coe-staff: Speaker Series

Tue Oct 22 10:52:45 PDT 2019

Dear Faculty, Staff, Students,
This week and next marks a period of high activity for our college's Fall Speaker Series. See below for dates and details. Three speakers will be visiting the college to share their insights and expertise in the areas of Data Science and Teacher Preparation. We hope you are able to join us for their colloquia. RSVPs were included on the notices that went out so that we can monitor attendance, but you do not need to RSVP to attend. We look forward to seeing you there.
Faculty, please share broadly with your students and discuss any arrangements you may make regarding their attendance and alignment with coursework.
Best,
Dianna
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Dr. Sung-Woo Cho: Artificial Intelligence and Social Policy Decision-Making: A Cultural Transformation
 Abstract: This interactive presentation will discuss the recent applications of artificial intelligence on large amounts of non-traditional forms of data in education and public policy, and how these applications can significantly enhance traditional forms of research. Natural language processing (NLP), a form of artificial intelligence, can determine themes in bodies of text (i.e., words) and categorize items according to those themes. These items can include research articles, site visit notes, entire websites, and social media posts. Predictive analytics, another form of artificial intelligence, can predict outcomes to often high accuracy and generalizability when enough data are trained and tested. Abt Associates is using both NLP and predictive analytics to more efficiently and thoroughly analyze current streams of data, and this colloquium will present applications from several different research areas, including education policy. Dr. Cho will also discuss broader cultural shifts in data science that are currently occurring in the greater policy research industry.
 Bio: Sung-Woo Cho, PhD, is the Co-Director of the Digital Transformations Center and head of the Applied Innovations in Artificial Intelligence (AI)2 group at Abt Associates. His main areas of interest are in artificial intelligence applications in policy decision-making, research design and evaluation, and workforce training, particularly within community colleges. He created and oversees the Abt Data Science Fellowship, a company-wide initiative that trains employees in machine learning programming using Python, with a focus on natural language processing, predictive analytics, deep learning, and computer vision. Sung-Woo received his PhD in economics and education from Columbia University, and his B.A. in public policy and economics from Stanford University. He lives in the Washington, DC area with his wife, daughter, and dog.
Colloquium
When: October 23, 2019,  2:30-3:45 p.m.
Where: HEDCO 220,  College of Education
Student Seminar
When: October 24, 2019, 10:00-11:00 a.m.
Where: HEDCO 230T, College of Education
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Dr. Constance A. Lindsay: The Effects of Principal-Teacher Demographic Matching on Teacher Turnover in North Carolina
Abstract: A growing awareness of the influential role that teachers of color play for minority students has sparked interest in the efficacy of various strategies to improve the recruitment and retention of minority teachers. Given the already low representation of teachers of color in schools serving large proportions of black and Latino students, understanding the correlates of teacher attrition is key to developing supportive education policies to minimize teacher departure in those schools serving at-risk populations. This study uses a linear probability model with school fixed effects to investigate the role of principal-teacher demographic matching in the likelihood of teacher turnover decisions. We find evidence that being race-matched with a principal leads to lower rates of teacher turnover. Impacts are larger for teachers of color, compared to white teachers. We do not find a relationship between being gender-matched with a school leader and whether a teacher changes jobs over this time period. Furthermore, we report insignificant findings associated with a gender match. Policy implications are discussed.
 Bio: Dr. Constance A. Lindsay is an Assistant Professor of Education Leadership at UNC Chapel Hill. Lindsay earned a doctorate in human development and social policy from Northwestern University, where she was an Institute of Education Sciences' pre-doctoral fellow. Since leaving Northwestern, Lindsay has worked in education policy in various contexts, applying her research training in traditional studies and in creating and evaluating new systems and policies regarding teachers, and using large administrative datasets. Lindsay's areas of expertise include teacher quality and diversity, analyzing and closing racial achievement gaps, and adolescent development. Her work has been published in such journals as Educational Evaluation and Policy Analysis and Social Science Research. Lindsay received a bachelor's degree in economics from Duke University and master's degree in public policy from Georgetown University. Before doctoral study at Northwestern, she was a Presidential Management Fellow at the US Department of Education.
Student Seminar
When: October 25, 2019, 10:00-11:00 a.m.
Where: HEDCO 230T, College of Education
Colloquium
When: October 25, 2019, 2:30-4:00 p.m.
Where: HEDCO 220, College of Education
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Dr. Perman Gochyyev: Lord's Paradox and Consequences For Effects Of Interventions On Outcomes:
Abstract: The main focus of this talk is on the Lord's paradox within the latent variable modeling framework. Lord (1967) describes a hypothetical paradox in which two researchers, analyzing the same dataset using different but defensible methods, come to very different conclusions about the effects of an intervention on outcomes. Lord's paradox has re-emerged in many causal inference settings today around the issue of when it is appropriate to control for baseline status. Recent applications can be seen in areas such as estimating the local average treatment effect of special education services on student performance; the birth-weight paradox about the relationship between the birth weight and mortality rate of children born to tobacco smoking mothers; and potential over-medicalization of some mental health issues. This talk will discuss two main approaches for analyzing such data: (1) to regress the change from pretest to posttest on the treatment indicator; and (2) to regress posttest on treatment indicator and pretest. The talk will show how these two approaches can yield conflicting results-hence the paradox. I will elaborate on how approaches can be addressed within the structural equation modeling framework and provide examples from my research.
Bio: Perman Gochyyev, PhD, is a behavioral statistician and research center project manager at the University of California, Berkeley in the Berkeley Evaluation and Assessment Research (BEAR) Center. Dr. Gochyyev received his doctorate in Quantitative Methods and Evaluation from UC Berkeley in 2015 in the Policy, Organizations, Measurement & Evaluation (POME) area. His research focuses on latent variable and multilevel modeling, issues related to causal inference in educational and behavioral statistics, multidimensional and ordinal IRT models, and latent class models.
Colloquium
When: October 30, 2019, 12:00-1:45 p.m.
Where: Lokey ED 176, College of Education
Student Seminar
When: October 30, 2019, 3:00-4:00 p.m.
Where: HEDCO 230T, College of Education

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Dianna

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Dianna Carrizales-Engelmann, PhD
Assistant Dean for Administration, College of Education
dcarriza at uoregon.edu<mailto:dcarriza at uoregon.edu> | 541-346-5407
1215 University of Oregon | Eugene, OR 97403-5277

I support all students regardless of immigration status or country of origin. As a Dreamer Ally, I support Dreamer students and promote their sense of belonging and safety as they pursue their higher education goals. For more information and resources please visit our Dreamers<https://www.uoregon.edu/dreamers> page and the Immigration FAQs<https://international.uoregon.edu/immigration_faq> page. I commit to not sharing your status with anyone if you reveal it to me, but also remind you that when interacting with faculty, staff, and offices around campus you are never required to reveal your immigration status.
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