found allthree cost subscales were significantly and negatively related with students’ intentions to persistin science, with the effort subscale having the strongest negative relationship with persistence.Informed by Perez et al.’s evidence of potential multidimensionality of the cost construct, Flakeet al.21 developed a new cost scale intended for broader use in an academic context. Similar tothe scale developed by Perez and colleagues, Flake et al.’s scale included task effort, loss ofvalued alternatives cost, and emotional cost. Flake et al. also suggested a new dimension, thecost of outside efforts, related to other demands on an individuals’ time and energy that mayincrease the cost associated with a particular task. Their preliminary
design.However, some educators have described an important empathic requisite or antecedent:designers must adopt a user-centric mindset. For example, Postma et al. discussed moving designstudents from an “expert” mindset, where the designer thinks they know best, to a “participatory”mindset, where the designer perceives their self and user(s) both as experts.48 Forming thismindset is important, as student designers who hold an expert mindset tend to exclude theirproject partner throughout a design process.49 Hence, educators ought to prompt students to thinkabout engineering with a user as opposed to for a user12,50 as this may catalyze the utilization ofempathy while simultaneously alleviating absolutist/positivistic biases.414.2 Service
intelligent tutoring systems and peer collaboration. In B. P. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Intelligent tutoring systems (pp. 636–645). Amsterdam, The Netherlands: IOS.[6] Menekse, M., Stump, G., Krause, S., & Chi, M. T. H. (2013). Differentiated overt learning activities for effective instruction in engineering classrooms. Journal of Engineering Education, 102, 346–374.[7] Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415.[8] Hora, M. T., & Ferrare, J. J. (2013
Civil Engineering Course," presented at the ASEE, St. Louis, Missouri, 2000.[10] O. Buzzi, S. Grimes, and A. Rolls, "Writing for the discipline in the discipline?," Teaching in Higher Education, vol. 17, pp. 479-484, 2012.[11] H. Drury, T. Langrish, and P. O Carroll, "Online approach to teaching report writing in chemical engineering: implementation and evaluation," International Journal of Engineering Education, vol. 22, p. 858, 2006.[12] F. S. Johnson, C. C. Sun, A. J. Marchese, H. L. Newell, J. L. Schmalzel, R. Harvey, et al., "Improving The Engineering And Writing Interface: An Assessment Of A Team Taught Integrated Course," presented at the ASEE, St. Louis, Missouri, 2000.[13] J. A. Leydens and J
Advances in Engineering Education FALL 2017Large Lecture Transformation: Improving StudentEngagement and Performance through In-class Practice in an Electrical Circuits CourseJAE-EUN RUSSELLANDMARK S. ANDERSLANDUniversity of IowaIowa City, IASAM VAN HORNEUniversity of DelawareNewark, DEJOHN GIKONYOANDLOGAN SLOANUniversity of IowaIowa City, IA ABSTRACT Post-secondary educators are increasingly experimenting with the possibility of blending orreplacing traditional lecture-based instruction with student-centered instruction. Although somestudies have been completed, much remains to be learned about when and why student-centeredinstruction
) included in the case studies presented in Section2. During the design of the tutorial, the complexity of Case 1 was intended to be lower than Case2’s. This was achieved with integrating relatively less comprehensive product familydevelopment assignment in the first case study. A brief reminder of the contents of the cases, Page 11.68.14Case 1 involves product family architecture from the functional and component perspectives.Case 2 includes market segment needs in product family architecture. However, in the actualDEA model, the numerical value of the technical complexity has to be entered in a positivecorrelation with the outputs (see the DEA
With oversight, guidance and assessment from the instructors, one teaching assistant wasdedicated to teach each of the 7 laboratories for the entire semester. Each TA was responsiblefor developing the laboratory procedures, administering a pre-laboratory quiz, providing the pre-laboratory instructions and monitoring the progress of the students during the laboratory. Theinstructors and the TA‟s all had office hours during the week to assist the students withquestions. All materials for all labs were available via a dedicated BlackBoard internet site, andstudents were able to use this online site to communicate with labmates, instructors and TA‟s,check their grades, and upload their assignments. All students within a section met as a class
double-pagespread of a children’s book (chosen by us), and convert it into pop-up form. The book we usedwas chosen for its charming text, its wonderful yet simple illustrations, and its pop-up potential.After a lively read aloud of the picture book, teachers were given envelopes at random thatcontained the original spread from the actual book, several photocopies of the pages, as well asthe form for the final page. They were to look first at the illustration(s) on their pages andbrainstorm possibilities for movement and pop-ups. We gave the following Engineering DesignChallenge to each teacher:• After receiving your page spread, brainstorm ideas of how you may want to set up your page. What do you want to move? How will the page be set up
Session Number : 3561 Linguistic Evidence of Cognitive Distr ibution: Quantifying Lear ning Among Under gr aduate Resear cher s in Engineer ing L. Donath, R. Spr ay, E. Alfor d T. McGar r y and N. Thompson Univer sity of South Car olinaAbstractThe Research Communication Studio at the University of South Carolina nurtures undergraduatelearning in engineering through guided interaction among student peers, near-peer graduatementors, and faculty members. The RCS bases its pedagogical approach on Dorothy Winsor’sconcept of thought and knowledge as a network distributed among members
they interested in?Much of our initial evaluation has been geared towards developing insight on these questions.4.1 High Quality Graduates - The measures of high quality are the metrics associated withOutcomes 1.1, 1.2, and 1.3. To date, we have had 24 graduates and their placements are listed inTable 1. Table 1 – Graduates of the Program # Focus/Complemenary Areas Grad Current Company Name or Eth. Date Graduate School Sex 1 Material Science / Anthropology F 98 UCAN/Privacy Rights Clearinghouse CA WF 2 GIS / General Business S 99
thisstudy [8]. These studies were examined and contributed to the student‟s overallassessment. Accompanying these developments in the Technical College Sector was therise of an Association for Liberal Education that had its own research officer [9].It will not have escaped the notice of the reader that no mention is made of universitystudents in technological studies receiving such treatment. Why should a person on adegree course be treated differently? The concept of liberal education has a long historythat can be traced back to the Greeks, and Davies provides an all too brief history of thedevelopment of liberal education from those times [8.ch 1]. From the Greeks and theRomans we get the notions of being free to learn, and as Davies notes
Professor in the Department of Engineering Mechanics at the U. S. Air Force Academy. He has published approximately 100 technical publications and generated approximately 2 million dollars of research finding. His current research interests include development of new design methodologies as well as methods for improving engineering education. Page 22.1350.1 c American Society for Engineering Education, 2011Studying Ideation in Engineering Design Education: Application to Highly Mobile RobotsIntroduction Developing innovative ideas as part of engineering design can be
partnered with The Henry Ford, both of which are located in the Detroitmetro area. As a result, Lawrence Tech‟s camp was focused on exploring creativity, innovation,and ingenuity as it relates to the American experience and manufacturing. In subsequentsummers, Boston University and St. Louis University will host summer enrichment opportunities Page 25.364.3in their respective metro areas. (Themes, details, and objectives for the Boston and St. Louiscamps had not been finalized by the time of publication of this paper.)2. Lawrence Tech Summer Enrichment ProgramThe Detroit metro area is well known as being the world‟s automotive industry capital and
furthest tocompletion, with electrical design being lowest and equal to plumbing. An unexpected result was how manytake their designs to a construction document (CD) level of completeness (Fig. 2b). This could be partiallydue to a team’s ability to go to that level of refinement, or perhaps certain key parts of a discipline’ssystem(s) are developed to that extent while other parts are not. For example, a team may design a singlestructural connection but not all of them in the building. To provide some literature context, most capstoneshave students target a level of completeness of their project somewhere between SD and DD [18-19]. a) covered within the capstone b) completeness of student work
their ability to learn the ma-terial, apply the material they have learned, and how well they believe they will perform in the Figure 5: User testing flow chart.learning activity. The full list of questions in the affective assessment is provided in Appendix B.The cognitive assessment consists of five multiple-choice questions focusing on technical aspectsof AFM imaging and identifying sources of common image artifacts.In the lab session, once it was confirmed that each student had completed the pre-lab, they wererandomly assigned to either the simulation cohort or the traditional paper (control) cohort. Stu-dents in the paper cohort did not have access to the simulation and were instead provided withimage(s
behaviors.The foundation of the MBTI lies in four fundamental dimensions, each represented by a pair ofopposing traits: • Extraversion (E) – Introversion (I): This dimension focuses on where individuals direct their attention and energy. Extraverts gain their energy from external sources and thrive on social interaction, while introverts find solace in their inner world and prefer reflection. • Sensing (S) – Intuition (N): This aspect relates to how individuals process information. Sensing types rely on concrete details and present realities, while intuitive types prioritize abstract concepts and future possibilities. • Thinking (T) – Feeling (F): This dimension highlights decision-making styles. Thinkers
Services at Utah State University. Her research centers the intersection identity formation, engineering culture, and disability studies. Her work has received several awards including best paper awards from the Journal of Engineering Education and the Australasian Journal of Engineering Education. She holds a Ph.D. in Engineering Education from Virginia Tech as well as M.S. and B.S. degrees in civil engineering from the South Dakota School of Mines and Technology.Dr. Bruk T Berhane, Florida International University Dr. Bruk T. Berhane received his bachelorˆa C™s degree in electrical engineering from the University of Maryland in 2003. He then completed a masterˆa C™s degree in engineering management at George
with the question, four in-context examples of answers, and the corresponding codes and instructed it to generate thecode(s) for the new answer instance. The in-context examples for GPT-4 prompt are drawn fromthe training split of the manually-coded dataset. We finetuned the Mixtral of Experts (MoE) [30]model using input and target pairs derived from the manually-coded training datasets. Thistrained model was then prompted with new test inputs, and the model-generated coded sequencewas evaluated against the manually coded target sequence. We evaluated both models on a testset of around 140 samples for each thermodynamics question. Using manual and languagemodel-based coding, we aim to answer two research questions: 1. What aspects of student
P O TENCIAL P O TENCIAL CURRENT P RO CES S P O TENCIAL FAILURE RP N ( S EV x O CC x RECO MMENDE EFFECTS O F S EV CAUS ES O F O CC P RO CES S DET ACTIO NS TAKENS TEP / FUNCTIO N MO DE DET) D ACTIO NS FAILURE FAILURE CO NTRO L
caring that includes both comfortwith faculty and empathetic faculty understanding from the same author.Discrimination (25 items)Discrimination is an active process that influences belonging in engineering (McGee, 2020). Toaccount for this potential, we adapted and included five items across five different identity-axes(race/ethnicity, gender, sexual orientation, (dis)ability, and socioeconomic status) from Bahnsonet al.’s (2022) work on discrimination in engineering graduate student experiences.Comfort and Team Inclusion (19 items)We believe feelings of discrimination and differences in belonging are also seen through students’comfort and inclusion on their team. As such, we included items based on these topics. Like othersabove, these scales
448Figure 1. Relationships between the knowledge worker at the center of the CI with high-performancecomputers and Teragrid, middleware, VO (virtual organizations), data management and knowledgediscovery, and visualization services. The objective of CIBRED is to educate and bring awareness of CI(Courtesy of S. Wang12; courtesy of Stan Watowich13).from computational technologies, enables individuals, groups, and organizations to advanceresearch and education in ways that revolutionizes the practice of participation. Once again, a newworkforce empowered with the knowledge and skills to design, deploy, adapt and apply CI, areneeded to sustain this revolution across all areas of science and engineering. The OCI CI-TEAMprogram supports educational
participant is not actually swimming any particular stroke. Prior methods of conductingstudies would record some level of inactive data (e.g. participant walking to the pool), whichwould then have to be parsed and removed manually. We can also record flip turns with this app,which also allows us to avoid manually removing flip turns in order to preserve data integrity.This application was developed through prior work done with Powell [55].Data Processing And Feature ExtractionIn order to extract features and train a classifier using our accelerometer data, we first need toclean the data, extract features, and present it to our classifier(s) in a .csv file format, which is afile format that WEKA, a machine learning library, accepts as input for
to the Reflection Tool QuestionsAn excerpt of the responses Instructor 1 gave during the interview is summarized below in Table2. The comparisons to student responses are also listed, which includes only the top responses(or top two if the difference in number of responses was 2 or less). A discussion of thecomparison of the two responses follows Table 2.Table 2: Instructor 1’s perception of student responses to reflection questions in the Engineering Page 22.351.6Economic Analysis CourseReflection Tool Questions Instructor 1 perception MEA specifics Comparison to
specimen, the process of loading the brass specimen can ANDWILL STRETCH THE SPECIMEN. To safely load the brass specimen, follow the processdescribed in step 6 but for the lower jaws, grab the test rod and lightly tap the joystick. Havesomeone monitor the load on the laptop and ensure the load does not exceed 100 lbs. This canhappen very quickly. 6. Once the test rod has been mounted in the jaws, connect the strain gage to the cord, see Figure 6a, by plugging in the data cord to the test rod, see Figure 6b. To connect the cord to the box, with the black locking switches in the up position, see Figure 7a, insert the white wire into the D120 port on Channel 1 Input. Then insert the black wire into the S- port and the red wire
influenced by. Like individual socioeconomics,these characteristics reflect hierarchical social and economic ranking amongst people. Importantly,they reflect Keynes (1936) argument that socioeconomics are group mentalities that organizepeople’s positions amongst society. Keynes (1936) illustrated that individuals with similar incomeslive together (household) or near one another (neighborhood/school) and likely have a similaroccupation. Given these features, we consider the following relational socioeconomic factors:1. Family/household income, occupation, and education are representations of the total, combinatory income(s), prestige, or educational status of the household. Household socioeconomic status has also been inferred based on what
the perceived challenges of live streaming as an informal learning opportunity forcomputer science students?Through this work, we aim to understand and evaluate whether or not live streaming impacts anundergraduate student’s perceived self-efficacy in software or game development, RQ1 . Toquantitatively measure self-efficacy, we have adapted questions from Ramalingam andWiedenbeck’s Computer Programming Self-Efficacy Scale and Hiranrat et al.’s surveymeasurements for software development career [41, 42]. As we allow the students to choose theirown projects and set their own goals, we expect there to be some division among the participantson how quickly they believe themselves to be improved based on the gravity of the goals they setfor
from merely reacting tochallenges to actively learning and growing from them. Ultimately, this approach shifts themindset from reactive problem-solving to personal development and continuous learning. Beyond these alignments, in terms of connection to industry and leadership, personalmastery does have a presence in industry. Literature noting that current engineering education isnot producing leadership qualities in engineers [30] suggests that something must be done tomeet the U.S.’s leadership needs. With many of the traditional organizations within industrytransitioning to learning organizations, likely to meet the demands of the Fourth IndustrialRevolution as learning is the “currency of survival” [8, p.1], lifelong learning remains