experiments. By the end of thesemester, students have integrated them together to create a working micro-grid.IntroductionDespite being routinely identified by the National Academy of Engineering as the greatest engineeringachievement of the 20th century [1], academic support for and student enrollment in power engineeringeducational programs has not kept pace with other sub-disciplines of electrical engineering. This hasresulted in a national need for well qualified power engineering and engineering technology graduates[2]-[4]. Additionally, the technical skills required of power engineers is constantly evolving and nowinclude emerging topics such as smart grids, renewable and alternative energy sources, advanced energyefficiency and demand side
retain promisingstudents through graduation.1 Therefore, research exploring how students develop commitmentto engineering is of particular interest. This issue is especially important when considering thewidespread goal of increasing the diversity of engineering and promoting the success of diversestudent populations in this field. To better understand career commitment, this research paper explores the relationshipbetween students’ occupational values and their perceptions of engineering as a career field andhow this relationship impacts major commitment. Research has documented substantial sexdifferences in occupational values and interests, where women are more likely to prefercommunal or helping occupations while men are more likely
features, we use feature vector with low dimension. We employ SupportVector Machine (SVM) for the classifier with the gait-based feature vector. The extracted featuredataset are divided into two parts, i.e., training and testing datasets. The training data set areused for training a SVM classifier while the testing dataset are used for the evaluation.According to the experimental results, we know that GEI is an applicable feature for human gaitrepresentation. Despite of the limitation of the dataset, e.g., different races and thickness ofclothes which weaken the distinct differences between males and females, the average accuracyof the proposed approach reaches up to 87% under 10 times holdout validation.1. IntroductionGender classification plays
)professionals is recognized as paramount in the United States. STEM fields currently impact themajority of activities that comprise modern life. The demand for more and better trained STEMprofessionals continues to increase without a clear boundary. To fully participate in today’ssociety, all students, regardless of race, gender or economic status, require a strongunderstanding of the STEM fields.1 Yet, it is well recognized that there exists an achievementgap in STEM between minority and majority student populations. Underrepresented groups orgroups that have been traditionally underserved in STEM, comprise 26% of the general USpopulation but only account for 10% of the science and engineering workforce.2 This disparity isa social justice issue, as
mechatronics engineering throughthis hands-on project as an assessment of the design project presented.I. IntroductionA ball-and-beam system is one of the challenging control bench-marking systems integrated intomany practices and techniques [1]. This project will resolve in taking the ball-and-beam conceptand develop a ball-and-plate balancing system. The system will utilize sensors, actuators, andcontrol law to manipulate the servos in a feedback stabilization using three-degree-of-freedomcompensation. This is essentially implementing two ball-and-beam experiments in parallel toconstructing a ball-and-plate prototype.The concept of the ball-and-beam system is a simple system that is an unstable open-loop.Without an active feedback control system
semester.IntroductionThe cost of procrastination is often not quantifiable. However, analysis of two assignments froman introduction to engineering course at Texas Tech University produced a relationship betweenassignment grade and submission time as a function of time between the start and submittaldates. The relationships discovered in the Fall 2013 semester clearly illustrate the adverseeffects of procrastination on student performance. The data used herein comes from 4 of 13sections of an undergraduate ENGR 1315 - Introduction to Engineering course offered in the Fallof 2013, 2 of 13 from Spring 2014 and 4 of 13 from Fall 2014, at Texas Tech University. Thethree-hour course lesson meetings are on Tuesdays and Thursdays for 1 hour and 20 minutes,with 28 course
operational procedure and base the assessment on an explanation of what onewould expect to happen if the experiment were performed or why the apparatus acts the way itdoes. For simplicity we only show 5 learning outcomes that are operationally based and brieflydiscuss the assessment of the first two. Many of the assessments used in this work are taken oradapted from Ref 1. The student will: (a) develop operational definitions of electrical charge; (b) explain the evidence for the existence of only two types of charge; (c) determine if a material is a conductor, a dielectric, or a photoconductor; (d) apply Coulomb’s law to systems of charged objects; (e) identify charge transfer mechanisms;...Examples of the
), NewMexico State University, Prairie View A&M University, and Macomb Community College. Thework focused on four related knowledge areas: (1) drafting and design, (2) manufacturingprocesses, (3) process engineering, and (4) CAD/CAM/CIM. Each institution had specific program objectives and therefore the number andsequencing of courses required to cover the material varied. To make the work independent ofthe institutions, course-level student learning outcomes in the four knowledge areas wereidentified. A curriculum writing process was undertaken which narrowed these down to acommon core meeting the needs of all participating institutions. Relevant courses at each
studying new manufacturing processes and systems for a new discipline in GreenPlastics Manufacturing Technology (GPMT).1-4The primary goal of the NSF project (DUE-1044794) was to transform the exiting materials andmanufacturing curriculum to keep pace with advanced green technologies in the manufacturingand mechanical engineering technology programs (MMET) at Rochester Institute of Technology(RIT). We developed new educational approach and undergraduate teaching modules to promoteSTEM practice for Green Plastics Manufacturing Technology (GPMT) within foundationalcourses in materials and manufacturing education for the MMET programs.The GPMT approaches, which were based on the findings and results in the evidence-basedpedagogy, were applied to
) is one of the pathways to achieve the STEM endorsement (next to Mathematics, Scienceand Computer Science). The goal for House Bill 5 is to provide students with earlier exposure toa coherent course sequence and to increase preparedness and sustain interest in STEM careers.Given the increase in messaging on the value of STEM, we don't know how well the message isacted upon by high school students and as we barely understand students’ choices before theendorsement requirement, we need to set a baseline. Therefore, this study attempts to set out thebaseline through analyses of trends in several years of CTE-STEM course enrollment in TX priorto House Bill 5.We chose to focus on the CTE-STEM pathway out of two reasons: (1) The CTE-STEM
, and the project is either assigned or students may have several projects to selectfrom. Capstone courses are also widely used for the assessment of Student Outcomes (SOs) due 1to the wealth of information one can collect. Many of the capstone projects may involve real-world problems and multidisciplinary teams. While multidisciplinary projects are easy to achievein some areas of engineering, it has been a challenge for civil engineering projects. Evenworking on a project involving multiple concentration areas within civil engineering is achallenge due to the way courses are offered at many universities [1 – 9].Students in our civil engineering
presents a robust and explainable alternative for muscle segmentation in clinical andresearch applications.IntroductionMagnetic resonance imaging (MRI) is an essential tool in medical diagnostics due to itsnon-invasive and whole-body imaging capabilities. However, the development of techniques toefficiently, and accurately segment individual muscles remains limited. Current methods aremainly based on 2D [1] and 3D [2] convolutional neural networks (CNNs) [3], which requireextensive annotated datasets and significant computational resources. Furthermore, theseapproaches often struggle with generalizability and underperform in segmenting smaller muscles,with Dice similarity coefficients (DSCs) [4] ranging from 0.60 to 0.80 [2]. Achieving
studies [1, 2] have examined the impact of academic accommodations on theacademic success of STEM students, revealing a rapid increase in the number of students withdisabilities attending post-secondary institutions in recent years. Educational equity is key tomeeting students’ needs while having access to all the resources, so one may effectively reachtheir full potential. One study [4] investigated the experiences of STEM students withdisabilities at a large research university in the United States. This study found that studentswho received academic accommodations, such as extended time on exams and access toassistive technology, were more likely to persist in their studies and achieve academic successthan those who did not receive
collected over the previous three years from several disparatesources to identify opportunities for program improvement. By examining the data through anaccess and opportunity lens, the committee sought to uncover persistent issues that had beenoverlooked because they never ranked as immediate priorities. Input from undergraduate andgraduate TAs, undergraduate and graduate student ambassadors, and surveys from multipleundergraduate cohorts revealed a need for resources to help faculty support TAs in their coursesand for TA training. At this time, neither the college nor the department offered such training.In Fall 2022, we prioritized two key goals that could be readily addressed: 1) reducing TAfrustration stemming from insufficient training, lack
academic performance (dependentvariables), we performed an analysis of covariance (ANCOVA) on the experimental data,controlling for student attributes. We found that the attendance and interaction between SAT-Math and attendance were the two terms that influenced the homework scores the most.IntroductionIn courses in science and engineering, ancillary learning opportunities outside the classroom cantake different forms. Currently, two popular ancillary approaches are peer-led team learning(PLTL) and supplemental instruction (SI). In a PLTL session, students solve problems as agroup that is led by a peer leader who is not normally a subject matter expert (SME), whereas anSI session is led by an SME.1 In both types of sessions, students learn how to
is weak, they struggle to relate to new concepts taught in theclassrooms. This is a progressive process as the new concepts they learn one day might be thepre-requisite for a later concept in the same course or later in a higher-level course. In order tounderstand this, the following research questions are investigated. (1) Do pre-requisite concepts (from a pre-requisite course) play any role in a student’s understanding of a new concept? (2) Within the same course, how well do our students make connections between the related concepts? (3) To what extent can students learn a higher-level engineering concept without a proper understanding of mathematical concepts (both basic and advanced)? (4) How well can our
less effective at differentiating student performance. In contrast, manually createdquizzes offer greater depth, better alignment with course objectives, and foster critical thinking,though they require more effort to design. These findings offer evidence-based insights into thestrengths and limitations of AI in educational assessment. To address these challenges, wepropose strategies for leveraging AI-generated quizzes more effectively, such as incorporatingtargeted prompts and interactive workflows. Overall, this paper provides valuable insights andpractical recommendations to enhance the alignment of AI tools with educational goals andimprove the efficiency of quiz creation.1 IntroductionQuizzes and assessments are fundamental in higher
thanprevious methods. [1] This paper describes the design of the infrastructure and discusses thereasoning behind the decisions made in the process of to create a game-based learning system forinformation security topics. This paper also discusses the sophisticated virtual networking andsecurity implementation required to tie all the parts of the gaming system together.1. IntroductionThe best way to teach the concepts of information security requires utilizing game-basedcompetition. Game-based competition is used to motivate learners to stay engaged on a task for aperiod of time. [1] A game-based approach for teaching information security topics requiredmany operating systems, such as Windows desktops, Windows Server and Linux operatingsystems, and
year of AMIA (a work in progress) and details thebackground and motivation of the academy. Goals, educational components, community partnersand process implemented in year one of the AMIA is discussed. Interim assessment results,success, and lessons learned based on feedback of the participants is covered. Conclusions andnext steps for AMIA year 2016 are discussed. This is work in progress paper and authors plan tofollow up with detailed assessment results in year two of this academy.Background and MotivationFunded through a $1.25 million 3 year grant 1, 2,4, the first phase of the AMIA brought togethercommunity comprising of middle school students and teachers, technology and engineeringstudents, and university professors and administrators
help solve problems. Laboratory experiences have practically always been used bymechanical engineering educators to instill those fundamentals in students;1-3 and it is,presumably, in the laboratory that undergraduate students learn to fill in for themselves the gapsbetween theory and practice. However, a common problem in the undergraduate laboratory isill- or under-defined learning objectives, which often lead to deficiencies in studentperformance.4 Such a problem existed in mechanical engineering at the Mercer UniversitySchool of Engineering. The overall goal of this paper is to examine the initial results ofcurriculum changes that were made in mechanical engineering to better align learning objectiveswith student performance.BackgroundThe
properties could be determined using testing procedures described inthe American Society for Testing and Materials (ASTM) standards. One such standard isdesignated D-638-14, titled “Standard Test Method for Tensile Properties of Plastics,” whichstates that “test specimens shall be prepared by machining operations, or die cutting, frommaterials in sheet, plate, slab, or similar form. Specimens can also be prepared by molding thematerial to be tested.” Missing from the list of test specimen preparation methods are 3D-pritingtechniques. In this study, students prepared test specimens of Acrylonitrile-Butadiene-Styrene(ABS) plastic material by 3D-printing according to ASTM D638 Type 1 specimenspecifications. These test specimens were compared to
of a perceived social elementinclusion, (b) changes in learning from the perspective of the reviewer rather than the receiver offeedback, and (c) improvement in perceived information literacy. Additionally, this researchexamines Canvas attributes as identified by Sondergaard & Mulder(1) (2012) of (a) Automation,(b) Simplicity, (c) Customizability, and (d) Accessibility, which support statements from theliterature that indicate a lack of investigation of more modern peer review tools. Survey results,both qualitative and quantitative, were analyzed across three different peer-reviewed assignmentsfor this examination. Of the 91 respondents, representing a 32% response rate, descriptiveanalysis revealed themes ranging from Changes in Student
Meet and greet students. Introduction 1 Class introduction of students and instructor Introduction to Hydraulics and Pneumatics. Define the terms fluid power, (Lab) hydraulic System and Pneumatics.. Explain the functions of fluid power Different types of fluid power systems. systems. (Lab) Identify the basic structure of fluid power systems. Identify and explain the design and
map ABET student outcomes and supervisor surveymetrics. The correlation between the outcomes and expectations of employers will be determinedand gaged based on the data collected over a span of few years.IntroductionThis Engineering Department offers three different internships through ENGR 4900 (3 credits),ENGR 4901 (1 credit), and ENGR 4902 (2 credits) Engineering Practice courses. A student hasto take either ENGR 4900 or a combination of ENGR 4901 and 4902. This is not a very commonoccurrence, usually internship is an optional element of the curriculum1,2,3. Student’s choice isusually determined by the flat tuition threshold of 18 credits per semester. For example, if thestudent has 1 credit space in their schedule or registered for 17
toconduct research while integrating theory, knowledge and skills to develop a solution to adefined problem.1 Engineering instruction integrates well into problem based learning, allowingstudents real world problem solving experience in a classroom setting. It has been utilized inmaterials courses to examine material strengths and in mechanical engineering courses toexamine system behavior and fluid dynamics.2,3 It has been utilized in chemistry instrumentationlaboratories built around medical case analysis of drug analysis and quality controls inbreweries.4 With its increasing use, students have benefit from the engaging scenarios, wherelearning gains have been found to be twice that of a traditional classroom setting.5 In addition to problem
identifiedimpactful outreach approaches, including connecting with student organizations to more directlyreach underrepresented populations, create programming, and build relationships. Findings alsoallowed for the development of system-wide learning materials and interventions optimized toreach this student group.Introduction Libraries are essential for student success, contributing to both academic achievementand feelings of belonging on campus – key factors in retention and post-graduate outcomes.Despite longstanding efforts to increase diversity in STEM fields, computer science andengineering programs contain proportionally fewer women than other STEM fields, both inengineering programs [1] and in professional roles [2]. Researchers sought
AbstractSmart manufacturing technologies improve the productivity, efficiency, and competitiveness forU.S. industries. Key enabling technologies in smart manufacturing are to 1) acquire real-timeheterogeneous data from IoTs, sensors, and machines tools, and 2) make decisions from the datausing analytics. This Maker project discusses the development of a prototype Application softwarefor a 3D printer based on MTConnect protocol. This Application is able to collect, visualize, andstore data from additive manufacturing processes. This project aims to train students about 1)MTConnnect on Adapter, Agent, and Application development, 2) additive manufacturing, 3)database, and 5) communication protocols, for manufacturing operations. The results
a structured process that typically follows a series of well-defined steps to achieve optimal solutions for engineering problems.[1], [2] Thecommon steps in mechanical design include identifying the problem, establishingdesign requirements, generating concepts, analyzing and selecting the mostpromising concept, creating detailed designs, and finally prototyping and testing.Each step builds upon the previous one, ensuring that the final product meets thefunctional, economic, and safety requirements. Effective mechanical design ofteninvolves iterative refinement, where feedback from analysis and testing loopsback to earlier stages to improve the design further.The advent of AI tools like ChatGPT has introduced both opportunities andchallenges
Sheridan is a Research Scientist in the Brinson Advanced Materials Laboratory at the Duke University Department of Mechanical Engineering and Materials Science. His current research interest include optimal experimental design, uncertainty quantification, and AI-augmented laboratory techniques, especially in the context of AFM nanomechanics and viscoelasticity.Prof. Junhong Chen, University of Chicago Junhong Chen is currently Crown Family Professor of Pritzker School of Molecular Engineering at the University of Chicago and Lead Water Strategist & Senior Scientist at Argonne National Laboratory. He also serves as the Science Leader for Argonne’s presence in the City of Chicago (Argonne in Chicago). Since March 1
—including faculty and students—can experience a fairer and more empoweringenvironment. Scrum encourages self-management, accountability, and continuous improvement.I. IntroductionIn academia, hierarchical structures often create rigid dynamics, where senior tenured facultyexert significant control over junior, non-tenured members and students [1, 2]. This top-downapproach can stifle the growth and collaboration of junior faculty and students. Scrum, an Agileapproach designed for flexibility and self-organization, contrasts sharply with this rigidity. Byimplementing Scrum, academic teams—comprising junior faculty, senior faculty, and studentscan experience a fairer and more empowering environment. Scrum encourages self-management,accountability, and