behavior is higher when one (an agent) perceives that other peoplewould recognize his or her behavior with lower possibility. The following formulademonstrates the equation: CUB≈ 𝑓([𝑃(𝑃𝐷𝑥 )]) where: CUB: Conducting Unethical Behavior Formula 1 P: Possibility PD: Perceived Disclosure of behavior xTo further clarify the mentioned theory, imagine Dr. Jefferson2, a general practitioner, whoworks in the Ministry of Health Affairs. Since the beginning of the project he has beenengaged with the business analysts team in development of a Fraud Detection System (FDS)as a "business person" to clarify system
summary of thefrequency of all of the variables incorporated within the ASEE papers can be found within Graph2. Frequency of ASEE Papers Incorporating the Key Words product for disability cs curriculm learning disability technology disability participation disability as population design project children elderly k-12 students college age emotional mental disability physical disability 0 50 100 150 200 250 300 350 400 Graph 2: Frequency of ASEE Papers Incorporating Stated Key WordsDiscussion
interdisciplinary collaboration, design education, communication studies, identity theory and reflective practice. Projects supported by the National Science Foundation include exploring disciplines as cultures, liberatory maker spaces, and a RED grant to increase pathways in ECE for the professional formation of engineers.Prof. Thomas Martin, Virginia Tech Tom Martin is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, with courtesy appointments in Computer Science and the School of Architecture + Design. He is the co-director of the Virginia Tech E-textiles Lab and the associate director of the Institute for Creativity, Arts, and Technology. He received his Ph.D. in Electrical and
Paper ID #23301Peer Review and Reflection in Engineering Labs: Writing to Learn and Learn-ing to WriteDr. Vanessa Svihla, University of New Mexico Dr. Vanessa Svihla is a learning scientist and assistant professor at the University of New Mexico in the Organization, Information & Learning Sciences program, and in the Chemical & Biological Engineering Department. She served as Co-PI on an NSF RET Grant and a USDA NIFA grant, and is currently co-PI on three NSF-funded projects in engineering and computer science education, including a Revolutioniz- ing Engineering Departments project. She was selected as a
Electrical and Computer Engineeringat the University of Florida. Her research is centered on her advisor’s device and processsimulator, Dr. Mark Law Florida Object Oriented Device/Process Simulator (FLOOD/FLOOPS).She has three major projects of which the applications are radiation effects on AlGaN/GaN HighElectron Mobility Transistors (HEMTs), mechanical stress effects of Silicon based Hall magnetic © American Society for Engineering Education, 2018 2018 ASEE Southeastern Section Conferencesensors, and pH chemical/biological sensors on open-gated GaN-based HEMTs. Maddie plans tograduate in August 2018 and is pursuing postdoctoral opportunities.Jennifer S. CurtisJennifer Sinclair Curtis is a
(andincreasingly robotic) factories up and running (p. 24).” As evidence, the U.S. Department ofLabor [11] reported that construction and manufacturing had the highest ratio per vacancy, whencomparing technician skills gaps to vacancies. In Florida Jobs 2030, the Florida ChamberFoundation [12] reported that the greatest projected long-term skills gaps in manufacturing werein sales representatives and maintenance and repair workers. Employability skills such ascommunication, critical thinking, and problem solving were underscored as important, inaddition to developing productivity skills (e.g., word processing), occupation-specific skills (e.g.,AutoCAD), and advanced digital skills (networking and design). These skills were specificallymentioned for the
] featured in Figure 3. For the purposes of this project, theDOL competency model is considered canonical.Figure 3. Advanced Manufacturing competency model [9].As Figure 3 suggests, the Advanced Manufacturing Competency Model contains tiers of skills,knowledge, and abilities essential for successful performance in the industry. At the base of themodel, the competencies apply to a large number of industries. As a reader moves up the model,the competencies become industry and occupation specific. The DOL also makes detailed modelcompetencies available in text form [12], and we used that content to start our initial BOK. Ourstudy participants have reviewed this initial BOK and agreed that its content was a good foundationupon which to build with
developengineering learning experiences for their classrooms that are not exclusionary to traditionallyunderrepresented students.The current study works to add to the previously mentioned set of Draw-A-Teacher Tests bydeveloping a Draw-An-Engineering Teacher Test (DAETT) to identify teachers’ mental imagesof engineering teaching. Specifically, the study seeks to answer the following research questions:1. What mental images do participants hold of themselves teaching engineering at theelementary level?2. How do pre-service teachers’ mental images of teaching engineering change aftercompleting a semester long science methods course that includes engineering-focusedcomponents?This project is a work in progress and the current paper reports on the
for the student to elaborate on their survey responsesand learning experiences. The interview will be transcribed and holistically coded for a broadunderstanding of experiences.Work Cited[1] “NSF Science and Engineering Indicators: 2012 - Data.gov,” 2012.[2] National Science Foundation and National Center for Science and Engineering Statistics, “Women, Minorities, and Persons with Disabilities in Science and Engineering: 2017: (558442013-001).” American Psychological Association, 2017.[3] E. T. Pascarella and P. T. Terenzini, How college affects students: A third decade of research (Vol. 2). San Francisco: Jossey-Bass, 2005.[4] “Framework for Evaluating Impacts of Broadening Participation Projects,” 2009.[5] S. Freeman et al
refers to. For this reason, Sapir [26] described metonymy as “the logical inverse of metaphor… [with] two terms that occupy a common domain but do not share common features.”• Synecdoche is a specific type of metonymy, in which one part of an entity represents the entire entity [23]. For example, in the sentence “We need some more hands on the project,” hands refer to people.• Analogy is a broad category encompassing any figure of speech involving a comparison of domains [32], and therefore, metaphor is “a species” of analogy [3]. Readers of this paper who completed the SATs (Scholastic Assessment Test) in the U.S. prior to 2005 are likely familiar with the extended analogy form of one comparison juxtaposed with another
for twenty-five years. In 2002 he established Leaders of Tomorrow, a student leadership development pro- gram that led to the establishment of ILead in 2010. He is also a Professor in the Department of Chemical Engineering and Applied ChemistryDr. Robin Sacks, University of Toronto Robin is an Assistant Professor with the Institute for Leadership Education in Engineering at the Uni- versity of Toronto where she teaches leadership and positive psychology. She served as Director of the Engineering Leadership Project, which aims to understand how engineers lead in industry.Mr. Mike Klassen, University of Toronto Mike Klassen is the Assistant Director, Community of Practice on Engineering Leadership at the Institute
and the design process of undergraduate students in project-based courses. c American Society for Engineering Education, 2018 WIP: High-Achieving Students’ Perceptions of and Approaches to Problem Solving in Introductory Engineering Science CoursesThis work-in-progress paper is grounded in the understanding that undergraduate students’approach to solving assigned engineering problems – a component of their engineeringepistemology – influences the substance and quality of what they learn in the moment and in thefuture [1], [2]. Engineering students need meaningful strategies for approaching multiple types ofproblems in order to develop the knowledge and reasoning necessary for success in
consent forms, whichwere collected by a graduate student who was unaffiliated with the course and sequestered untilafter final grades were submitted. At that time, we found that 15 students had consented to allowtheir data (i.e., their course assignments) to be used for research. Our research project wasapproved by the local Institutional Review Board (IRB#17595).Data collectionWe collected both quantitative and qualitative data from the students. For quantitative data, weadministered a pre- and post-survey during the first and last week of the course, respectively.The survey had a total of 42 items. To measure mindsets, we included Dweck’s Implicit Theoryof Intelligence Scale (8 items) [9]. To measure goal orientations, we included the scales
that the wiring would be problematic and potentially delay lab workthe following weeks. However the concerns proved unfounded and the students were able tocomplete the wiring successfully in teams of four within the 2 hour lab time. To help the process,the instructor station was wired ahead of the lab and left as an example for the students.After the first week, the wiring was left in place and modified as needed in following labs. Inthose weeks the students were able to add sensors and actuators to satisfy weekly lab exercises.At the conclusion of the semester students were given more elaborate projects where they neededto connect their station to control a piece of equipment like a conveyor and can crusher.At the time of final submission the
agenda and conversation in the sessions thus far.Next StepsAs this is a work in progress, the next stage of the project involves the tracking of futuredevelopments of the program and individuals. Tracking how useful faculty participants rate theexperience over time and success measures such as self-correcting mechanisms, and reflectionon goal setting and goal achievement will be included in the feedback gathering to assess impact.References[1] R. Wilson, “Why are associate professors so unhappy?” The Chronicle of Higher Education,p. A3-A4, June 3, 2012 [Online]. Available: Chronicle,https://www.chronicle.com/article/Why-Are-Associate-Professors/132071. [Accessed August 9,2016].[2] A. Canale, C. Herdklotz, & L. Wild, “Mid-career faculty
Paper ID #22007Work in Progress: Institutional Context and the Implementation of the Red-shirt in Engineering Model at Six UniversitiesDr. Emily Knaphus-Soran, University of Washington Emily Knaphus-Soran is a Research Associate at the Center for Evaluation & Research for STEM Equity (CERSE) at the University of Washington. She works on the evaluation of several projects aimed at improving diversity, equity, and inclusion in STEM fields. She also conducts research on the social- psychological and institutional forces that contribute to the persistence of race and class inequalities in the United States. Emily earned a
Session CEED 442Cultural Behaviors• A person arrives to meet you 30 minutes after your scheduled meeting time.• A person throws a stone at a dog.• A student helps another student answer a question during a test. • A supervisor raises their voice in front of the team when you did not complete the project to their satisfaction.• A person cuts in front of you in a line. Proceedings of the 2018 Conference for Industry and Education Collaboration Copyright ©2018 American Society for Engineering Education
alternating semester hire, with repeated opportunities for theindustry to evaluate the particular student involved. It also provides the student an experience tothe particular facets of an industry, or multiple industries, if they have not yet decided on wherethey wish to begin their career.It is important both to the student and the industry involved that the internship provide “realworld” work, not drawing filing or other paperwork projects which do not apply to the programthe student is following. That is not to say that the student must be given original design work tocomplete, but rather some small segment of design, drawing modification, subroutine algorithmdevelopment, and so forth. The effort must be applicable, but able to be completed in
butrequires some study and practice to acquire proficiency.There are three vector operations which we will find useful in our study. One operation, calledthe dot product, is written as 𝐀⃗ ⃘⃗𝐁 and yields a scalar (that is, a number and not a vector). Thenext operation, called the cross product, is written as 𝐀 ⃗ × ⃗𝐁 and yields a vector which isperpendicular to the plane spanned by the vectors 𝐀 ⃗ and ⃗𝐁 and the third operation produces ascalar and is called the triple scalar product, and denoted by 𝐀 ⃗ × ⃗𝐁 ⃘𝐂.The dot product provides a means to compute the length of the projection of a line segment onto ⃗ onto thesome intersecting line. In the ordinary
analytic designs that are tailored to the unique needs of each program context. She has published in scholarly and practitioner-focused jour- nals on topics including evaluation design, instrument validation, and the effectiveness of policy change. After graduating from the University of North Carolina at Chapel Hill with a B.S. in Psychology Adrienne completed a Masters of Education in Curriculum and Instruction at UNC Greensboro. She taught third grade before returning to UNC Chapel Hill to complete a PhD in Education. In addition to her evaluation work Adrienne has worked on multiple research projects, taught doctoral- level research methods and statistic courses, and mentored undergraduate and graduate students.Dr
Research (CLUSTER). In her research, she is interested in understanding how engineering students develop their professional identity, the role of emo- tion in student learning, and synergistic learning. A recent research project uncovers the narratives of exemplary engineering faculty who have successfully transitioned to student-centered teaching strategies. She co-designed the environmental engineering synthesis and design studios and the design spine for the mechanical engineering program at UGA. She is engaged in mentoring early career faculty at her univer- sity and within the PEER National Collaborative. In 2013 she was selected to be a National Academy of Engineering Frontiers of Engineering Education Faculty
NSF INCLUDES: Enhancing STEM through Diversity and Inclusion 5 Convergence Accelerator Accelerating Discovery through Convergence Research time-limited “tracks”: accelerating impactful convergence research in areas of national importance separate from directorates in leadership, budget, and programmatics (but relying on, and building on foundational disciplinary research, including Big Ideas) projects with clear goals, milestones, directed deliverables (e.g., 6-months) more intentional, more directed management; mission- HDR
Paper ID #281012018 Best PIC II Paper: Systems Engineering Division: Development of aSurvey Instrument to Evaluate Student Systems Engineering AbilityMrs. Diane Constance Aloisio, Indiana-Purdue University Diane Aloisio is a PhD candidate in the School of Aeronautics and Astronautics at Purdue University. Her research concentrates on taking a systems approach to finding the common causes of systems engineering accidents and project failures. Diane received a dual BS degree in Mechanical and Aerospace Engineering from University at Buffalo in New York. c American Society for Engineering Education
solve problems together, fostering peer instruction, which has been shown to be moreeffective in student success than traditional lecture-based styles. The arrangement also allows theprofessor to easily move among the students as they work on solving problems. This physicalarrangement allows more one-on-one instructor interaction, providing for more personalizationof the learning process. - Projection screen - Marker Board - Lectern Figure 1. Classroom LayoutThe second
Paper ID #30590Increased Performance via Supplemental Instruction and Technology inTechnical ComputingDr. Nathan L Anderson, California State University, Chico Dr. Nathan L. Anderson is an Assistant Professor in the Department of Mechanical and Mechatronic Engineering and Sustainable Manufacturing at California State University Chico. He engages in multiple research projects spanning computational materials science to educational pedagogy. Prior to joining academia, he worked in the semiconductor manufacturing industry for KLA Corporation. Before industry, he spent time at Sandia National Laboratories. He earned his Ph.D. in
the business librarian to provide technical,intellectual property and business information in support of a major competition.Similarly, the University of Utah has integrated their three libraries into the innovation space.They were involved in the concept, prototyping, product, and commercialization stages ofinnovation, employing medical, business, patent, and innovation librarians to provide innovatorswith information resources at all of these stages [2]. Different university libraries have alsosought to embed themselves with the Technology Transfer Office. The University of Arizonasaw its opportunity during a time of reorganization and formalized the relationship by havinglibrarians work on individual projects involving literature search
, and evidence to support those codes for allparticipants. This enabled us to better observe patterns in our data and also to calculatepercentages (e.g., the percentage of participants whose design failed). These percentages aremeant to help us describe our particular sample and we do not mean to generalize beyond this to,for example, reflect percentages of all kindergartners.Researcher RolesAs mentioned above, both authors contributed to data analysis. While the second author’s rolewas purely that of a researcher in this project, never having met the participants in person, thefirst author had an “active membership” role in the classroom community [21, 24]. Prior to theinterview, the first author spent about six hours in each classroom or with
givenapproximately three assignments throughout the semester that required them to sketchorthographic projections and isometric views of objects. These assignments were designed tohelp improve spatial visualization ability. However, the class was generally focused on 3Dmodeling skills and SolidWorks operation, and not on spatial visualization ability.A survey was also administered to assess self-efficacy and to ask the students about how helpfulthey found the different learning activities in the course. We measured self-efficacy regarding 3Dgraphics topics using the three-dimensional modeling self-efficacy scale described by Densenand Kelly [21]. We will refer to this scale as the 3DM-SES in this paper. Agreement on eachitem of the nine items of this survey
, particularly at the post-secondary level?If Dewey [21] is correct in asserting that all experience becomes through continuity andinteraction, then education must tend to these elements. How do educators choose strategically tobuild upon student experiences over time? How do educators assemble the right environment,comprising subjects and objects that, through interaction, lead to the greatest positive growth?Here, I am most interested in the second question. In agreement with Dewey, the significance ofthe experience is going to depend upon how well students are grounded in a shared “socialenterprise.” This is not the kind of enterprise that one should associate with business orentrepreneurialism. It is, rather, enterprise as a complex project or
purpose of this theory paper is to show engineering education researchers how they cancreatively leverage mixed methods in their research such that they can achieve moremethodologically comprehensive integration and transparency. This paper will be of interest toboth newcomers and veterans of using mixed methods research designs. In addition to presentingexemplars within mixed methods research designs, we offer additional strategies for researcherswho find it challenging to integrate mixed methods beyond the data collection and drawinginferences stages of a project. BackgroundMany methodologists have described mixed methods with slight variations in their formulations[9]. At its most fundamental