Paper ID #38853Work in Progress: Using the Formative Assessment Enactment Model toCharacterize Instructor Moves in a Learning-Assistant SupportedMechanics CourseIsabella Stuopis, Tufts University PhD Candidate in Mechanical Engineering at Tufts University (May 2023). Interests: undergraduate engineering education, undergraduate learning, learning outside of the classroom setting, collaboration in engineering, learning assistants, student discourseDr. Kristen B. Wendell, Tufts University Kristen Wendell is Associate Professor of Mechanical Engineering and Adjunct Associate Professor of Education at Tufts University
engineering, statistics, and business to improve how we design and construct our built environment while sustaining our natural environment. Recently, Dr. Ibrahim has been passionately interested in education research.Dr. Gustavo B. Menezes, California State University, Los Angeles Menezes is a Professor of Civil Engineering at Cal State LA. His specialization is in Environmental and Water Resources Engineering. Since becoming part of the faculty in 2009, Menezes has also focused on improving student success and has led a number of ©American Society for Engineering Education, 2023 A Measurement of Systemic STEM Educational Wellness at a Minority- Serving Institution using the Eco-STEM Educational
Paper ID #42845Visuospatial and Embodied Cognition in STEM Education: A SystematicLiterature ReviewMrs. Fadhla B. Junus, Purdue University Fadhla Junus, a third-year Ph.D. student at Purdue University’s School of Engineering Education, brings a unique blend of industry experience and academic expertise to her research on technology-enhanced learning, specifically in developing personalized learning environments for higher-education computer programming students. She is interested in investigating how students learn computer programming, how to make learning computer programs easier, what theories support designing
Paper ID #43285Board 72: Discourse Moves and Engineering Epistemic Practices in a VirtualLaboratorySamuel B. Gavitte, Tufts University A PhD student at Tufts University working with Dr. Milo Koretsky and Dr. Jeff Nason to research engineering epistemic practices in the context of virtual and physical lab activities.Dr. Milo Koretsky, Tufts University Milo Koretsky is the McDonnell Family Bridge Professor in the Department of Chemical and Biological Engineering and in the Department of Education at Tufts University. He is also co-Director of the Institute for Research on Learning and Instruction (IRLI). He received his B.S
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
Paper ID #44171Evaluation of the Effect of Anonymous Grading on Student Performance onHigh-Stakes AssessmentsDr. Neha B. Raikar, University of Maryland, Baltimore County Dr. Raikar is a Senior Lecturer at the University of Maryland, Baltimore County, in the Chemical, Biochemical, and Environmental Engineering department. She has taught both undergraduate and graduate-level courses. Dr. Raikar also has 3 years of industry experience from working at Unilever Research in the Netherlands.Dr. Nilanjan Banerjee Nilanjan Banerjee is an Associate Professor at University of Maryland, Baltimore County. He is an expert in mobile and
Ramirez, Purdue University Nichole Ramirez is the Assistant Director of the Vertically Integrated Projects (VIP) program at Purdue University. She holds a Ph.D. in Engineering Education from Purdue University. She is also an involved member of NAMI at the local and state levels. She advises NAMI on Campus Purdue and helped launch Ending the Silence, a NAMI Signature program for the state of Indiana.Dr. Douglas B. Samuel My research focuses on the development of dimensional trait models of mental health problems and their application in clinical practice.Mr. Syed Ali Kamal, University at Buffalo, The State University of New York Syed Ali Kamal is a doctoral student at the Department of Engineering Education at
Paper ID #37229Work in Progress: Emotional Configurations in Undergraduate EngineeringEducationEmily Kostolansky, Tufts University Emily Kostolansky is a master’s student in mechanical engineering at Tufts University. Her research inter- ests in engineering education include undergraduate engineering education and emotions in engineering.Dr. Kristen B Wendell, Tufts University Kristen Wendell is Associate Professor of Mechanical Engineering and Adjunct Associate Professor of Education at Tufts University. Her research efforts at at the Center for Engineering Education and Out- reach focus on supporting discourse and design
received his PhD degree in Mechanical Engineering from Texas A&M University in 2001. He is the Director of the NSF NRT-LEAD program and a Professor in the School of Mechanical and Materials Engineering at Washington State University.David B. Thiessen, Washington State University David B.Thiessen received his Ph.D. in Chemical Engineering from the University of Colorado in 1992 and has been at Washington State University since 1994. His research interests include fluid physics, acoustics, and engineering education. ©American Society for Engineering Education, 2024Enhancing Engineering EducationEnhancing Engineering Education: A Comparative Analysis of Low-Cost Desktop Learning Module
design techniques enhances engineers understanding of users’ needs. 2. Bairaktarova, D. (2022). Caring for the future: Empathy in engineering education to empower learning. 3. Bernárdez, B., Durán, A., Parejo, J. A., Juristo, N., & Ruiz–Cortés, A. (2022). Effects of Mindfulness on Conceptual Modeling Performance: A Series of Experiments. 4. Carbonetto, T., & Grodziak, E. M. (2019, July 28). Mindfulness in Engineering v2. 5. Estrada, T., & Dalton, E. (2019). Impact of Student Mindfulness Facets on Engineering Education Outcomes: An Initial Exploration. 6. Hess, J. L., Beever, J., Strobel, J., & Brightman, A. O. (2017). Empathic Perspective- Taking and Ethical Decision-Making in
across all disciplines.ASEE 2024 Educational Research and Methods (ERM) Division References[1] W. Lu & B. Zoghi, “Designing a professional master’s program to build life-long successfulskills for engineering managers,” In 13th annual International Conference of Education,Research and Innovation, November 9-10, 2020. [Online]. Available:doi:10.21125/iceri.2020.1794. [Accessed February 4, 2024].[2] M. White, “A Brief History of Generative AI,” January 2023. [Online]. Available: Medium,https://matthewdwhite.medium.com/a-brief-history-of-generative-ai-cb1837e67106. [AccessedFebruary 4, 2024].[3] “A Guide to the Engineering Management Body of Knowledge, 5th edition,”ASEM.org.[Online]. Available: ASEM
Experiences of Non-traditional Students: A perspective from higher education. Studies in Continuing Education, 57-75.Bohl, A. J., Haak, B., & Shrestha, S. (2017). The Experiences of Nontraditional Students: A Qualitative Inquiry. The Journal of Continuing Higher Education, 166-174.Carpenter, N. E., & Pappenfus, T. M. (2009). Teaching Research: A Curriculum Model That Works. Journal of Chemistry Education, 940-945.Carpi, A., Ronan, D. M., Falconer, H. M., & Lents, N. H. (2016). Cultivating minority scientists: Undergraduate research increases self-efficacy and career ambitions for underrepresented students in STEM. Journal of Research in Science Teaching, 169-194.Ditta, A. S., Strickland-Hughes, C. M., Cheung, C
responses:I have a good understanding of the concept of specific heat.The student responses are tabulated below. Table 2 Student responses to the survey question Likert scale responses pre-lab post-lab A strongly agree 21 33 B agree 24 19 C neutral 10 3 D disagree 0 0 E strongly disagree 0 0 sum 55 55 agree % (A+B)/sum 82% 95% improvement 13%The percentage of students agreeing with the survey
Paper ID #37892Examining Engineering Students’ Shift in Mindsets Over the Course of aSemester: A Longitudinal StudyDr. Dina Verdin, Arizona State University, Polytechnic Campus Dina Verd´ın, PhD is an Assistant Professor of Engineering in the Ira A. Fulton Schools of Engineering at Arizona State University. She graduated from San Jos´e State University with a BS in Industrial Systems Engineering and from Purdue University with an MS in Industrial Engineering and PhD in Engineering Education. Her research interest focuses on changing the deficit base perspective of first-generation col- lege students by providing asset
multiple-choice selection but also their explanation and response to follow-up questions—to a conceptualstatics question compare across diverse institutional contexts? To address this overall question,we ask more specifically: a. How are student correctness, confidence, and their metacognitive reflections on the question related to their institution? b. What do the student responses suggest about their epistemological frames in learning statics? MethodsQuestion AdministrationFor this study, we selected one concept question which was administered via the ConceptWarehouse [29] (ConcepTest #4606), as shown in Figure 1. The question was delivered to 241students at six
generates compare estimates than the Bayesian method for some modeling parameters,the Bayesian approach produces substantially improved results for the standard deviationestimates of the relationship effect (𝜎𝑟 ), the autoregressive coefficient of the relationship effect(𝛽𝑟 ), the correlation between target and perceiver effects (𝜌𝑝𝑡 ), and the correlation betweenreciprocal ratings (𝜌𝑟 ). All our qualitative conclusions from Panel A holds for Panel B as well.Nevertheless, when the overall sample size has increased, the differences between the Bayesianand SR-SEM methods become smaller, due to the impact of the prior distribution beingweakened with a larger sample.Table 3Simulation Results Panel A: 15
Paper ID #44003Latino/a/x Engineering Students and Nepantla: A Multi-Case Study withinthe US SouthwestDr. Joel Alejandro Mejia, The University of Texas at San Antonio Dr. Joel Alejandro (Alex) Mejia is an Associate Professor with joint appointment in the Department of Biomedical Engineering and Chemical Engineering and the Department of Bicultural-Bilingual Studies at The University of Texas at San Antonio. His research has contributed to the integration of critical theoretical frameworks in engineering education to investigate deficit ideologies and their impact on minoritized communities, particularly Mexican Americans
.Cuevas, J. (2015). Is learning styles-based instruction effective? A comprehensive analysis of recent research on learning styles. Theory and Research in Education, 13(3), 308-333. https://doi.org/10.1177/1477878515606621Fauziah, H. & Cahyono, B. Y. (2022). Prevalent beliefs in learning styles myths: Indonesian research trends on learning styles. Issues in Educational Research, 32(4), 1384-1402. http://www.iier.org.au/iier32/fauziah.pdfFelder, Richard & JE, Spurlin. (2005). Applications, reliability, and validity of the Index of Learning Styles. International Journal of Engineering Education. 21. 103-112.Felder, Richard & L.K. Silverman, “Learning and Teaching Styles in Engineering Education,” Engr
. Bira, J. B. Gastelum, L. T. Weiss, and N. L. Vanderford, “Evidence for a mental health crisis in graduate education,” Nat Biotechnol, vol. 36, no. 3, pp. 282–284, 2018, doi: 10.1038/nbt.4089.[2] J. Cornwall, E. C. Mayland, J. Van Der Meer, R. A. Spronken-Smith, C. Tustin, and P. Blyth, “Stressors in early-stage doctoral students,” Studies in Continuing Education, vol. 41, no. 3, pp. 363–380, Sep. 2019, doi: 10.1080/0158037X.2018.1534821.[3] T. John and P. Denicolo, “Doctoral Education: A Review of the Literature Monitoring the Doctoral Student Experience in Selected OECD Countries (Mainly UK),” Springer Science Reviews, vol. 1, no. 1–2, pp. 41–49, Dec. 2013, doi: 10.1007/s40362-013-0011-x.[4] F. A. Huppert, “Challenges
Paper ID #43631Teaching Online Engineering: A Systematic Literature ReviewYoula Ali, University of Oklahoma Youla Ali, a Junior majoring in Computer Science at the University of Oklahoma, currently serves as a Research Assistant in the Engineering Pathways program under the mentorship of Dr. Javeed Kittur for the academic years 2023-2024. Her research focuses on online engineering education, driven by her desire to understand the challenges that instructors face when transitioning course components, such as experiments and labs, to remote formats. As an engineering student herself, Youla aims to offer valuable insights
] K.J. Chapman, M. Meuter, D. Toy, and L. Wright, "Can’t we pick our own groups? Theinfluence of group selection method on group dynamics and outcomes," Journal of ManagementEducation, vol. 30, pp. 557-569, 2006.[12] S.A. Myers, "Students’ perceptions of classroom group work as a function of group memberselection," Communication Teacher, vol. 26, pp. 50-64, 2012.[13] S.A. Rusticus and B.J. Justus, "Comparing student- and teacher-formed teams on groupdynamics, satisfaction, and performance," Small Group Research, vol. 50, pp. 443-457, 2019.[14] B. Rienties, P. Alcott, and D. Jindal-Snape, "To let students self-select or not: That is thequestion for teachers of culturally diverse groups," Journal of Studies in International Education,vol. 18, pp
-test scores can serve as a covariate to eliminate the initialdifference between groups, thereby making pre-tests equivalent across groups. In our study, wecontrolled for the influence of the pre-scores on the writing assessments, which allows us toreduce the effect of draft scores on the difference in post-writing scores between the two groups.We first validated the three assumptions of the MANCOVA: a) homogeneity of the regressionslopes, b) multivariate normality, and c) equality of the covariance matrices. The MANCOVAmet the assumption of homogeneity of regression slopes because the interaction effects were notsignificant. The Shapiro-Wilk test of multivariate normality yielded a low p-value (<0.001), butthe Central Limit Theorem ensured
) Scanning the area and (b) an example of a processed virtual model.Selecting Energy Audit Measures to Evaluate: To conduct energy audits of buildings, there aremany energy-consuming systems that energy auditors need to be aware of, understand how theyoperate, and be able to identify energy, cost, or emissions savings for. For this research, threemain energy-consuming systems are selected. These include lightning, plug loads (smallappliances plugged into wall outlets), and heating, ventilation, and air conditioning (HVAC).These topics were chosen because these are three of the energy-consuming systems for whichenergy recommendations are most frequently recommended throughout the history of energyaudits within the IAC program [31]. The specific
NS 0.404 a Chemer, et al. 0.51 b 0.10 b NS 0.24 b NS 0.11 b Model 1 = Complete instrument (28 Items), Model 3 = Reduced instrument (12 Items) a p<0.001, bp<0.01, cp<0.05, NS = Not SignificantConclusion A shortened version of a survey instrument, based on the Mediation Model of ResearchExperiences (MMRE) theoretical framework was developed and evaluated for use in a datadriven, proactive advising process. Items for the shortened instrument were drawn from twosources, with slight differences in wording between questions on the two instruments for thesame underlying constructs. Results from this work indicate that the source instruments aremeasuring somewhat different definitions
, practitioner papers, and conference proceedings. The aim is to ensure that articles meet quality standards as determined by others in the engineering and education communities. 4. The article was focused on instruction in a formal classroom setting for grades 6 - 8. 5. The article has an explicit connection to engineering teaching or learning.A search was conducted on February 15, 2022. All resulting citations were exported toCovidence, a web-based software for systematic reviews [12]. Covidence removed anyduplicates. Articles were reviewed for relevance, and eligibility criteria were applied. Twoauthors screened the same subset of articles (n=32) for a Kappa coefficient of 0.904 [13]. Thescreening process, shown in Appendix B, resulted
High engagement No/weak conceptual Strong conceptual change change2.3 Purpose of the StudySince the seminal work of Posner and the follow up study by Pintrich, several studies haveexamined the roles of cognitive, motivational, and affective factors on knowledge revision. Thiscurrent study aims to synthesize findings from these various studies to determine the variablesthat influence conceptual change and their relative effectiveness. Specifically, this systematicreview aims to achieve the following objectives: a. Identify the main categories of factors that predict conceptual change or knowledge revision. b. Identify the main categories of factors that
study.The analyses described above were shared among the institutions at the project kick-off. Further-more, two initial research questions were posed: • How do the structural complexities of curricular pathways differ among various top engi- neering (within and between) disciplines? (a) (b)Figure 3: (a) The structural complexities of the 18 undergraduate aerospace engineering considered inthis study, with the three participating schools highlighted. (b) The structural complexities of differentundergraduate engineering programs, by discipline, were collected from 20 different universities. The dotsin the figure correspond to outliers
). Learning to Conduct “Team Science” through Interdisciplinary Engineering Research. In 2016 ASEE Annual Conference & Exposition. 3. Knight, D. B., Davis, K. A., Kinoshita, T. J., Twyman, C., & Ogilvie, A. M. (2019). The Rising Sophomore Abroad Program: Early Experiential Learning in Global Engineering. Advances in Engineering Education. 4. Jesiek, B. K., Shen, Y., & Haller, Y. (2012). Cross-cultural competence: A comparative assessment of engineeringstudents. International Journal of Engineering Education, 28(1), 144. 5. https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting- engineering-programs-2022-2023/ 6. Miskioglu, E. (2018, June). It takes all kinds
0.05. The grades for midterm 1, midterm 2 and thefinal exam, as well as the overall course grade, were compared between cohorts. Box plots of thedata used for each test are shown in Fig. 1. All test results are summarized below in Tab. 2. a) 100 b) 100 80 80 60 60 40 40 20 20 0 0 Interactive Traditional Interactive Traditional c) 100 d
Paper ID #43879Generative Artificial Intelligence in Undergraduate Engineering: A SystematicLiterature ReviewMr. Hudson James Harris, University of Oklahoma Hudson Harris is a first-year biomedical engineering student at the University of Oklahoma. Fascinated by the potential implications of artificial intelligence (AI) in the coming years, Hudson authored this paper to capture a snapshot of current research on generative AI within undergraduate engineering. This work aims to serve as a foundational resource for ongoing academic discourse and future developments. Hudson’s interest in the intersection of AI and