their ability to concretelyevaluate student growth [12], [13], [33]. Direct assessments are complicated by three considerations: validity, reliability, andethical limitations on truly scientific study design. Validity asks: does the assessment measurewhat it is supposed to measure? Reliability asks: can writing be consistently and quantitativelyevaluated by different evaluators? Finally, ethics forbid writing centers from executing theclassic “treatment/no treatment” experimental design: true negative controls would requiredenial of writing center access to students who want it. Due to these three constraints, “thetypical evaluation of writing programs...usually fails to obtain statistically significant results” [34].For this reason
administration.The research protocol of using these institutional data received the approval of the university’sresearch ethics board.4.2 Data Analysis MethodsFor the purposes of the analysis, the variables in the linked data files were grouped into threecategories: (1) student experience; (2) learning outcomes; (3) demographics and background.The details about the variables are included in Appendix A. The missing values in the originaldata sets for those variables constituted a very small proportion, with 7% as the highest. Beforethe data analysis, we imputed variables in the categories of student experiences and learningoutcomes using the median values; and we did not apply any imputation to variables in thecategories of demographics and background.To
interviews to gather qualitative data, enabling acomprehensive understanding of the participants' nuanced experiences [46]. Our interviewprotocol was meticulously designed with a structured framework to ensure consistency andcomparability among responses, drawing from best practices in exploratory qualitativeresearch [48]. It aimed to explore common attributes between innovative individuals andfounders without limiting participants' responses. Ethical approval was obtained fromStanford University's Institutional Review Board, and interviews were conducted via Zoomwith consent for recording. Twenty-six hours of interview recordings were captured andtranscribed, and transcripts were anonymized to ensure confidentiality. More information onthe strategy
Engineering at the University of Toronto. She previously completed her Bachelors in Industrial Engineering also at the University of Toronto. She is passionate about supporting women in Engineering and STEM more broadly, both within and outside of her research. She has held fellowships in Ethics of AI and Technology & Society organizations.Dr. Alison Olechowski, University of Toronto Alison Olechowski is an Assistant Professor in the Department of Mechanical & Industrial Engineering and the Institute for Studies in Transdisciplinary Engineering Education and Practice. She completed her PhD at the Massachusetts Institute of Technology (MIT). ©American Society for Engineering Education
rubrics.MethodsThis paper is part of an ongoing project to investigate how systems thinking can be used incombination with popular threat modeling frameworks like STRIDE to teach and assesscomponent-level and system-level threat modeling to upper-level software engineering students.In this section, we provide an overview of the methods we used in our study. We begin bydescribing the software engineering course where we piloted our study. Next, we discuss our datacollection strategy, introduce the pilot version of our rubric, our data analysis approach (scoringstrategy using our rubric), and ethical considerations.Data collectionTo answer our research question, we collected data on the students’ team projects. In the project,student teams had to deliver the
with her students, inviting community members whowere impacted (many of her students’ relatives) to come present to the class. As a result, the fourth-grade students engaged in the engineering design process to construct and test dam designs withthe community context in mind, grappled with the ethics of engineering, and offered alternativesolutions. This example demonstrates the power of connecting an engineering task to place, localhistory, and community and cultural contexts to increase relevance and importance for students.Other CRED tasks developed by teachers included areas of interest such as: designing a filtrationsystem to improve indoor air quality, developing a severe weather app to be used by teen drivers,creating a model of a
Mechanical Engineering from Bahonar University in Iran.Dr. Sreyoshi Bhaduri, ThatStatsGirl Dr. Sreyoshi Bhaduri is an Engineering Educator and People Research Scientist. She employs innovative and ethical mixed-methods research approaches to uncover insights about the 21st century workforce. Sreyoshi has a doctorate in Engineering Education, and Masters degrees in Applied Statistics (M.A.) and Mechanical Engineering (M.S.), from Virginia Tech. She earned her Bachelors degree in Mechatronics Engineering from Manipal University in India. Sreyoshi has been recognized as a Graduate Academy for Teaching Excellence (VTGrATE) Fellow, a Global Perspectives Program (GPP) Fellow, a Diversity scholar, and was inducted in the
transition- ing to an education-focused career track, Melissa taught at Stanford University, Santa Clara University, and Foothill College. These engagements have included courses within and outside the major, aimed at undergraduates at all years, high school students, and working adults. Melissa is now the Science and Engineering Education Fellow (SEEF) for the Bioengineering department, where she works on broader educational research projects and curricular change. Her work includes trying to better understand and support student development as ethical and quantitative thinkers. Through work with Stanford’s Center for Teaching and Learning (CTL), Melissa has also developed diversity and inclusion content for instruc
: including“specified criteria for success” as they go about defining problems, and planning and carryingout “fair tests in which variables are controlled and failure points are considered to identifyaspects of a model or prototype that can be improved.”6 Another principle for elementary through high school engineering education, accordingto the Committee on K12 Engineering Education, is that it promotes engineering habits of mind.Specifically, the committee referenced the following habits of mind: “systems thinking,collaboration, ethical considerations, creativity, communication and optimism.”7 Optimism“reflects a world view in which possibilities and opportunities can be found in every challengeand an understanding that every technology can
dichotomy of relevant versus irrelevant, or fair versus unfair, frames the feelings of manyengineers when it comes to their treatment of ethics. Unlike many aspects of engineering ethicslooks mostly in hindsight, not at all with innovation. It is usually seen as a reaction to a crisis.This hindsight is framed by topics that were seen as unimportant, the first pillar of Cech’s theoryof disengagement [17]. The final pillar is prevalent in many undergraduate and graduateengineering departments to an extreme measure. Numerous studies have pointed to the need toweed out the weak students from undergraduate programs. This builds on the very foundations ofengineering education as a vocational degree for the brightest students. This overarching concernwith
include the profes- sional formation of engineers, diversity and inclusion in engineering, human-centered design, engineering ethics, leadership, service-learning, and accessibility and assistive-technology.Prof. Brian C. Fabien, University of Washington c American Society for Engineering Education, 2017 Paper ID #19405 Professor Fabien joined the University of Washington in 1993 and is currently the Associate Dean for Academic Affairs in the College of Engineering. His research interests include the kinematics of mecha- nisms, dynamic system analysis and optimization, as well as control system design