: Containing Design: Rethinking Design Instruction to Support Engineering Device Development for Low-Income CountriesAbstractWork-in-Progress: One of the primary benefits of a makerspace is the concentration of tools,materials, and expertise in one place [1]. Without makerspaces, design education in many low- tomiddle-income countries (LMIC) stops with a “paper” design and does not move onto a physicalprototype. More than 75% of registered makerspaces are in North America and Europe [2], andless than 4% of registered makerspaces are in Africa [3].As part of a joint project between Duke University (NC, USA) and Makerere University(Kampala, Uganda), “twin” makerspaces were built at the respective universities. At Makerere,this makerspace was a first
programming concepts, database design and implementation,graphical user interface design, and web application development. Students complete three im-mersive simulation-based learning (ISBL) modules as course assignments. ISBL modules involvetechnology-enhanced problem-based learning where the problem context is represented via a three-dimensional (3D), animated discrete-event simulation model that resembles a real-world system orcontext, in this case, we have three simulated systems/contexts around which ISBL assignments aredefined: an airport, a manufacturing system, and a hospital emergency department. The researchexperiments involve four groups: (1) students with no choice who use the same assigned simulatedsystem for all three ISBL assignments
the drone body and aprocedure for embedding the electric wiring was developed. This integration required severaldesign modifications, which were implemented and prototyped. We believe that this modulardrone development project design and mentorship guided by the principles of experientiallearning and empowered by AM has increased the efficacy of students and helped them developseveral skills that are valuable to the future engineering work force including team skills,leadership, time-management, life-long and interdisciplinary learning, and entrepreneurshipmindset. Through a survey and focus group approach, the findings of an independent evaluatorconfirm those benefits to the students participating in the project.1. IntroductionAdditive
to the advancement in the digital era tohave mediums such as Python (open-source programming language) and Jupyter (Integrateddevelopment environment - IDE). Jupyter is a combination of open-source programminglanguages: Julia (Ju), Python, (Py), and R [1]. Although Jupyter notebooks are heavily used inmachine learning and data science, some explored their power in modeling and teaching otherfields of study [2]. Jupyter is an interactive web tool that can accommodate differentprogramming code types, computational output, markdown text, and LaTex in a notebook form.It also allows adding figures and videos around the used code which is another interactive keyfeature. These features make Jupyter notebook unique with significant potential as a
of students felt that their projects were successfuland mentioned that they had learned while working with their peers. The students were mostsatisfied with the projects when they met their own project goals. Even with limited data fromone semester as well things to improve, the overall reflections on active learning experienceunder minimal instructor involvement was encouraging, which will lead us to conduct further in-depth research in the following upper-division engineering courses.Introduction Self-learning (or active-learning) is an essential skillset for lifelong learning and personalgrowth, as well as a recognition of taking control of one’s education and professionaldevelopment [1-3]. To promote such a learning environment
packed with 6 mm ceramic Intalox saddles. A simplified schematic of the small-scalecolumn is shown in Figure 1. Liquid Inlet Gas Outlet 12” Liquid Overflow Liquid Distributor 8” NaOH & Water Air & CO2 Stream Stream 16” Packing
increasingly more minority students are enrolling in college, the number of collegegraduates with STEM degrees is still not favorable to minority students such as Latinx [1]. Inparticular, Latinas enrolled in STEM programs continue to experience hostile environments inmen-dominated spaces such as Engineering, even in Hispanic-Serving Institutions (HSIs) [2, 3].HSIs are all degree-granting higher education institutions with 25% or more full-time Latinxundergraduate students [4]. Institutional strategies, financial assistance, faculty representation,mentorship opportunities, and culturally responsive research opportunities are all critical inretaining and graduating students, especially minority and underrepresented students [5]. We aimat increasing
DesignAbstractOne approach to look at student learning is to identify “threshold concepts.” These are conceptsthat, once grasped, allow students to engage with the material in a fundamentally different way.First described by Meyer and Land [1], these concepts are transformative, irreversible,integrative, and troublesome. The process of mastering a threshold concept (TC) meanstraversing a liminal space during which the student is changed. Looking inward at our owncapstone program, we identified three candidate TCs: (1) Complex engineering problems arebest solved by teams working together. (2) A team can learn a lot from a prototype, even(especially?) when it doesn’t work. (3) The goal isn’t to find the right answer, but to learn aprocess by which a
Programs led by Dr. Barr is the Director of Assessment and Evaluation of STEM Programs at Rice University. He has been an evaluator and psychometric expert on several federally funded projects in ©American Society for Engineering Education, 2023 Design and Testing of a Quantitative Instrument to Evaluate Engineering Research Center ParticipationIntroductionThe National Science Foundation’s (NSF) Engineering Research Center (ERC) program aims toimpact society by developing research and innovation in universities across the country [1].Awards granted by this program are the highest-funded, single award from the NSF; a total of 75Research Centers have been funded since the program’s
studentsstruggled, and this might have impacted the learning outcomes; some institutions offered passinggrade owing to the sudden change in mode. However, the adaption to the new mode generatednew approaches of the instruction. In the fall 2020, classes ran mostly in two different formats:online which could be synchronous or asynchronous, and hybrid which was a combination ofonline class and in person examinations. The modes helped create electronic resources, e-resources, for the classes. This shift in mode had prompted to new learning tools. The primaryfocus of the study was to explore the impact of the e-resources on the performance of thestudents. An average grade is used as an indicator for performance evaluation which is similar tothe one in [1
EducationResearch through Collaborative Secondary Data Analysis” [1].The Mini-ProjectsSecondary Data Analysis as a Mechanism for New Insights and Future Researcher PreparationThe first project aims to explore the potential of SDA for training of newer researchers to thefield. The data originator is an experienced researcher with a large dataset resulting from acompleted NSF funded project. While the original project had delivered on its goals, there wasscope for further analysis of the interviews that had been conducted with undergraduateengineering students. Our project involves a researcher from another undergraduate-focusedinstitution, who wanted her undergraduate researchers to get experience doing qualitativeresearch on an already existing dataset
projectIntroduction and Literature ReviewThe Engineering Grand Challenges [1] represent the fourteen most important engineeringproblems to be addressed in the 21st century. These multidisciplinary challenges include makingsolar energy economical, restoring and improving urban infrastructure, providing access to cleanwater, and developing carbon sequestration methods. To address these design challenges,engineers in multidisciplinary teams must be able to communicate and justify their designseffectively for their work to be valued and implemented by stakeholders. Therefore, it is essentialthat graduating engineering students can work in interdisciplinary teams and communicateeffectively. However, as Berdanier [2] noted, communication skills are an undervalued
and comprehensive NLP techniques are widely available, we argue that more limitedmethods like text extraction can still provide advantages to those looking to implement theseNLP in their instruction.BackgroundNatural language processing refers to a range of computational techniques for the analysis ofnaturally developed human languages [1]. Early NLP methods utilized rule-based grammar anddictionary based frequency counts, effectively counting the number of times certain words orphrases appear in a given text. More modern methods utilize large pre-trained models [2] ortransformer-based architectures [3] to address variations in semantic meaning. While advances inmachine learning (ML) and neural networks (NN) have recently garnered significant
completion within six years in comparison with other races, Black students have thelowest completion rate (41 percent) and are more likely to discontinue enrollment or stop outthan to complete a college credential [1]. Over time, these trends have largely remained the samewhen comparing Black and Latinx collegiate students with other majority races and it has causedsome to ask the question: What are colleges and institutions doing to address this? One of theanswers researchers have given is putting an institutional emphasis on inclusive teaching. Research has illustrated that student academic and social success can be improvedthrough instructors creating inclusive classroom environments that facilitate a sense of belonging[2], [3]. Though
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
Level Statistical CourseAbstractConveying mathematical graduate-level courses online can be challenging. A graduate-levelcourse in applied statistical process control and experimental design has been offered since 2015.This course includes three main themes: (1) probability theory with discrete and continuousprobability distributions, (2) statistical tools for estimation, hypothesis testing, and control charts,and (3) 2k full and fractional experimental designs and analysis. After three years of offering thein-person class, the program moved to an online modality to reach more professional students. Allmaterials, modules, assignments, exams, and instructors remained the same between in-person andonline modalities. The study compares the
issues of equity, inclusion, and social justice. ©American Society for Engineering Education, 2023Defining Accountability Among Black and white Women AccomplicesMonica F. Cox, The Ohio State UniversityKristen R. Moore, University at BuffaloIntroductionOver the past years, we authors have been having conversations about what it means to be an accomplice,particularly what accompliceship means between Black and white women. In 2021, we theorized (usingBlack Feminist Epistemology) that accomplice behavior can be understood in terms of power anddialogue; accompliceships are characterized by sharing power and engaging in dialogue with oneanother[1]. This paper extends power and dialogue into the realm of accountability
academic and professionaljourney. Unfortunately, this decision is often plagued by uncertainty and indecision, leading to ahigher attrition rate among students who think they have made a definite choice. [1]Selecting an academic major is a complex process that is influenced by various factors such aspersonal interests, family and peer pressure, and access to reliable information. The informationavailable to students can be outdated, unreliable, or inaccessible to underrepresented groups,leading to ill-informed decisions. To address these challenges, we must understand engineeringstudents' information-seeking behaviors when making their major selection.This research paper aims to delve into the academic major selection process among
from our professional development, summarizing learning objectives, presentationcontent, and activities. Additionally, we present comments shared by instructors related to ourprofessional development, including common barriers to implementing educational innovationsin their courses. Our work will provide insights to practitioners interested in promoting inclusiveclassroom practices in engineering education and researchers who are translating research topractice, especially through professional development.Keywords: Faculty professional development; inclusive pedagogy; asset based practicesIntroductionDespite many years of effort to increase participation, engineering suffers from unequalparticipation based on race and gender [1], [2] and
plant, called a Chemical Engineering Education Reactor (CEER).Students learn well in a combination of lecture and discovery-based methods, where lectureprovides base knowledge of the field and discovery methods encourage critical thinking andsubject matter integration [1]. Incorporating active learning, like discovery methods, have beproven to improve concept tests more than any other form of instruction [2], encouraging furtherimplementation of active learning. CEER will seek to take advantage of active learning benefitsto teach individual concepts as well as how these concepts integrate. CEER is intended to enable experimental work on topics including heat and massbalances, heat and mass transfer, instrumentation and measurements
M3 model includes co-teaching and co-learning from facultyand students across different academic units/colleges, as well as learning experiences that spanmultiple semesters to foster student learning and innovative ideas. This collaborative initiative isdesigned to reach the broader campus community, regardless of students' backgrounds or majors.Therefore, the study presented in this paper explores how student participation in thistransdisciplinary learning model and their perceptions of their innovation skills may varyregarding major and gender. This exploration can be important as 1) the model may or may notbe meeting the needs of participants across areas of study and 2) perceptions of abilities mayinfluence a sense of belongingness for
and easily transferable to otherdomains. The research questions for this paper are: (1) what are the high-level technical andprofessional knowledge, skills, and abilities that students in a microelectronics workforcedevelopment program need to be certified? (2) What are the overall framework components forcertification, and what is the supporting literature? (3) What is a current example of theframework applied to professional skills for undergraduate students, and what are the next stepsfor technical skills? This paper includes detailed examples of the framework and supportingliterature for professional skills (i.e., teamwork, lifelong learning), and how technical skills (i.e.,circuits, quantum mechanics, quantum computing) are
in 2011 in Mechanical Engineering, focused primarily on automotive cont ©American Society for Engineering Education, 2023 Design Across the Curriculum: Improving Design Instruction in a Mechanical Engineering Program.IntroductionEngineering design is a critical learning outcome for a mechanical engineering curriculum.ABET requires that Mechanical Engineering programs demonstrate that graduating students have“an ability to apply engineering design to produce solutions that meet specified needs withconsideration of public health, safety, and welfare, as well as global, cultural, social,environmental, and economic factors.”[1] Design has also been identified as a curricular
computational thinking. Analysis of pilot data gatheredfrom five sections of a life science course in a northeastern U.S. urban high school during the2022-2023 academic academic year will inform the next iteration of the module.Background and MotivationThe thought processes associated with formulating problems and solutions such that they can beefficiently and effectively carried out by both machine (i.e., computer) and human is known ascomputational thinking (CT) [1]. While the construct of computational thinking originated incomputer science, CT practices like abstraction, pattern recognition, and modeling arerecognized to be incorporated in all science, technology, engineering, and math (STEM)disciplines [2], [3] and have revolutionized how
engaging and relevant. As such, it hasthe potential to modernize STEM curricula and advance the fundamental understanding at theintersection of technology and environment. 1. IntroductionGeophysical methods are useful in subsurface explorations as they are sensitive to contrast inphysical properties of soils over continuous coverage. Electrical Resistivity (ER) surveys havebeen used to investigate soils for over a century and rely on the fact that varying geologicconditions alter the distribution of electrical potential in the ground [1]. Based on this principle,ER methods have a wide array of practical and research applications related to Civil Engineeringsuch as geological and hydrogeological investigations of the subsoil (including testing
curriculum modules were implemented in the firstoffering of the course. Preliminary assessment results from the first offering of the course arediscussed.1 IntroductionSmart products can sense their environment, analyze lots of data (big data), and connect to theInternet and other smart products over a network to allow exchanging data. Today, there are manyconsumer smart products in our lives such as smart door locks, bike locks, smart kitchenappliances, irrigation controllers, smart thermostats (e.g. Nest), and Amazon Echo just to name afew. Such physical objects (things) connected to the Internet is called the Internet of Things (IoT)[1].Smart products are becoming ubiquitous and STEM workforce demands are shifting rapidly, butthe current
company they did not complete a co-op with.BackgroundCooperative work experience, also commonly referred to as co-op, is not a novel program foracademic and industry partners. Co-ops have been integral parts of engineering programs for thepast 100 years. The first formally documented cooperative program started at the University ofCincinnati in 1906 [1]. As the word implies, co-ops are a partnership between academia andindustry. Academia relies on industry for graduate employment and feedback for accreditationand industry requires students for future employees [2]. Today, a co-op is not just consideredsummer employment. Many programs allow students to participate in a co-op during a springand\or fall semester in addition to summer break. At York
or civil engineering departments. It usually covers analysis and control of free andforced vibrations that are often found in machines and buildings. To help students understandvibration phenomena in such complex dynamic systems, most vibration classes start with simple1DOF or 2DOF systems and associated analysis tools are developed as mathematicalmethodologies [1].Even though a simple mass-spring-damper system with 1DOF serves as a helpful starting pointfor students to learn about vibration phenomena, heavy use of mathematics sometimes makes itmore challenging for them to intuitively understand physical vibration motions. In addition,students have different experiences and backgrounds, so we cannot assume that all the studentscan easily
include adiscussion of retention and GPA patterns over the five years. Future work will involve the investigation ofstudent’s COOP experiences and its impact on the change in attitude toward their major and career goals.IntroductionThere is currently a dearth of skilled engineering graduates due to fewer students enrolling in andcontinuing in engineering schools, endangering the stability of the American economy andnational security [1], [2]. And also, an international issue of concern has been the retention ofengineering students. Significant research has been conducted to address this issue. Researcherslook at this topic from a variety of lenses, demonstrating its importance. US academicinstitutions are currently reporting engineering student
engineering courses. Research suggests amismatch between the skill demands of industry and the offerings of educational institutionsresulting in a skill gap [1-6]. As a major contributor to the United States economy and thesecond-largest labor sector with 8% of the total workforce Field [7]), the construction industry istaking a massive hit from this skill deficiency. Many scholarly publications and reports regardingemployability in the construction industry have reported employers’ concerns and dissatisfactionwith the low level of skills of their newly hired construction graduates [8-13]. As expressed bymany employers, one of the downstream implications of these skill gaps is project failure due todecreased work performance, productivity, and