. Following more data cleaning, 96 respondentsfrom the pre-and post-survey were obtained.Implementation of ECP in ClassesPhase 1: Data Collection1.1 Pre-Test: A survey was administered to students enrolled in a biology course at the universityto measure their current level of motivation and learning prior to the experiment-centric pedagogyintervention. This survey asks students to rate their level of motivation and learning on a 1-7 Likertscale, as well as include open-ended questions about their attitudes towards Biology courses andtheir experiences with Experiment-Centric Pedagogy.1.2 Implementation: A special project (Effect of Exercise on Heart rate) based on Experiment-Centric Pedagogy was implemented in the biology course. This involved the use
software development [1], politics [2], or theworkplace [3], the idea of incorporating game elements to enchance performance is rapidly beingimplemented. Such is the case in learning as well. Gamification is a new tool in making the studentenvironment more effective and dynamic than the traditional classroom model [4]. Gamificationis a natural application of experiential learning, wherein students learn by doing i.e. being activelyengaged in material with tasks, problems, or projects. Trivial examples of gamification to enhancelearning include those of educational games or in self-teaching tools such as Khan Academy orClasscraft. Early discussions of gamification in the classroom share the opinion that gamification has thepotential to improve
efficiency and suitability ofthe peer observation process itself; impact-related questions inquire about the effect of theprocess on the quality and outcomes of the teaching practice; and culture-related questions arerelated to the overall perceptions of the impact of the peer observation process on thedepartmental culture around teaching and faculty relationships. Finally, a set of questions isrelated to the project objective of promoting inclusive teaching practices, evaluated separately.Table 1 shows the questions related to each theme from the committee member survey (CMS)and observed faculty survey (OFS). Table 1: Grouping of survey questions across the three themes Theme Process Impact
(TMCT) was developed in 2018 at Utah State University as apart of a National Science Foundation funded project in partnership with the National Federationof the Blind (NFB) [9]. The TMCT is a tactile testing instrument that is intended to measure andquantify both spatial visualization and spatial relational capabilities of the BLV population. Afteranalyzing pilot TMCT participant score data, our research team decided to increase the utility ofthe TMCT by splitting the original format of the 25-questions into two parallel subtests (A & B),each containing 12 questions. With this significant change to the format of the instrument, weneeded to determine if the reliability of the TMCT was retained in its split form.A pilot analysis confirmed
problem by getting all parts correct, then they hadthree options. 1) They could choose to take a written retake, which meant receiving a similarquestion on the next written exam. 2) They could choose to take a verbal retake which meantgoing through the same question with follow up questions from the instructor. 3) They couldchoose to complete a mini-project to make up the grade. Students were surveyed about theequitable grading strategy. Eight students commented on the retakes. Retakes provided a push tolearn what they had failed to learn the first time.Ritz et al. [12] compared student performance between two sections of a sophomore-level staticsand mechanics of material course. The control section had graded homework and two midtermexams graded
answered.At the top of every post in the active tab of KarmaCollab is a button that will launch a QR codescanner. The video chat automatically launches once the companion web app is scanned from anybrowser (no login required). KarmaCollab video rooms have no capacity limits and allow forscreenshare, which is used extensively in project courses involving simulations and coding.Self-Managed ArchiveKarmaCollab tries a new approach to archiving posts. Other platforms such as Slack, Piazza, andBlackboard allow for an infinitely long archive of all questions posted, sometimes even from pastinstances of the course. KarmaCollab uses a model more like Twitter, where trending archivedtopics bubble to the top of the archive, and posts that have lost relevance
from The University of Texas at Arlington.Lauren Fogg, zyBooks, a Wiley Brand Lauren Fogg obtained her Bachelor’s degree in Mechanical Engineering in 2021 and her Master’s degree in Mechanical Engineering in 2022 from Louisiana Tech University. She is currently working on her Ph.D. in Engineering with a concentration in Engineering Education from Louisiana Tech University. She is cur- rently an Associate Engineering Content Developer with zyBooks, a Wiley Brand. Her research interests are diversity, gender equity, retention, project-based learning, cognitive models of problem-solving, and making engineering textbooks more accessible and innovative for students.Dr. Alicia Clark, zyBooks, A Wiley Brand Alicia
Leslie Massey is an instructor in the First-Year Engineering Program at the University of Arkansas. She received her BS in Biological Engineering and MS in Environmental Engineering from the University of Arkansas. She previously served as a project manaDr. Heath Aren Schluterman, University of Arkansas Dr. Heath Schluterman is a Teaching Associate Professor and the Associate Director of Academics for the First-Year Engineering Program at the University of Arkansas. Dr. Schluterman completed his B.S. and Ph.D in Chemical Engineering at the University of Arkansas.Gretchen Scroggin ©American Society for Engineering Education, 2023 Exploring Chemistry Success in First Year Engineering
tool, a set ofsurvey questions were given to those students whose schedules have been made using theadvising tool. The collected survey data has been analyzed statistically to determine the tool'sefficacy from students’ perspectives. The analyzed data indicate that the students were overallsatisfied and had positive attitudes towards different aspects of the tool.MotivationIn any major, preparing an effective and error-free course plan for undergraduate students eachsemester is crucial for their timely graduation. However, various constraints may arisethroughout the student’s four-year program, which can cause uncertainties in their graduationtiming. Students also often want a clear picture of their projected graduation date, including
learning environments of interdisciplinarysettings, which focused on collaboration and equipment malfunctions [20]. In another, a clinicalimmersion program for biomedical engineering students, where participants evaluated clinicalneeds to address in a capstone project, was effectively pivoted to a remote format [21]. Largelyout of necessity, these studies have focused more on the adaptation process than the systematicmeasurement of reciprocal outcomes or virtual internship designs While the immediate needs forvirtual internship opportunities, caused by COVID-19, may be dwindling, these modalities willlikely have a role in addressing access and equity in both the workforce and higher education inthe near future [13], [18].Equity and AccessThere is
, interdisciplinary programs, and projects to prepare students todesign and simulate zero emission technologies. The following detailed descriptions of thesecalculations should be viewed in terms of the background needed to perform them and the impactthey have on the education of the next generation of engineers who will design and build thesedevices.In the following sections we review some of the non-membrane-based technologies of extractingsalinity gradient energy. Non-membrane electrode-based technologies including CapMix andmixing entropy battery (MEB) produce electricity during a batch cycling, which means thesetechnologies have not been shown to operate on a continuous cycle considering the current stateof the art. The literature indicates that the non
would be that students overestimate their motivationlevels when a class starts. To test the reliability of our findings regarding early and late-term differences, weplan to include all first-year engineering students in a future iteration of this project, resulting in a sample sizeestimated to be well over 300 students.We also conducted a path analysis to test the hypothesized impact of a caring instructor on the other motivationfactors. We hypothesized that the caring factor would significantly predict student empowerment throughmodel in which caring had both a direct effect on empowerment, and indirect effects on empowerment throughthe success, interest, and usefulness factor scores. In other words, we believed that success, interest
second time atthe end of the week to see if their initial strategies had evolved. TMCT scores were consideredhigh if the participant correctly answered 9 or more of the 12 problems. Scores of 3 or lowerwere considered low scores.Case DescriptionsThis case study [29] is part of a larger research project aiming to measure the spatial ability andidentify the spatial strategies used by BLV populations. Annually, thirty participants wererecruited from all areas around the United States for this larger study. The study spanned 5 yearsbut had interruption due to the Covid-19 pandemic. All participants were high school studentsranging in academic grade level from 9th grade to 12th grade. This paper presents a case study offour of the 30 total
36% 3-5 courses 3 14% 1-3 courses 1 5% The Aerospace major requires 13 units (~5 courses) of lower division engineering coursesto be completed in the first two years of the program with one of them being a prerequisite to thesenior year project courses and the rest are considered as important courses to support studentlearning in the core courses. The responses to this question as shown in Table 7 indicated that morethan half of the survey respondents (12/22) had taken more than 3 lower division engineeringcourses required by the program while about 45% do not have the minimum lower divisionengineering courses prior to
use.AcknowledgementsThe authors would like to express their sincere thanks to their advisor, Professor BalaMaheswaran and the First Year Engineering Learning and Innovation Center at NortheasternUniversity for their support during this project and producing the final prototype. They wouldalso like to thank our teaching assistant David Hunter for providing them with essentialinformation and feedback regarding their Bluetooth Module and code.References[1] Benjamin K. Barry, & Roger M. Enoka. (2007). The Neurobiology of Muscle Fatigue: 15Years Later. Integrative and Comparative Biology, 47(4), 465–473.http://www.jstor.org/stable/4540179[2] Mayo Clinic. (n.d.). Sprains. https://www.mayoclinic.org/diseases-conditions/sprains
) contaminated a sandy soil with benzene on the East shore of Lake Liebig. As part of the planned remediation scheme, the benzene will be removed with a soil vapor extraction (SVE) system and burned in an incinerator. A group of citizens has vowed to stop the project because they are concerned about children on a school playground 1-km directly downwind from the site. Students must determine if the cancer risk at the playground is acceptable when the incinerator achieves or exceeds the Environmental Protection Agency’s destruction removal efficiency. Figure 1. Sketch of the Overarching Quantitative Problem2. Create an overarching puzzle that will be completed in parallel with the quantitative problem. A crossword puzzle was used to
justifications with concept questions, inpart, because they provide a daunting amount of information for instructors to process and forresearchers to analyze.In this study, we describe the initial evaluation of large pre-trained generative sequence-to-sequence language models (Raffel et al., 2019; Brown et al., 2020) to automate the laboriouscoding process of student written responses. Adaptation of machine learning algorithms in thiscontext is challenging since each question targets specific concepts which elicit their own uniquereasoning processes. This exploratory project seeks to utilize responses collected through theConcept Warehouse to identify viable strategies for adapting machine learning to supportinstructors and researchers in identifying
. Army is home to two entities that address environmental problems or projects. First, theU.S. Army Environmental Command’s (USAEC) mission is to deliver cost-effectiveenvironmental services globally to enable Army readiness, to include leading and executing 2022 ASEE National Conferencecleanup and environmental quality programs [12]. More outward facing, the second is the U.S.Army Corps of Engineers (USACE) Environmental Program, which prides itself on being the“nation’s environmental engineer”. USACE (or “the Corps”) manages several large federalmissions, to include regulating waterways, managing natural resources, constructing sustainablefacilities, and restoring degraded ecosystems. The Corps also cleans up
maintained many years beyond thecompetition of the funded project supported by the National Science Foundation.AcknowledgmentsThe authors thank contributions from Alex Edgcomb and numerous teaching assistants. Thismaterial is based upon work supported by the National Science Foundation under Grant No.DUE 1712186. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. This work was completed within the framework of University of ToledoIRB protocols 2011808 and 202214.DisclaimerOne of the authors may receive royalties from sales of the zyBook detailed in this paper.References[1] A. Edgcomb, F. Vahid, R. Lysecky, A
risen for employees in computing fields [1]. Between 2019 and2029, computer and information technology occupations are projected to rise at a rate of 11%,higher than other fields [2]. Apart from needing to develop a skilled workforce, the computingindustry struggles to maintain an equal representation of women, especially women of color.Only 25.8% of computer and mathematical occupation employees are women. Among those,only 23.1% are Asian, 8.7% are Black/African American, and 7.8% are Latinx [3].COVID-19 has added to the industry’s gender and ethnic underrepresentation issues. Thepandemic’s reallocation shock has caused more than 31 million Americans to rely onunemployment [4]. A recent study conducted by the University of Chicago estimates 32
Paper ID #36649Hyflex for Successful Student Veteran EngineeringEducation: Say it Like You Mean ItRobert J. Rabb (Chair, Mechanical Engineering) Professor, Mechanical Engineering, The CitadelAlyson Grace Eggleston Dr. Alyson Eggleston is a cognitive linguist specializing in the impact our speech has on the way we think and solve problems. She is the founding Director of Technical Communication at The Citadel, and has developed a project-based technical communication course that serves over 14 STEM majors and several degree programs in the social sciences. She is also acting Residential Fellow for the Center for
incorporating the NAE GrandChallenges for engineering as a multi-disciplinary hands-on design project into the introductionto engineering course,” In Proceedings of American Society for Engineering Education AnnualConference and Exposition, New Orleans, LA, USA, June 26-29, 2016.[4] C.L. Dancz, K.J. Ketchman, R. Burke, R. Mahmud, M.M. Bilec, K. Parrish, E.A Adams, B.Allenby, and A.E. Landis, “Integrating sustainability Grand Challenges and experiential learninginto engineering curricula: Years 1 through 3,” In Proceedings of American Society ofEngineering Education Annual Conference and Exposition, New Orleans, LA, USA, June 26-29,2016.[5] E. Fife, “Understanding the impact of engineering through engagement with the NationalAcademy of Engineering Grand
approaches in thecontext of bioengineering. The course is taught as an active-learning course with lecture andproblem solving sessions in class, 8 homework assignments (roughly every two weeks), 3quizzes, 3 midterms, and a project. This course was chosen because all students in the programwere enrolled, providing consistent access to the whole cohort, and because the course had threeevenly spaced midterms, each worth 15% of the overall grade, which allowed for ease ofcollecting performance information used in the study.InstrumentsLearning styles:In this study we use the Index of Learning Styles [5, 11] which is an on-line survey instrumentused to assess preferences on four dimensions (active/reflective, sensing/intuitive, visual/verbal,and
been replaced by β0 and xβ. The coefficients by β0 and β are notknown, and they must be estimated based on the available training data using a technique knownas Maximum Likelihood Estimation (MLE). In logistics regression, β0 is known as the interceptand xβ is known as the coefficient. Figure 6. Flipping the Logit curve into Sigmoid curve.In this project module you will discover how to use basic statistics and begin to prepareyour data for machine learning in Python using Numpy and SciPy.Both NumPy and SciPy are Python libraries used for used mathematical and numerical analysis.NumPy contains array data and basic operations such as sorting, indexing, etc. whereas, SciPyconsists of all the numerical code. SciPy has a number of sub-packages for
received his Ph.D. in Genomic Signal Processing from the Department of Electrical and Computer Engineering at Texas A&M University. The algorithms proposed by Dr. Gargari for solving discrete and continuous optimization problems have been used in different areas of science and engineering, and in several thousand projects in academia and industry. His research interests include Artificial Intelligence, Evolutionary Computation, Pattern Recognition, Error Estimation, and Proteomics.Lu Zhang © American Society for Engineering Education, 2022 Powered by www.slayte.com Multi-Semester Course Staffing Optimization Mudasser F. Wyne
for growth in the field. As research oneducational best practices within the Black community continues to grow [6] and K-12Broadening Participation in Computing (BPC) projects rise, so should the attention to diversity inthe higher education space continue to grow.Within the United States specifically, there has been a surge in diversity, equity, and inclusionclaims by tech companies in response to the racial climate in America [7, 8]. Current events notonly bring attention to the plight of Black Americans but also present a good time to look at howthe large-scale systemic racism seen in our society is also present in the workforce (specificallytech) and education systems. The systemic racial issues embedded in higher ed and that play arole
Paper ID #38370Perceptions of Engineering Learning Software in Classroomswith Diverse Student Populations Using an ExpandedTechnology Acceptance ModelKimberly Cook-chennault (Associate Professor)Idalis Villanueva (Dr.) For the past 10 years, Dr. Idalis Villanueva has worked on several engineering education projects where she derives from her experiences in engineering to improve outcomes for minoritized groups in engineering using mixed-and multi-modal methods approaches. She currently is an Associate Professor in the Engineering Education Department at the University of Florida. In 2019, she received the
the survey administered to students in the engineering science course asked about gender. “Other” students either reported “agender,” or reported no gender.I recruited first- and second-year students for two reasons. First, the empirical literature onstudents’ beliefs about engineering knowledge suggests that engineering students’ beliefs remainrelatively stable during the first two years of undergraduate engineering education [15].Conversely, students’ beliefs are likely to change dramatically over the final years of theundergraduate engineering curriculum as they engage in team- and project-based learningexperiences that shape their perspectives on engineering knowledge [15]. As a result, I chosefirst- and second- year students for the
reports a study that exclusively focuses on internal thrivingcompetencies. Data for this study were collected during the first phase of a three-phase datacollection process as part of a larger project to create a model of engineering thriving bygathering consensus from engineering experts [2]. We acknowledge that thriving for engineeringstudents includes a breadth of interactions between the students and their environment within thelarger engineering culture and system. From a research perspective, best practices whenconducting rank-order research caution against asking participants to rank a list of 147 factorsbecause “distinctions between individual elements become difficult for the person making theranking to maintain meaning” [5]. To scope
Context 11 Capstone, Internship, Senior Project, courses in disciplines such as physics, biology, chemistry, the humanities, or other areas Math and Statistics Calculus, discrete structures, probability theory, elementary statistics, advanced topics in statistics, and linear algebra. Table 3: Mapping of 7 competencies to 11 ACM Data Science Task Force Competencies3.5 Data Analysis Pearson’s Correlation Coefficient Analysis was conducted. Pearson correlationcoefficient