces, restores, enhances, supplements, or improvesnatural CNS output and