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1. Streamlining the Process of Evaluating the Education and Diversity Impacts across Engineering Research Centers

   https://doi.org/10.18260/1-2--35212  

   Zhao, Zhen ; Carberry, Adam ; Cook-Davis, Alison ; Larson, Jean ; Jordan, Michelle ; Barnard, Wendy ; O'Donnell, Megan ; Savenye, Wilhelmina ( January 2020 , American Society for Engineering Education)

   The Engineering Research Centers (ERCs), funded by the National Science Foundation (NSF), play an important role in improving engineering education, bridging engineering academia and broad communities, and promoting a culture of diversity and inclusion. Each ERC must partner with an independent evaluation team to annually assess their performance and impact on progressing education, connecting community, and building diversified culture. This evaluation is currently performed independently (and in isolation), which leads to inconsistent evaluations and a redundant investment of ERCs’ resources into such tasks (e.g. developing evaluation instruments). These isolated efforts by ERCs to quantitatively evaluate their education programs also typically lack adequate sample size within a single center, which limits the validity and reliability of the quantitative analyses. Three ERCs, all associated with a large southwest university in the United States, worked collaboratively to overcome sample size and measure inconsistency concerns by developing a common quantitative instrument that is capable of evaluating any ERC’s education and diversity impacts. The instrument is the result of a systematic process with comparing and contrasting each ERC’s existing evaluation tools, including surveys and interview protocols. This new, streamlined tool captures participants’ overall experience as part of the ERC by measuring various constructs including skillset development, perception of diversity and inclusion, future plans after participating in the ERC, and mentorship received from the ERC. Scales and embedded items were designed broadly for possible use with both yearlong (e.g. graduate and undergraduate student, and postdoctoral scholars) and summer program (Research Experience for Undergraduates, Research Experience for Teachers, and Young Scholar Program) participants. The instrument was distributed and tested during Summer 2019 with participants in the summer programs from all three ERCs. The forthcoming paper will present the new common cross-ERC evaluation instrument, demonstrate the effort of collecting data across all three ERCs, present preliminary findings, and discuss collaborative processes and challenges. The preliminary implication for this work is the ability to directly compare educational programs across ERCs. The authors also believe that this tool can provide a fast start for new ERCs on how to evaluate their educational programs. 

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2. An Inquiry into the Use of Intercoder Reliability Measures in Qualitative Research

   Wilson-Lopez, A. ; Minichiello, A. ; Green, T. ( July 2019 , ASEE Annual Conference proceedings)

   In this theory paper, we set out to consider, as a matter of methodological interest, the use of quantitative measures of inter-coder reliability (e.g., percentage agreement, correlation, Cohen’s Kappa, etc.) as necessary and/or sufficient correlates for quality within qualitative research in engineering education. It is well known that the phrase qualitative research represents a diverse body of scholarship conducted across a range of epistemological viewpoints and methodologies. Given this diversity, we concur with those who state that it is ill advised to propose recipes or stipulate requirements for achieving qualitative research validity and reliability. Yet, as qualitative researchers ourselves, we repeatedly find the need to communicate the validity and reliability—or quality—of our work to different stakeholders, including funding agencies and the public. One method for demonstrating quality, which is increasingly used in qualitative research in engineering education, is the practice of reporting quantitative measures of agreement between two or more people who code the same qualitative dataset. In this theory paper, we address this common practice in two ways. First, we identify instances in which inter-coder reliability measures may not be appropriate or adequate for establishing quality in qualitative research. We query research that suggests that the numerical measure itself is the goal of qualitative analysis, rather than the depth and texture of the interpretations that are revealed. Second, we identify complexities or methodological questions that may arise during the process of establishing inter-coder reliability, which are not often addressed in empirical publications. To achieve this purposes, in this paper we will ground our work in a review of qualitative articles, published in the Journal of Engineering Education, that have employed inter-rater or inter-coder reliability as evidence of research validity. In our review, we will examine the disparate measures and scores (from 40% agreement to 97% agreement) used as evidence of quality, as well as the theoretical perspectives within which these measures have been employed. Then, using our own comparative case study research as an example, we will highlight the questions and the challenges that we faced as we worked to meet rigorous standards of evidence in our qualitative coding analysis, We will explain the processes we undertook and the challenges we faced as we assigned codes to a large qualitative data set approached from a post positivist perspective. We will situate these coding processes within the larger methodological literature and, in light of contrasting literature, we will describe the principled decisions we made while coding our own data. We will use this review of qualitative research and our own qualitative research experiences to elucidate inconsistencies and unarticulated issues related to evidence for qualitative validity as a means to generate further discussion regarding quality in qualitative coding processes. 

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3. Exploring and examining quantitative measures

   Bostic, J. ( October 2016 , Proceedings for the 38th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   The purpose of this working group is to bring together scholars with an interest in examining the research on quantitative tools and measures for gathering meaningful data, and to spark conversations and collaboration across individuals and groups with an interest in synthesizing the literature on large-scale tools used to measure student- and teacher-related outcomes. While syntheses of measures for use in mathematics education can be found in the literature, few can be described as a comprehensive analysis. The working group session will focus on (1) defining terms identified as critical (e.g., large-scale, quantitative, and validity evidence) for bounding the focus of the group, (2) initial development of a document of available tools and their associated validity evidence, and (3) identification of potential follow-up activities to continue the work to identify tools and developed related synthesis documents (e.g., the formation of sub-groups around potential topics of interest). The efforts of the group will be summarized and extended through both social media tools (e.g., creating a Facebook group) and online collaboration tools (e.g., Google hangouts and documents) to further promote this work. 

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4. Work in Progress: Understanding Student Perceptions of Stress as part of Engineering Culture

   Cross, Kelly J. ; Jensen, Karin J. ( January 2018 , American Society of Engineering Education Conference Proceedings)

   High levels of stress and anxiety are common amongst college students, particularly engineering students. Students report lack of sleep, grades, competition, change in lifestyle, and other significant stressors throughout their undergraduate education (1, 2). Stress and anxiety have been shown to negatively impact student experience (3-6), academic performance (6-8), and retention (9). Previous studies have focused on identifying factors that cause individual students stress while completing undergraduate engineering degree programs (1). However, it not well-understood how a culture of stress is perceived and is propagated in engineering programs or how this culture impacts student levels of identification with engineering. Further, the impact of student stress has not been directly considered in engineering regarding recruitment, retention, and success. Therefore, our guiding research question is: Does the engineering culture create stress for students that hinder their engineering identity development? To answer our research question, we designed a sequential mixed methods study with equal priority of quantitative survey data and qualitative individual interviews. Our study participants are undergraduate engineering students across all levels and majors at a large, public university. Our sample goal is 2000 engineering student respondents. We combined three published surveys to build our quantitative data collection instrument, including the Depression Anxiety Stress Scales (DASS), Identification with engineering subscale, and Engineering Department Inclusion Level subscale. The objective of the quantitative instrument is to illuminate individual perceptions of the existence of an engineering stress culture (ESC) and create an efficient tool to measure the impact ESC on engineering identity development. Specifically, we seek to understand the relationships among the following constructs; 1) identification with engineering, 2) stress and anxiety, and 3) feelings of inclusion within their department. The focus of this paper presents the results of the pilot of the proposed instrument with 20 participants and a detailed data collection and analysis process. In an effort to validate our instrument, we conducted a pilot study to refine our data collection process and the results will guide the data collection for the larger study. In addition to identifying relationships among construct, the survey data will be further analyzed to specify which demographics are mediating or moderating factors of these relationships. For example, does a student’s 1st generation status influence their perception of stress or engineering identity development? Our analysis may identify discipline-specific stressors and characterize culture components that promote student anxiety and stress. Our objective is to validate our survey instrument and use it to inform the protocol for the follow-up interviews to gain a deeper understanding of the responses to the survey instrument. Understanding what students view as stressful and how students identify stress as an element of program culture will support the development of interventions to mitigate student stress. References 1. Schneider L (2007) Perceived stress among engineering students. A Paper Presented at St. Lawrence Section Conference. Toronto, Canada. Retrieved from: www. asee. morrisville. edu. 2. Ross SE, Niebling BC, & Heckert TM (1999) Sources of stress among college students. Social psychology 61(5):841-846. 3. Goldman CS & Wong EH (1997) Stress and the college student. Education 117(4):604-611. 4. Hudd SS, et al. (2000) Stress at college: Effects on health habits, health status and self-esteem. College Student Journal 34(2):217-228. 5. Macgeorge EL, Samter W, & Gillihan SJ (2005) Academic Stress, Supportive Communication, and Health A version of this paper was presented at the 2005 International Communication Association convention in New York City. Communication Education 54(4):365-372. 6. Burt KB & Paysnick AA (2014) Identity, stress, and behavioral and emotional problems in undergraduates: Evidence for interaction effects. Journal of college student development 55(4):368-384. 7. Felsten G & Wilcox K (1992) Influences of stress and situation-specific mastery beliefs and satisfaction with social support on well-being and academic performance. Psychological Reports 70(1):291-303. 8. Pritchard ME & Wilson GS (2003) Using emotional and social factors to predict student success. Journal of college student development 44(1):18-28. 9. Zhang Z & RiCharde RS (1998) Prediction and Analysis of Freshman Retention. AIR 1998 Annual Forum Paper. 

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5. EXPLORING AND EXAMINING QUANTITATIVE MEASURES

   Shih, J. ; Bostic, J. ; Carney, M. ; Krupa, E. ( October 2017 , Psychology of Mathematics Education North America)

   The purpose of this working group is to continue to bring together scholars with an interest in examining the use of and access to large-scale quantitative tools used to measure student- and teacher-related outcomes in mathematics education. The working group session will focus on (1) updating the workgroup on the progress made since the first working group at PME-NA in Tucson, Arizona, specifically focusing on the outcomes of the Validity Evidence for Measurement in Mathematics Education conference that took place in April, 2017, in San Antonio, (2) continued development of a document of available tools and their associated validity evidence, and (3) identification of potential follow-up activities to continue this work. The efforts of the group will be summarized and extended through both social media tools and online collaboration tools to further promote this work. 

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