]. Natural LanguageProcessing (NLP) uses machine learning methods like transformer-based machine learningmodels [7], [8], which can be used through fine-tuning or in-context learning methods. NLP canbe used to train algorithms that can automate the coding of written responses. Only a few studiesfor educational applications have leveraged transformer-based machine learning models, furtherprompting an investigation into its use in STEM education. However, since language analysis ischallenging to automate because of its complexity, NLP has been criticized for increasing thepossibility of perpetuating and amplifying harmful stereotypes and implicit biases [9], [10].This study details preliminary results to plan for using NLP for linguistic justice
. Seifert, A. L. Patalano, K. J. Hammond, and T. M. Converse, “Experience and expertise: The role of memory in planning for opportunities,” in Expertise in context: Human and machine, Menlo Park, CA: AAAI Press, 1997.[14] K. M. Martin, E. Miskioglu, C. Noble, A. McIntyre, C. S. Bolton, and A. Carberry, "Predicting and Evaluationg Engineering Problem Solving (PEEPS): Instrument Development," presented at the Research in Engineering Education Symposium & Australasian Association for Engineering Education Conference, Perth, Australia, 2021.[15] P. S. Steif and J. A. Dantzler, “A statics concept inventory: Development and Psychometric Analysis,” Journal of Engineering Education, vol. 94, no. 4, pp. 363–371
majorsat the host institution to also register for the course if they are interested. The first iteration ofthis course ran in the Fall of 2022 with 11 students. The students were from engineering (1biomedical, 1 chemical, and 3 mechanical) and engineering technology majors (1 mechanicalengineering technology and 5 electrical and computer engineering technology). The course isbeing planned to run on a yearly basis.Preliminary FeedbackA mid-semester one-question feedback survey was run asking students what is the mostimportant/valuable thing they have learned in this course so far. Table 2 below summarizes theresults. There were three themes observed, namely Python programming, 3D modeling, anddesigning prototypes and making which match with the
authors seek to continue the student perception surveys over the next two academic yearswith new groups of Multidisciplinary Design Capstone students. The results will be evaluatedbased on student demographic differences and similarities to evaluate research question 2 (RQ2).One comparison that will be made will be comparing the results from the engineering students tothe engineering science minor students. Full statistical analysis will be completed on the overallresults and the results of each subgroup. In addition, student work will be collected as part of thecourse curriculum assignments to evaluate and compare to student perception surveys. Theauthors plan to directly assess students’ works as it pertains to research question 3 (RQ3
lecture. Additionally, the plan is to present this work as aworkshop at Lilly Conferences, which provides opportunities for the presentation of scholarshipof teaching and learning. With more historical background, this work could showcase thedevelopment within fluid mechanics against the backdrop of scientific progress.In April 2015, the University Faculty Senate at the Pennsylvania State University approved anew requirement for Integrative Studies within the General Education program. Theimplementation details for this requirement were approved in March 2016 and apply to studentswho started at the Pennsylvania State University during or after the summer of 2018. TheIntegrative Studies requirement offers two pathways for students to fulfill it
3 4 5 6 7 teams I like the objectivity of engineering education 1 2 3 4 5 6 71=Strongly Disagree, 2=Disagree, 3=Slightly Disagree, 4=Neutral, 5=Slightly Agree, 6=Agree,7=Strongly AgreeThe list of items is not final. Our ongoing research may direct us to add/remove or amend items.Our future work aims to further refine and psychometrically validate the EUSWQ. 4.1 PSYCHOMETRICS OF EUSWQ AND FUTURE WORKFor our future work, we are planning to validate the EUSWQ after presenting it to a larger numberof the undergraduate engineering student population. We aim to conduct two types ofpsychometric validation analysis. As part of the structural validity of the EUSWQ, exploratoryfactor analysis (EFA) will be conducted to verify
, ability or personal values essential information or present, or motivation to relative to knowledge/skill operations to make demonstrate perform quantitative with definitions, a decision, quantitative quantitative tasks information, equations, basic compare/contrast, information or operations, and quantitative build a model, concepts to an tasks. operations project, plan, etc. external or pseudo audienceTable 3: Coding indicators used to determine if a student
addressed, andstudents were encouraged to escalate them to campus faculty resource centers if necessary.The course was structured into approachable modules, with shorter textbooks matched to thesequencing. The course started with an introduction to robot basics including sensing, actuation,planning, and control [16]. The course progressed into programming and architectures includingreactive control, deliberative control, and hybrid architectures [17]. Lastly, project work exploredtopics such as robot operating systems (ROS) [18], [19], robotic simulators [20], and cloudrobotics [21].Overall, the proposed solution emphasizes the importance of designing inclusive practices thatprovide multiple means of engagement, while setting clear expectations and
students on how to maximize the value of the high-impact experience? Plan how this information will be How will you apply this high-impact experience to your career and useful to you professional life?Appendix Table 2: Representative examples of approved high-impact experiences. Experience Description Activity Category This is designed to engage engineering undergraduate students with multidisciplinary team research projects related to engineering challenges facing our society. The grand
, no. 4, pp. 461-480. 2019.[34] Penn State Department of Mechanical Engineering, “Strategic Plan 2020/2021-2025/2026,” Penn State Department of Mechanical Engineering, 2020.[35] H. Weigand, P. Johannesson, & B. Andersson. “An artifact ontology for design science research.” Data & Knowledge Engineering, vol. 133, p. 101878. 2021.[36] B. M. Wildemuth. “Existing documents and artifacts as data.” Applications of social research methods to questions in information and library science, pp. 158-165. 2009.[37] H. J. Rubin & I. S. Rubin, I. S. Qualitative interviewing: The art of hearing data. Sage.[38] K. Charmaz. Constructing grounded theory: A practical guide through qualitative analysis. Sage. 2006.[39] E
theyhave benefited from it regarding how ready they are to approach the homework for that week inthe course. In the future, it would be interesting to see if by changing to more industry standardways of planning code such as the use of pseudocode or flowcharts would impact the studentoutcomes.6.0 Works Cited[1] J. P. Penny and P. J. Ashton, "Laboratory-style teaching of computer science," ACM SIGCSE Bulletin, vol. 22, no. 1, pp. 192-196, 1990.[2] O. Hazzan, N. Ragonis and T. Lapidot, Guide to Teaching Computer Science, Cham: Springer, 2020.[3] M. Prince and R. Felder, "The Many Faces of Inductive Teaching and Learning," J. College Science and Teaching, vol. 36, no. 5, pp. 14-20, 2007.[4] M. Prince and R. Felder, "Inductive Teaching and
a tool to discuss their design ideas, artifacts, testresults, and plans for iteration, as shown in Table 1.Table 1. Aspects of mechanistic reasoning and their definitions Aspect of In elementary school engineering design Related elements from Russ et al. (2008) mechanistic and Krist et al. (2019) frameworks reasoning Identifying Describing how a design (or design sub-system) Russ: Describe the target phenomenon target performed in a test or describing a specific goal for (#1) and identify the set-up conditions (#2) performance future design performance Naming Recognizing the distinct components of a design
oral presentation, theaudience were given a form to evaluate the presentation and give feedback according to theinstructions. Sharp (2003) mentions that the speakers enthusiastically welcomed the peerfeedback.Kmiec et al. (2003) reported on an NSF-funded project that aimed at improving students’ writtenand oral technical communication skills from a teamwork perspective. They implemented theproject in the chemical engineering’s “Unit Operations Lab”. The module on collaborative oralpresentation targeted proficiencies such as planning, designing, and conducting the presentationas a team. Their strategies for facilitating the development of these skills included multiple oraland communication consultation sessions with the teams and allowing a
, etc. [10, 11,12]. Teacher preparation is advocated as a vital avenue to provide a sustainable professionalexperience that will, in turn, reach many at the school level [13]. Part of the role of education isto improve skills in decision-making, critical thinking, and problem solving. Lecture and theorybased teaching methods deny success to those who learn through experience. Therefore,opportunities that provides a learner a chance to make mistakes and receive immediate feedbackthat AI brings affords is a welcome to education [8,15].MethodA three-year plan to study the impact of the AI computer vision workshops for teachers isunderway. The first two years have been completed and are being reported in this paper. Yearthree will be conducted
micro-certificate in the professoriate, and led several educational experiences for underrepresented high school students. Amanda plans to pursue a higher education teaching career and research strategies to promote active learning and improve self-efficacy amongst engineering students.Dr. Raj R. Rao, University of Arkansas Dr. Raj R. Rao is a Professor of Biomedical Engineering, University of Arkansas, Fayetteville. He currently serves as the Editor-in-Chief of the Journal of Biological Engineering, as an ABET Program Evaluator; and is a member of the Biomedical Engineering Society (BMES) Education Committee. His research interests are in the broad area of cellular engineering that utilize
received a master’s degree in Technology and Engineering from Iowa State University and bachelor’s of science in Manufacturing Systems from North Carolina A&T State Univer- sity. Dr. Johnson is currently serves as the President of Women in Technology, Management, and Applied Technology (WITMAE) and secretary of the National Transportation Review Board. Dr. Johnson has publications in both national peer-reviewed journals involving aviation and technology education.Dr. Willie L. Brown Jr., University of Maryland Eastern Shore Dr. Willie L. Brown, Jr. is the Interim Vice Provost for Institutional Planning and Quality; and an Associate Professor of Engineering and Aviation Sciences at the University of Maryland Eastern
Learning Goals, Essential Project Design Elements, and Project-basedTeaching practices [12]. At the core of the BIE PBL framework are the student learning goals,which include key knowledge, understanding, and success skills. Surrounding these core learninggoals are the seven essential project design elements: 1) a challenging problem or question, 2)sustained inquiry, 3) authenticity, 4) student voice and choice, 5) reflection, 6) critique andrevision, and 7) public product. Project-based teaching practices consist of 7 elements: 1) designand plan, 2) align to standards, 3) build the culture, 4) manage activities, 5) scaffold studentlearning, 6) assess student learning, and 7) engage/coach. This research-informed PBLframework was chosen for its
at the university.Planned Next StepsContinuing to work with academic advisors across the college of engineering on approvingcommunity-engaged courses as technical electives and capstone design courses is important forimproving access to the HE program. Using the IDI as an assessment tool may help to provideinsight into the impacts of the program related to intercultural competency growth. Furtherqualitative assessment metrics are in development and planned for implementation.References[1] Jacoby, B. 2014. Service-Learning Essential: Questions, Answers and Lessons Learned,Edition 1. Jossey Bass Higher and Adult Education, A Wiley Brand.[2] Greene, H. L., & Eldridge, K., & Sours, P. J. (2019, June), Engagement in Practice
Paper ID #40243Why Students Choose STEM: A Study of High School Factors That InfluenceCollege STEM Major ChoiceDr. Joyce B. Main, Purdue University Joyce B. Main is Associate Professor of Engineering Education at Purdue University. She received an Ed.M. in Administration, Planning, and Social Policy from the Harvard Graduate School of Education, and a Ph.D. degree in Learning, Teaching, and Social Policy.Tram Dang, Purdue University Tram Dang is a PhD student of Engineering Education at Purdue University as well as a tenured professor of physics and engineering at Santa Monica College (SMC), a two-year transfer-focused
different types of ADHDexperiences.8. Future Work and LimitationsA major limitation to this work was the small sample size used to generate an LDA model as anLDA model is more accurate with more data for training. While the ideal sample size for LDAmodeling can vary, previous literature suggest using a sample size of at least fifty withpreference for larger sample sizes for more accurate and stable results [73], [74]. We plan toaddress this limitation in our future work by using APIs to data mine social media platforms. Bydata mining social media posts, we will be able to gather large amounts of data to train our LDAmodel for more accuracy. Further, the LDA method alone analyzes words and does not considercontext or syntax of those words (e.g., the
participants identifying as biracial. The interviews were semi-structured, in-person interviews that lasted approximately onehour. As of the date of this study, two interviews in the series of planned interviews had beencompleted per student. The first interview occurred in the spring semester of students' firstcollege year (spring 2023) and was designed to understand students' perceptions of themselves asengineers, how they experienced the classroom and other spaces on campus, and theirattributions about their self-efficacy for student success. The second interview occurred in thefall semester of the second college year (fall 2023) and was designed to understand students'experiences as they transitioned into their majors, and made sense of
Paper ID #38440A New Normal: Pedagogical Implications for Physics and STEM Teachingand Learning in the Post-Pandemic EraDr. Teresa L. Larkin, American University Teresa L. Larkin is an Associate Professor of Physics Education and Director and Faculty Liaison to the Combined Plan Dual-degree Engineering Program at American University. Dr. Larkin conducts educational research and has published widely on topics related to the assessment of student learning in introductory physics and engineering courses. Noteworthy is her work with student writing as a learning and assessment tool in her introductory physics courses for non
will happen anyway. Hence, the need to bepurposeful, act with deliberation, and to plan ahead.On Distance EducationSeveral panelists comment on the significance and challenges of distance education.On Educating EducatorsDr. Watford emphasizes how important educating educators is … they are challenged to educatedifferently, yet are not prepared to do so. She also comments on how long ‘active learning’ hasbeen part of engineering education transformation, but questions how pervasive or wellimplemented such approaches are on a large scale. Students are not responsive to ‘old’methods. We must learn how to teach using the tools and methods we are challenging students tolearn. She uses the VLOOKUP function in Excel as an example. How many professors
, followed by the Clean Air and Water Acts, was part of a broader plan to protect the environment from any point source of pollution or contamination.” (Ramirez, 2021).The article points out the limits of the law when it was created in that it omitted to include civilrights protections. Ramirez then traces a history of the environmental justice movement and howits roots lay with Black communities. Through several case studies, Ramirez traces the activismby various communities to then detail the development of environmental policies that have comeafter NEPA. Ultimately, the Vox article discusses the limits of NEPA as it does not addressdisparate harm to disadvantaged communities. Even if developers put together an environmentalimpact
material (e.g., when they address assigned homework).Office hours outside of class suffer from logistical difficulties associated with aligning time of availabilitywith times of student need. Further, the students most in need of help are often least likely to seek it out.The traditional environment often has a competitive aspect in which the only measure of success is anexam grade.The course redesign process involved a period of planning and discussion among the faculty assigned toteach the courses and some faculty who taught the downstream courses that depend most directly on theoutcomes from the mechanics courses. Implementation of the new course elements was gradual.Dynamics was the first course to undergo the complete redesign. This choice
sessions with content ranging from “Getting Started inEarSketch” to “ Racial Discourse in the Classroom.” These sessions range from 1-2 hours, arefacilitated by YVIP curriculum directors, include hands-on activities, and are recorded for futurereference. MethodsEvaluation Framework The evaluation was conducted following the principles of the participatory evaluationframework, defined as “applied social research that involved a partnership between trainedevaluation personnel and practice-based decision makers, organization members with programresponsibility, or people with a vital interest with the program” [15]. These various stakeholderswere involved in the planning and design of the evaluation
engineering) is the work of Guerin and hercolleagues [20]. Borrego and her team also examined motivational factors to consider master’sand Ph.D. degrees separately. For instance, they found that “for every one-unit increase instudents’ self-efficacy, they were over eight times more likely to plan to enroll in a master’sprogram and 13 times more likely to plan to enroll in a Ph.D. program relative to not attendinggraduate school” [8, p. 154].Therefore, the body of literature on women in graduate degrees in engineering remains extremelylimited, especially disaggregated on each graduate degree (MSc, MEng, or Ph.D.) or engineeringsubfields. In order to address systematic challenges that threaten EDI, specifically in engineeringgraduate programs, it is