length of the data presented. Thisindicates that models optimized with cognitive features are particularly adept at distinguishingbetween binary outcomes. The most accurate predictions were made by ChatGPT 4.0 (as shownin Figure 3(b)), achieving an accuracy of 67% with 2-week data, and improving to 69% accuracyfor both 4-week and 8-week datasets. Nonetheless, when tasked with a more nuanced four-classclassification using only cognitive features, the accuracy across all three datasets falls below50%.The incorporation of background features (C + B) notably enhances binary classificationaccuracy. For example, ChatGPT 4.0’s accuracy for 2-week data improved to 73%, and furtherincreased to 75% and 77% with 4-week and 8-week data, respectively. Gemini
research and a fertile newway to understand the underlying social, motivational, and cognitive dimensions of conceptualchange.References 1. Streveler, R., T. A. Litzinger, R. L. Miller and P. S. Steif (2008). Learning conceptual knowledge in the engineering sciences: Overview and future research directions. Journal of Engineering Education 97(3). 2. Brown, S. and D. Lewis (2007). Student Understanding of Normal and Shear Stress and Deformations in axially loaded members. ASEE Annual Conference & Exposition, Louisville, KY. 3. Brown, S., D. Montfort and K. Findley (2007a). Development, Implementation, and Assessment of a Bending Stress Tutorial. ASEE/IEEE Frontiers in Education Conference. Milwaukee, WI
. Figure 4. Participant 1’s (KAI score = 72) concept sketchesParticipant 2 (KAI Score= 88). Participant 2 was the second most adaptive student in ourexperimental group, with a 16-point style difference (in the more innovative direction) withParticipant 1. Prior research has identified the “just-noticeable-difference” (JND) for KAI as 10points (Kirton 2011), meaning that differences of 10 points or more between two individuals’cognitive styles will be noticeable over time (by the individuals themselves and those aroundthem). Participant 2 generated four concepts, which also appeared to be modifications of existingsolutions; however, he was more elaborate and detailed in his sketches than Participant 1 (seeFigure 5). His first concept was a sitting
did not altertheir features dramatically. However, this also allowed him to propose ideas that would haveimmediate efficiency, as they relied on existing, practical solutions. Figure 4. Participant 1’s (KAI score = 72) concept sketchesParticipant 2 (KAI Score= 88). Participant 2 was the second most adaptive student in ourexperimental group, with a 16-point style difference (in the more innovative direction) withParticipant 1. Prior research has identified the “just-noticeable-difference” (JND) for KAI as 10points (Kirton 2011), meaning that differences of 10 points or more between two individuals’cognitive styles will be noticeable over time (by the individuals themselves and those aroundthem). Participant 2 generated four
student veterans in engi- neering. Her evaluation work includes evaluating teamwork models, broadening participation initiatives, and S-STEM and LSAMP programs.Mr. Hossein Ebrahiminejad, Purdue University-Main Campus, West Lafayette (College of Engineering) Hossein Ebrahiminejad is a Ph.D. student in Engineering Education at Purdue University. He completed his M.S. in Biomedical Engineering at New Jersey Institute of Technology (NJIT), and his B.S. in Me- chanical Engineering in Iran. His research interests include student pathways, educational policy, and quantitative research methods.Mr. Hassan Ali Al Yagoub, Purdue University-Main Campus, West Lafayette (College of Engineering) Hassan Al Yagoub is a Ph.D. student in
Engineering from the University of Iowa. Her educational research interests are focused on methods to attract and retain women and underrepresented minorities in STEM fields. c American Society for Engineering Education, 2020 INCORPORATING SUSTAINABILITY AND RESILIENCY CONTENT INTO CIVIL ENGINEERING UNDERGRADUATE CURRICULUMABSTRACTSustainability and Resiliency (S&R) concepts have risen to prominence in recent years. Theconcept of incorporating sustainability into civil engineering became popular in the late 1980sduring the advent of the construction industry’s first sustainable assessment system for officebuildings with more or less equally weighted environmental, economic
Aquisition USB USB USB 1st / 2nd year Analog IC Design Control Systems Linear Circuits Analog Circuit Analog IC Systems Signal Processing (4th year / Grad) (Grad) (2nd year) (3th year) (Grad) Course Year(s) Course Year(s) Linear Circuits (2nd year) Sp ’19, F ’20 Analog IC Design (4th year/ Grad) F ’15, ’17, ’19, ’21 1st / 2nd year Signal Processing F ’22, Su ’23 Linear Control
and their association with career interest in STEM,” International Journal of Science Education, Part B, vol. 2, no. 1, pp. 63–79, 2012.[5] Y. S. George, D. S. Neale, V. Van Horne, and S. M. Malcom, “In pursuit of a diverse science, technology, engineering, and mathematics workforce: Recommended research priorities to enhance participation by underrepresented minorities,” American association for the advancement of science, 2001.[6] N. Gonzalez, L. C. Moll, and C. Amanti, Eds., Funds of Knowledge: Theorizing Practices in Households, Communities, and Classrooms. New York: Routledge, 2005. doi: 10.4324/9781410613462.[7] P. Bell, L. Bricker, S. Reeve, H. T. Zimmerman, and C. Tzou, “Discovering and Supporting
this lack of representation in higher education engineeringprograms, the University of Lowell S-STEM program, funded by the NSF Scholarships inScience, Technology, Engineering, and Mathematics Program (S-STEM), has the goal torecruit three cohorts of low-income, high-achieving students who wish to pursue a career inhigher education. The UML S-STEM program supports engineering scholars for four years,their last two years of undergraduate school and their first two years of graduate school. Thegoal of the program is to attract and retain diverse engineering S-STEM scholars and preparethem to enter the competitive pool of future faculty candidates. We present our successes and challenges in recruiting the first two cohorts of low-income
many seconds) does it become possible to determine if a student will struggle. Asimple neural network is proposed which is used to jointly classify body language and predicttask performance. By modeling the input as both instances and sequences, a peak F Score of0.459 was obtained, after observing a student for just two seconds. Finally, an unsupervisedmethod yielded a model which could determine if a student would struggle after just 1 secondwith 59.9% accuracy.1 IntroductionIn this work, the role of machine learning for planning student intervention is investigated.Specifically, t his w ork a sks t wo q uestions: ( i) C an a s tudent’s s truggles b e p redicted basedon body language? (ii) How soon can these struggles be predicted
more on presumed difficulty with high-level concepts and specificapplication functionality. Further analysis will be presented at the conclusion of the springsemester once additional data has been collected and analyzed.AcknowledgementsT his material is based upon work supported by the National Science Foundation (NSF) underGrant No 1839357, 1839270, 1839259. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the NSF.References[1] EDISON: Building the data science profession; Edison Project.[2] S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth, Active learning increases student
materials are summarized below in Table 1. Publication Key Findings • Students struggle with shear and moment diagramsBrown, S., Montfort, D., and K. Hildreth. (2008). An and have limited understanding of how point loadsInvestigation of Student Understanding of Shear and and reactions affect internal forcesBending Moment Diagrams. Innovations 2008: World • Fundamental concepts like “moment” or “shear”Innovations in Engineering Education and Research. are difficult for some academically
, 2024AbstractThere is substantial opportunity for engineering graduates to enter the workforce to engage in afulfilling career and achieve social mobility. Still, there is a lack of adequate support forlow-income, academically talented students. The purpose of this poster is to describe theinterventions designed to support S-STEM scholarship students at Rowan University in the firstyear of our S-STEM project. Our S-STEM project objectives are threefold: 1) Providescholarships to encourage talented students with low incomes and demonstrated financial need toinitiate and graduate from engineering majors in the College of Engineering at Rowan Universityand subsequently enter the engineering workforce or a graduate program; 2) Develop a supportsystem that
, University of Missouri, Kansas City Dr. Michelle Maher explores student research, teaching, and disciplinary writing skill development and higher education access and equity issues. ©American Society for Engineering Education, 2023 Reaching Consensus: Using Group Concept Mapping in an S-STEM Research TeamAbstractThis study was done to explore Group Concept Mapping (GCM) as a method to reach consensusfor data collection using document analysis in an S-STEM research team. The team wascomprised of five members and the GCM approach was made up of six steps: (1) Preparation,(2) Generation, (3) Structuring, (4) Analysis, (5) Interpretation, and (6) Usage. The members ofthe
is to prepare the2023 Fall semester implementation. This will include a more detailed implementation frameworkfor 1101 Intro and UNIV 1301 sections. Further, the objective is to expand the interventions toinclude other departments in CECS and possibly to other colleges such as the College of Scienceor College of Business. Our vision is to have a sequence of interventions that continue thisFreshman Year experience with Sophomore, Junior, and Senior Year Innovator Experiences,with an increasing portfolio of skills each year. . T E S M ESS S ESS . T S . S E M T T
motivations or reasons fortransferring to a different institution; an important aspect of our study is to untangle thosereasons for engineering transfer students in Texas. Students accumulate transfer student capital,or knowledge about the transfer process, at sending institutions (i.e., the place(s) where studentsbegin their degree paths), receiving institutions (i.e., the final degree-granting institution), andpotentially from non-institutional sources. The development of transfer student capital maycome from experiences related to learning and study skills, course learning, perceptions of thetransfer process, academic advising and counseling, and experiences with faculty. Upon arrivingat the receiving institution, students must adjust to the new
Technology, New Delhi.Dr. Janet Callahan, Boise State University Janet Callahan is the Chair of Materials Science and Engineering at Boise State University. Dr. Callahan received her Ph.D. in Materials Science, M.S. in Metallurgy, and B.S. in Chemical Engineering from the University of Connecticut. Her educational research interests include materials science, freshman engineering programs, math education, and retention and recruitment of STEM majors. c American Society for Engineering Education, 2016 Lessons Learned from S-STEM Transfer Student Scholarship ProgramAbstractThis paper describes how the College of Engineering at Boise State University utilized
toward science and engineering we included an adapted version ofthe Middle/High Student Attitudes Toward Science, Technology, Engineering and Math(S-STEM) survey [33]. The scale measures students' attitudes toward their own proficiency inSTEM subjects (e.g., “I know I can do well in science”), the value of STEM toward futureendeavors (e.g., “Knowing about science will allow me to invent useful things”), and interest inSTE|M careers (e.g., “I believe I can be successful in a career in engineering”). The measureshad sufficient levels of reliability on the pre (ɑ = 0.87) and post surveys (ɑ = 0.87) .Additionally, to measure students' perceptions of engineers and engineering we adapted itemsfrom the “What is Engineering?” survey instrument [9]. The
abilities? • Career Decision Making: What career goals, expectancies, and values do engineering students have? How do these develop and change over time? What career choices do engineering students make for after graduation, and what role(s) does their career and self knowledge play in their decisions?We are using a multi-method approach to answer our research questions. We have alreadyinterviewed engineering faculty, student advisors, and career services staff at our six partnerinstitutions, to help us understand (1) the career resources available to engineering students onthese campuses, (2) the career pathways that these engineering students typically take, and (3)the skills and abilities they believe students need to find
Capacity to Pilot and Scale Corequisite Calculus for First Year Engineering Gateway CoursesAbstract:Norwich University, the oldest Senior Military College in the nation and the first private U.S.institution to teach engineering, has a residential program for approximately 2,100 primarilyundergraduate students in both the Corps of Cadets and civilian lifestyles. Norwich secured aNational Science Foundation S-STEM award in the beginning of 2020 to develop a program toattract and retain highly talented, low-income students in STEM. One of the aims of the projectwas to support students who enter college with less experience in mathematics as these studentswere significantly less likely to
researcher, including studying academic policies, gender and ethnicity issues, transfers, and matriculation models with MIDFIELD as well as student veterans in engi- neering. Her evaluation work includes evaluating teamwork models, broadening participation initiatives, and S-STEM and LSAMP programs. c American Society for Engineering Education, 2019 Paper ID #25442Dr. Joyce B. Main, Purdue University-Main Campus, West Lafayette (College of Engineering) Joyce B. Main is Assistant Professor of Engineering Education at Purdue University. She holds a Ph.D. in Learning, Teaching, and Social Policy from Cornell
Paper ID #42318Board 318: Instructor Experiences Integrating Facilitated Socially EngagedEngineering Content in their CoursesClaudia G Cameratti-Baeza, University of Michigan At CRLT, Claudia works with the Foundational Course Initiative (FCI) as Pedagogy & Instructional Design Consultant. In this role, she partners with departmental instructional teams and fellow FCI consultants to support the Universityˆa C™s large introductorDr. Erika A Mosyjowski, University of Michigan Erika A. Mosyjowski is the Research and Faculty Engagement Manager in the Center for Socially Engaged Design within University of Michigan College of
problems. They have learned to design,build, simulate, perform instrumentation and system integration, and/or test the developedmethods and algorithms in a multidisciplinary environment. This has resulted in improvedreadiness for careers that require multidisciplinary knowledge and skills.AcknowledgementThe project is funded by the NSF’s EEC Program. We would also like to thank LockheedMartin and Northrop Grumman Corporations for hosting the participants and giving them a tourof their research labs and facilities. We would also like to thank Northrop GrummanCorporation and Lockheed Martin Corporation for their continued support of the UAV Lab atCal Poly Pomona and its students.References[1] Bhandari, S., Tang, F., Aliyazicioglu, Z., Raheja, A
well as information for stakeholders to use inefforts to recruit and retain individuals traditionally underrepresented in engineering. The reportalso discusses the future of engineering education in light of these findings.This award was co-funded by the Division of Undergraduate Education in the Directorate forEducation and Human Resources and by the Division of Engineering Education and Centers inthe Directorate for Engineering. References[1] R. W. Lent, S. D. Brown, J. Schmidt, B. Brenner, H. Lyons, and D. Treistman. “Relation of contextual supports and barriers to choice behavior in engineering majors: Test of alternative social cognitive models,” Journal of Counseling Psychology, 50
the 1st generation, low income, urban and rural highschool student populations. As evidenced by their SAT Math achievement scores and high GPA’swhich prompted their admission, these students are smart. However, they received their STEMeducation in low performing urban and rural high schools and were raised in highly challengedunder-resourced neighborhoods. Research shows that these talented students succumb to theintensity of the 1st and 2nd year university math/science courses. The S-STEM BEATS projectbuilds upon prior NSF S-STEM and STEP projects lessons and practices which proved S-STEMscholars will thrive best when embedded and engaged in an academic innovation ecosystem whichallows students to benefit from the support talents and
, including approximately 3,000 graduate students. Roughly 75% of the graduate studentpopulation identifies as White, 7% as Hispanic, 2% as Black/African American, and 2% as Asian.U.S. citizens constitute 93% of the graduate population, and 38% of the graduate populationattends full-time. There are two populations of participants in this study. The first population is the studentssupported by the scholarships (SEGSP), hereafter, referred to as scholarship participants (S).The second population is comprised of graduate students in the College of Engineering notsupported by the SEnS-GSP, hereafter, referred to as general engineering students (G). Thisresearch took place during the 2020-2021 and 2021-2022 academic years. The demographics ofthe S and G
Southeast Asian woman who at the time of the interview had worked asa mechanical engineer in the U.S. for 11 years. She has held several professional roles in hercareer across the energy and automotive industries. At the time of the interview, she was a crashsafety engineer at a major automotive company.In describing her current role, Radha stressed how important it was to understand the impact herwork was going to have on other parts of the system. In her engineering context of crash safetytesting, this ‘system’ was the entire vehicle and its launch. She described how, “especially asmechanical engineer[s], we’re very prone to look at parts, right?...We are really important, butagain, we’re just part of it.” She identified the dynamic between
participant connected via a videoconferencing program such asSkype.Student FeedbackAnonymous feedback from students was solicited using an online survey. Questions includedone demographic question (year in school), 19 Likert-scale questions and 4 open endedquestions. The Likert questions and their responses are shown in Table 1. Reverse coding wasnot used in order to be consistent with past uses of the survey. The survey was voluntary so outof 31 possible students, 14 responded.The responses to Q1 – Q5 presented in Table 1 indicate the student’s feelings of being connectedto peers and faculty. Overall the student responses indicate a more secure feeling of connectionwith the S-STEM program faculty than within their individual academic programs (Q1
the American Educational Research Association and American Evaluation Association, in addition to ASEE. Dr. Brawner is also an Exten- sion Services Consultant for the National Center for Women in Information Technology (NCWIT) and, in that role, advises computer science and engineering departments on diversifying their undergraduate student population. She remains an active researcher, including studying academic policies, gender and ethnicity issues, transfers, and matriculation models with MIDFIELD as well as student veterans in engi- neering. Her evaluation work includes evaluating teamwork models, broadening participation initiatives, and S-STEM and LSAMP programs.Mr. Russell Andrew Long
(Institute of Transportation Engineers), v 83, n 7, p 22-26, July 2013.3. Gibson, I., Rosen, D., and Stucker, B. (2015). Additive Manufacturing – 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. 2nd Edition, Springer, 2015.4. 3D Printers. (n.d.). Retrieved January 31, 2018, from http://www.stratasys.com/3d- printers.5. Panda, S. K. (2009). Optimization of Fused Deposition Modelling (FDM) Process Parameters Using Bacterial Foraging Technique. IIM Intelligent Information Management, 01(02), 89-97. Retrieved March 18, 2016.6. Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C. B., Zavattieri, P. D. (2015). The status, challenges, and future of additive manufacturing in