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Conference Session
Gender Track - Technical Session VII
Collection
2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference
Authors
Mayari I. Serrano, Purdue Polytechnic Institute; Suzanne Zurn-Birkhimer, Purdue University, West Lafayette (College of Engineering); Rachel Ann Baker
Tagged Topics
Diversity, Gender
impacted my professional development. Professional Impact 3-Neither Agree nor Disagree, 10 Inspired me to emulate the successful women I saw at Inspiration 4-Agree, 5-Strongly Agree the conference.B. Conferences D. Attitudes towards non-technical conference and degree The 38 participants attended 14 different non-technical con- 1) Quantitative Analysis: General statistics and GLM pro-ferences, and those conferences have been grouped according cedure were obtained from the data collected for the questionsto the classification characteristics previously defined
Conference Session
Gender Track - Technical Session I
Collection
2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference
Authors
Maya Rucks, Clemson University; Marisa K. Orr, Clemson University
Tagged Topics
Diversity, Gender
, D., Kilgore, D., Loshbaugh, H., McCain, J., & Chen, H. (2008). Being and Becoming: Gender and Identity Formation of Engineering Students. Research Brief. Center for the Advancement of Engineering Education (NJ1).Flores, L. Y., & O’brien, K. M. (2002). The career development of Mexican American adolescent women: A test of social cognitive career theory. Journal of Counseling Psychology, 49(1), 14.Flynn, M. A., Everett, J. W., & Whittinghill, D. (2016). The Impact of a Living Learning Community on First-Year Engineering Students. European Journal of Engineering Education, 41(3), 331–341.Gibbons, M. M., & Shoffner, M. F. (2004). Prospective first-generation college students: Meeting their needs through
Conference Session
Gender Track - Technical Session I
Collection
2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference
Authors
Nicole Nieto, Ohio State University
Tagged Topics
Diversity, Gender
graduate and undergraduate level III. Examining Interventions a. Implicit Bias: define implicit bias and share examples of how implicit bias is present in technology spaces b. Buckeye Bias Busters Initiative: overview of this training currently being facilitated to employees in technology spaces at Ohio State IV. Conclusion a. Dialogue-Based Discussion: interactive dialogue on inequity in technology spaces b. Next Steps I. Introduction Recent allegations exposing hostile work environments in technology spaces have madeheadlines, bringing to light a culture that is generally unfavorable towards women, people ofcolor and other targeted
Conference Session
Gender Track - Technical Session VI
Collection
2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference
Authors
Lalita G. Oka, California State University, Fresno; Kimberly Stillmaker P.E., California State University, Fresno
Tagged Topics
Diversity, Gender
higher than the malestudents irrespective of the gender of the instructors. The survey questions for qualitative researchon the perception of success for female students are also included.1Assistant Professor, California State University, Fresno2Assistant Professor, California State University, Fresno 1 1. IntroductionLocated in the San Joaquin Valley of central California, Fresno State is home to about 25,000students. Being a state university, affordability is an important criterion for most students. Themajority of the students in engineering come from local high schools and maintain close ties withthe surrounding community. Recent reports
Conference Session
Gender Track - Technical Session III
Collection
2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference
Authors
Tim John Weston, University of Colorado, Boulder; Wendy DuBow, National Center for Women & IT; Alexis Kaminsky, Kaminsky Consulting, LLC
Tagged Topics
Diversity, Gender
about their major and minor at university, and ifrespondents worked at a computer or IT-related job. We coded the responses to major/minor andjob as “persisting” or “not persisting,” using two different dependent variables in our analysis.One was Tech-persister (coded persister/non-persister) using the criteria: 1) majoring orminoring in computer science, information science, or any engineering field (or completing thesemajors) and/or 2) as working in a technical position in the workforce (based upon job title andcompany). We created this broad category because the program and its funders care about femalepersistence in a wide variety of computing-related fields; however, we know many stakeholdersin our research are exclusively interested in