Paper ID #36929Exploring the Viability of Agent-Based Modeling to Extend QualitativeResearch: Comparison of Computational PlatformsSamantha Splendido, Pennsylvania State University, University Park Sam Splendido is a Ph.D. student in Mechanical Engineering at Pennsylvania State University. She is cur- rently a graduate research assistant under Dr. Catherine Berdanier in the Engineering Cognitive Research Laboratory (ECRL). She earned her B.S. in Biomedical and Mechanical Engineering from Pennsylvania State University.Catherine G. P. Berdanier, Pennsylvania State University Catherine G.P. Berdanier is an Assistant
Matthew West is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign.Dr. Geoffrey L. Herman, University of Illinois at Urbana - Champaign Dr. Geoffrey L. Herman is the Severns Teaching Associate Professor with the Department of Computer Science at the University of Illinois at Urbana-Champaign.Prof. Timothy Bretl, University of Illinois at Urbana-Champaign Timothy Bretl is a Severns Faculty Scholar at the University of Illinois at Urbana-Champaign, where he is both Professor and Associate Head for Undergraduate Programs in the Department of Aerospace En- gineering. He holds an affiliate appointment in the Coordinated Science Laboratory, where he leads a re
graduates [5].However, despite extensive research about how to promote change in undergraduate STEMeducation, systematic change has been limited [6], [7]. Many change initiatives and models thathave been utilized to study and promote change have failed to achieve the adoption of research-based instructional practices at universities [8], [9]. Similar trends in research have beenidentified within engineering education [10]. Thus, it is evident that alternative and more holisticways to understand and support change are needed.The COVID-19 pandemic created a real-world laboratory to explore what instructional practicesand strategies were changed and sustained when instructors were forced to use new instructionalmethods under uncertain situations
):6, 2011.[10] Evelyn Brister. Disciplinary capture and epistemological obstacles to interdisciplinary research: Lessons from central african conservation disputes. Studies in history and philosophy of science part C: studies in history and philosophy of biological and biomedical sciences, 56:82–91, 2016.[11] Nancy J Nersessian. The cognitive-cultural systems of the research laboratory. Organization Studies, 27(1): 125–145, 2006.[12] Lisa M Osbeck, Nancy J Nersessian, Kareen R Malone, and Wendy C Newstetter. Science as psychology: Sense-making and identity in science practice. Cambridge University Press, 2010.[13] Helen E Longino. The fate of knowledge. In The Fate of Knowledge. Princeton University Press, 2018.[14] Nicola
STEM education for future researchers. He is currently participating in an NSF-funded grant (#1923452) to spearhead research into middle school students’ digital literacies and assessment. Recently, Dr. Hsu has received a seed grant at UML to investigate how undergradu- ate engineering students’ digital inequalities and self-directed learning characteristics (e.g., self-efficacy) affect their learning outcomes in a virtual laboratory environment during the COVID-19 pandemic. Dr. Hsu’s research interests include advanced quantitative design and analysis and their applications in STEM education, large-scale assessment data (e.g., PISA), and engineering students’ perception of faculty en- couragement and
Paper ID #42732Evaluating ChatGPT’s Efficacy in Qualitative Analysis of Engineering EducationResearchDr. Xiaorong Zhang, San Francisco State University Dr. Xiaorong Zhang is an Associate Professor in Computer Engineering in the School of Engineering at San Francisco State University (SFSU). She is the Director of the Intelligent Computing and Embedded Systems Laboratory (ICE Lab) at SFSU. She has broad research experience in human-machine interfaces, neural-controlled artificial limbs, embedded systems, and intelligent computing technologies. She is a recipient of the NSF CAREER Award to develop the next-generation
the semester? RQ 2b. To what extent do the students’ IH scores correlate with accuracy of a self- reported knowledge survey?Engineering Education ContextThis study was conducted over two semesters in a junior level undergraduate course titled“Control Systems and Instrumentation”. This is a core course in a general engineeringundergraduate program that covers a large swath of topics pertaining to electrical, computer,and control systems. In a discipline specific degree program, these topics are traditionallyspread across many courses such as Circuits, Analog Electronics, Digital Electronics, PowerSystems, Embedded Systems, and Controls, many of which often have a separate associatedlab. This is a hands-on course and laboratory
, 1971). By acquiring multiple sources of information about the sameevent occurring in a social setting, researchers can integrate and triangulate these data, enhancingthe analysis’ depth and accuracy. Therefore, in this research project, the researcher engaged inextensive first-hand observation in classroom settings throughout the semester, collectedstudents’ written responses reflecting their class, and conducted open-ended interviews designedto validate our findings with students’ perspectives. Second, investigations of instructors’ pedagogical practices in naturalistic settings, versusin a laboratory or through lab-based experiments, can yield different findings (Le Compte &Goetz, 1982). Indeed, identifying instructor’s
course for students in class who would benefit 3.72 0.94 -0.54 -0.12 from additional academic support Harnessing the Power of Technology 3.56 1.0427 I can create short video messages/lectures 3.88 0.97 -0.76 0.1828 I can deliver online classes effectively 3.56 1.02 -0.60 0.0529 I can create a course website using free resources like Canvas, 3.86 1.07 -0.82 0.08 Google Classroom, Edmodo, etc.30 I can effectively use virtual labs in lectures 3.15 1.07 -0.09 -0.7131 I can effectively use virtual labs in laboratory courses 3.19 1.08 -0.17 -0.6832 I can implement interactive
(2013) found that ethical philosophy training was effective for adolescent students,but by the time students reach post-secondary engineering education that ship has largely alreadysailed. Meanwhile, academic integrity practice is taking place under new circumstances as well;Lesage et al. (2024) recently looked at generative artificial intelligence for both laboratory reportprose and for computer code in the mechanical engineering education context and found thatwhile it had the potential to reduce barriers for students, it also posed questions about the longer-term integrity of academic assignments.Measuring the Measurement ProblemYet, while academic integrity incidents can be readily assessed (many institutions, including theauthor’s, keep
. In addition, I work on Human-Computer Interaction and how it might allow us to interact with virtual worlds and robots. I enjoy collaborating with colleagues in other fields where I get to combine CS with Biology or Physics and play with their data. Topics of interest include: Flipped Classroom techniques to teach programming The benefits of games and puzzles in learning Construction of fair, scalable assessments Multimodal teaching with an emphasis on getting students to articulate their understanding 3D-Shape reconstruction and analysis The use of Embedded Systems and Machine Learning to automate (Biology) Laboratory tasks.Liberty Rose Lehr, Smith CollegeRahul Simha, The George Washington UniversityMichelle
University of Missouri. As a researcher in the postsecondary ©American Society for Engineering Education, 2024 Paper ID #44078Science, Technology, Engineering, and Mathematics (STEM) education space, Ymbar has focused onexamining STEM culture’s influence on racially and ethnically minoritized students with Dr. Terrell R.Morton and the Justice and Joy Research Team.Currently, Ymbar is conducting research for the National Renewable Energy Laboratory (NREL) andthe Department of Energy (DOE), alongside Andrew Parker and Dr. Greses P´ rez, to enable equity
,experiential learning, particularly through laboratory courses, emerged as their preferred method.Second, a strong emphasis on necessity appeared to drive their data skill development, suggestingstudents may not always actively seek out these opportunities.5.1.1 Experiential LearningThe students primarily saw experiential learning as the key method for developing their data skills.They emphasized the importance of hands-on experience and iterative learning in a community-driven environment, where real-world challenges and collaborative projects serve as thefoundation for developing true data proficiency. MAE students highlighted that individual practicewas a significant factor in developing data proficiency. While not said explicitly, when it comesto
science,” Stud. Hist. Philos. Sci. Part A, vol. 56, pp. 1–10, Apr. 2016, doi: 10.1016/j.shpsa.2015.10.006.[13] L. M. Osbeck and Nersessian, Nancy J., “Epistemic Identities in Interdisciplinary Science,” Perspect. Sci., vol. 25, no. 2, pp. 226–260, 2017, doi: 10.1162/POSC_a_00242.[14] E. Brister, “Disciplinary capture and epistemological obstacles to interdisciplinary research: Lessons from central African conservation disputes,” Stud. Hist. Philos. Sci. Part C Stud. Hist. Philos. Biol. Biomed. Sci., vol. 56, pp. 82–91, Apr. 2016, doi: 10.1016/j.shpsc.2015.11.001.[15] N. J. Nersessian, “The Cognitive-Cultural Systems of the Research Laboratory,” Organ. Stud., vol. 27, no. 1, pp. 125–145, Jan. 2006, doi
Tech- nical State University (2018). She is an Assistant Professor and Program Director of Information Sci- ence/Systems in the School of Library and Information Sciences at North Carolina Central University, Lab Director for the Laboratory for Artificial Intelligence and Equity Research (LAIER), Co-Director for the Center fOr Data Equity (CODE), an AAAS IF/THEN ambassador, and an Office e-Learning faculty fellow at North Carolina Central University. Her research focuses on utilizing machine learning to identify sources of misinformation on social media and on improving fault detection in autonomous vehicles. Dr. Grady advocates increasing the number of women and minorities in computer science. She believes that
careergoals: “I plan to pursue a PhD in biomedical engineering in the areas of tissue engineering andregenerative medicine. I ultimately hope to pursue a position in a research laboratory in industry,specifically in the pharmaceutical industry.”Students’ self-positioning as research assistants and agentic positions occurred over time inprevious examples when reflecting on their research experience. They prioritized theirresponsibilities as research assistants and recognized their research identity development throughgaining and practicing skills in order to be a better engineer or in an engineering research-relatedposition.DiscussionOur results demonstrate students took up varied positions when reflecting on technical work andresearch experience, and
Paper ID #36928Synthesizing Indicators of Quality across Traditions of NarrativeResearch MethodsMr. Kanembe Shanachilubwa, Pennsylvania State University Fourth-year doctoral student at Pennsylvania State University in the mechanical engineering department. Member of the Engineering Cognitive Research Laboratory (ECRL). Current research topics include grad- uate school attrition and persistence.Catherine G. P. Berdanier, Pennsylvania State University Catherine G.P. Berdanier is an Assistant Professor in the Department of Mechanical Engineering at Penn- sylvania State University. She earned her B.S. in Chemistry from The
Paper ID #37742Addressing the Needs of Hispanic/Latino(a) Students with the FlippedClassroom ModelDr. Alberto Cureg Cruz, California State University, Bakersfield Dr. Cruz is an Associate Professor of Computer Science, Principal Investigator of the Computer Per- ception Laboratory (COMPLAB), and board member of the Center for Environmental Studies (CES) at the California State University, Bakersfield (CSUB). He received a few grants from the National Science foundation and local agencies to support work in applied machine learning and engineering education.Dr. Amin Malek, California State University, Bakersfield Professor
Paper ID #41514Use of Theories in Extended Reality Educational Studies: A Systematic LiteratureReviewDr. Kimia Moozeh, Queen’s University Kimia Moozeh is a research associate at Queen’s university in Engineering Education. Her PhD dissertation at University of Toronto explored improving the learning outcomes of undergraduate laboratories. Her research interests are lab-based learning, online learning and metacognition.Dr. Paul Cameron Hungler P.Eng., Dr. Paul Hungler is an assistant professor in the Department of Chemical Engineering and Ingenuity Labs at Queenˆa C™s University. Prior to starting his current position, Major
Anti-Mirroring Related Alter Position Alter Alter Position Alter Subcodes Alter Gender Alter Gender Type of Type of Support SupportFindings & DiscussionProfessors and FacultyWitnessingOne of the simplest and most common ways professors and faculty witnessed nonbinaryengineering students was by respecting their preferred pronouns; respecting students’ pronounsis especially impactful due to the structural positions faculty hold in the laboratory and classroomsettings. Leon, Zayn, and Gwen Douglas shared experiences where they were happy that theirprofessors gendered them
research laboratory groups; andothers noted that they know they “should” go but when events come up they just don’t attend. Worryingly,some students expressed the sentiment that because they’re only there for two years, its not “worth it” tobuild a new friend base, seeing these “extra” things as purely social and not part of their technical progressand success.Faculty Behaviors and Departmental Support.This theme is potentially the most valuable theme from the paper, pulling together how the challenges andthe types of support can be enacted by faculty. The four categories of behaviors from Posselt’s frameworkare: Visibility, Responsiveness, Downplaying Status, and Cultivating Trust. We did not see explicitinstances of “Downplaying status” from the
[23]. Many racing simulators, including TORCS, display sensors andparameters while the agent is on the track to capture its velocity, speed, Rotations Per Minute(RPM), lap time, etc... (More details are provided later in theme IV). This helps users to bettervisualize what’s going on and see things that need to be adjusted. Observing these parameters helpsthe user to better understand the cause-and-effect relationships of certain components andalterations, helping to identify mistakes and see where to improve [15], [24-25]. The simulatoressentially becomes a virtual laboratory for engineers to experiment on, learning from mistakes,and moving forward without any risk of action. This not only gives engineers a betterunderstanding of AI/ML, but