Dr. Alex J. Bowers
Yilin Pan is currently a post-doctoral researcher jointly hired by the Department of Educational Policy and Social Analysis and the Department of Organization Leadership at Teachers College, Columbia University. She specializes in cost-effectiveness and cost-benefit analysis, decision-making in resource allocation and Bayesian statistics. Dr. Pan’s research aims to facilitate the utilization of research evidence to better guide policymakers and practitioners' decision-making about resource allocation. She has been working on applying Bayesian statistics to improve the methodologies that generate research evidence. Her attempts focus on localizing evidence of program effectiveness and cost obtained from evaluation settings to reflect the student and teacher characteristics of a specific decision-making setting, the subjective judgments of the local decision makers and the values of the local stakeholders. Yilin Pan earned a B.A. in English Literature and Linguistics and a M.A. in Higher Education at Tsinghua University, P. R. China, and a Ph.D. in Economics and Education at Teachers College, Columbia University. Prior to her current job, she worked as a consultant at the World Bank's education sector.
Kenneth E. Graves is a Ph.D. student in the Education Leadership program at Teachers College, Columbia University, where his current research broadly focuses on content-specific leadership in STEM, particularly on how principals enact technology leadership for social justice. His work also considers ethical and legal issues in technology leadership, leadership for computer science (CS) education, data-driven decision making for school improvement, and quantitative methodologies. Furthermore, his teaching interests lie in preparing future principals and technology leaders for instructional leadership in the digital age in topics such as designing innovative professional learning experiences for teachers, evaluating new technology initiatives, CS teaching methods and pedagogy, as well as using data visualizations for school reporting. Kenny has won several awards for research and teaching, including a 2016-2017 AERA Doctoral Dissertation Research Grant Award for a study where he uses Latent Class Analysis (LCA) with national-level data to investigate the extent to which there is a typology of technology-using teachers and leaders in order to understand the intersection of technology-using teacher and leader types, school contexts, and sociocultural factors in U.S. public schools. Kenny also just completed a summer internship as a Staff Associate at the Data Science Institute at Columbia University, as part of an internship sponsored by the Northeast Big Data Innovation Hub, where he is working with the NYC Foundation for Computer Science Education (CSNYC) and the NYCDOE to produce big data visualizations from the CS4All initiative to assist the organizations with evidence-based improvement. Prior to research, Kenny was an award-winning teacher and school leader in several schools. Kenny holds a M.A. in Instructional Technology and Media from Teachers College, Columbia University and a B.A. in English, Secondary Education, and Latin American/Iberian Studies from the University of Richmond.
April H. Bang is a leadership educator, researcher, and practitioner specializing in adaptive leadership, transformative learning and adult development, systemic change, and collaborative capacity building. She is an advanced doctoral student in the Adult Learning and Leadership program of the Organization and Leadership Department at Teachers College, Columbia University. Her current research integrates her specialized interests and adult learning concentration with leadership and data analytics. Prior to her doctoral studies, she taught leadership to undergraduate students at Yonsei University in Seoul, Korea and has diverse and extensive experience as a practitioner working across the fields of human rights, criminal justice reform, international rule of law development, and economic policy, including work at the Vera Institute of Justice, International Justice Mission, Harvard Kennedy School’s Executive Session on Human Rights Commissions and Criminal Justice, and the Federal Reserve Bank of New York. April is also developing as an artist. With an on-going curiosity to examine and demonstrate how art could foster individual and collective transformation, she has started to exhibit her visual artwork in galleries and has co-curated a community art installation in Harlem. She is passionate about art and social impact, and her experiential and scholarly research on the restorative and transformative power of the arts in conflict resolution led to an article in the Journal of Transformative Education. She is looking forward to learning and contributing as a member of the Education Leadership Data Analytics Research Group and further integrating her varied experiences and interests. She holds an MPP from Harvard Kennedy School, an Ed.M. in Adult Learning and Leadership from Columbia University, and a BA in economics from Smith College.
Lauren Fox is interested in researching how data can be used to understand educational outcomes. She received her B.S. in Mathematics from the University of Maryland, College Park and is currently in the Applied Statistics M.S. program at Teachers College, Columbia University. Within the research group, her work focuses on using statistical methods to understand how school leadership influences students’ decision to choose a career within the fields of science, technology, engineering, or math (STEM).
Elizabeth C. Monroe is a research assistant at Teachers College, Columbia University. She analyzes and draws conclusions from data to inform educators’ decisions with respect to practices and policies. Elizabeth is pursuing a Master of Science degree in applied statistics at Teachers College, Columbia University. She also earned her first master’s degree in elementary inclusive education at Teachers College, Columbia University. During her first master’s degree, Elizabeth observed and assisted in several elementary school classrooms, which sparked her interest in education research. Prior to her masters’ degrees, Elizabeth attended Boston University to study archaeology. She helped excavate a Roman villa in southern Italy and graduated summa cum laude with a Bachelor of Arts degree in archaeology. Elizabeth has always enjoyed research. Starting in middle school, she began creating and conducting her own scientific experiments, and she looks forward to a long career in quantitative analytics, which her diverse background will enable her to approach from unique perspectives.
Luronne Vaval is a Ph.D. student in the Science Education program at Teachers College, Columbia University. She is interested in Physics Education Research (PER), which involves research related to the teaching and learning of physics. More specifically, her work examines identifying student difficulty with representational fluency at the undergraduate level. In the research group, Lou served as a Doctoral Research Fellow during the 2016-17 academic year and will continue her work as a Graduate Assistant during the 2017-18 academic year. Her work in the group centers around investigating different growth or decline trajectories as they relate to STEM high school, college, and early career outcomes using latent class analysis (LCA) and growth mixture modeling (GMM) to. Lou holds a M.S. in Applied Statistics from Teachers College, Columbia University and a B.S. in Physics from the University of Florida.
Xiaoliang Zhou is a third-year doctoral student in the program of Measurement, Evaluation, and Statistics at Teachers College, Columbia University. His research interests include structural equation modeling, longitudal data analysis, diagnostic cognitive modeling, and big data and machine learning techniques. He is also interested in applying these models and skills to analysis of k-12 education data, the reason why he is currently a doctoral research fellow working for Dr. Bowers’ project Using Big Data to Investigate Longitudinal Education Outcomes through Visual Analytics. The objective of the research is to utilize visual techniques in other big data areas to discover STEM (science, technology, engineering, and math) students’ curricula selection and performance patterns, so that we can predict their early career outcomes. Zhou earned his M.A. degree in Linguistics and Literature from Beijing Foreign Studies University in 2008 and his M.S. degree in Applied Statistics from Teachers College, Columbia University in 2016. Previously, he was a full-time professor on academic writing, research methods, reading, and translation and interpretation for five and a half years in Beijing Information and Technology University as well as a part-time English trainer for a decade in 30 language training institutions.