College of Education and Human Ecology
This level of training is appropriate for individuals who won’t have primary responsibility for designing forms, rather their role will be to enter and edit data. Also for project coordinators/ program managers who will oversee data collection.
Please click here to register. A calendar invite will be sent after registration. Slides will be distributed to all registered participants 12 hours before training time.
This level of training is appropriate for individuals who have used REDCap and are comfortable with building basic forms, or have attended or watched Design 1 training. This session reviews more advanced features of building forms such as piping, embedding, branching logic, the basics of calculated fields, action tags, longitudinal data collection, randomization, and more!
Please click here to register. A calendar invite will be sent after registration. Slides will be distributed to all registered participants 12 hours before training time.
Hosts: Katherine Miller, The Concord Consortium; Seth Jones, Middle Tennessee State University; Kirsten Daehler, WestEd; David Weintrop, University of Maryland; and Chad Dorsey, The Concord Consortium
The goal of this workshop session is to invite early career researchers interested in data science education (DSE) into the DSE community. The session includes information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders. Participants share nascent research ideas and receive feedback and mentoring. While all members of the DSE research community are welcome to attend, the session focuses on resources and support for those who are early in their research careers and seeking to build a career in studying and supporting DSE at all educational levels—but particularly at the K-12 level.
This workshop will consist of a beginner friendly introduction to the Python programming language for data visualization. We will explore the pros & cons of using Python and showcase examples & resources. Those with or without programming experience are encouraged to attend! Join us to learn more about visualizing data using the Python programming language, so that you can visualize yourself as a future Python user!
This workshop will consist of a beginner friendly introduction to the R programming language for data visualization. We will explore the pros & cons of using R and showcase examples & resources. Those with or without programming experience are encouraged to attend! Join us to learn more about visualizing data using the R programming language, so that you can visualize yourself as a future R user!
A symposium featuring faculty research supported by The Ohio State University Department of Biomedical Informatics, Center for Biostatistics, Secondary Data Core, curated for an audience of clinicians and researchers. Research professionals will present on their experience and overall expertise with using secondary data to answer important questions throughout multiple areas of study.
Title: University Rankings Systems and Their Effects on Admissions Indicators and Reputation
Dr. Nicholas Bowman
Nicholas A. Bowman is the Mary Louise Petersen Chair in Higher Education, professor in the Department of Educational Policy and Leadership Studies, senior research fellow in the Public Policy Center, and director of the Center for Research on Undergraduate Education at the University of Iowa. His scholarship uses a social psychological lens to explore key topics in higher education, including student success, diversity and equity, rankings, admissions, and quantitative methodology. Bowman’s work has appeared (or is currently in press) in 100 peer-reviewed journal articles, over 25 book chapters, and nearly 150 peer-reviewed conference presentations. This research has been published in outlets such as Review of Educational Research, Educational Researcher, Sociology of Education, Social Psychological and Personality Science, and Science Advances. Bowman received the Promising Scholar/Early Career Award from the Association for the Study of Higher Education in 2012, and he was the University of Iowa Scholar of the Year in 2018.
Learn more about Dr. Bowman by visiting his webpage, view his Curriculum Vitae, and the Iowa University Public Policy Center.
Irwin Epstein, Professor Emeritus at the City University of New York (CUNY) and at the Silberman School of Social Work (Hunter College), will present on the principles, methods, limitations and varied uses of CDM while showing were it “fits” in the broader context of the evidence-based practice and practice-informed research in social work and related fields. Epstein will illustrate how CDM has been employed in practitioner-initiated studies, DSW and PhD dissertations conducted in social work and allied-health settings. He’ll encourage participants to consider ways in which available data can be used in research and describe previous “flirtations” between CDM and RCT methods. Finally, Epstein will announce the birth of a new clinical research paradigm parented by CDM and RCT.
As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists–and others who rely on data in their work–to ignore. But it is precisely this power that makes it worth asking: “Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from what we call data feminism, a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought.
With so many types of charts or graphs to choose from, how do you determine the most effective one to use to share your data? We will explore various families of charts – beyond the simple bar or line chart – that will allow you to compare categories, explore change over time, and examine distribution. We will also show you how to make these charts in Excel and Tableau.
