RMME/CEPARE Colloquium

Lessons from the Field: Working with Practitioners to Create Opportunities for Educational Research

Dr. Robert Schoen

Florida State University

Friday, April 19, at 11AM ET

Gentry 144

Dr. Robert Schoen is an associate professor of mathematics education in the School of Teacher Education and the associate director of the Florida Center for Research in Science, Technology, Engineering, and Mathematics in the Learning Systems Institute at Florida State University. Dr. Rob Schoen has directed more than one-dozen randomized controlled trials of educational interventions in applied settings. He will share stories and examples about how he decides what research opportunities to pursue and some of the strategies he has used to support successful implementation of those studies over a long period.

*Please contact Dr. Sarah D. Newton at sarah.newton@uconn.edu for access information to remotely attend this talk*

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RMME/CEPARE Colloquium

Designing a Measure of Implementation for a Non-Prescriptive Mathematics Intervention

Dr. Robert Schoen

Florida State University

Thursday, April 18, at 3PM ET

Gentry 142

Dr. Robert Schoen is an associate professor of mathematics education in the School of Teacher Education and the associate director of the Florida Center for Research in Science, Technology, Engineering, and Mathematics in the Learning Systems Institute at Florida State University. This talk will address the various phases in the development, use, and validation of an instrument designed to measure implementation of Cognitively Guided Instruction (CGI) during mathematics instruction. Several experimental trials of CGI-based teacher professional development programs indicate that the CGI programs increased student achievement. But the CGI programs did not offer clear guidance about how to teach mathematics, complicating the process of measure development and validation.

*Please contact Dr. Sarah D. Newton at sarah.newton@uconn.edu for access information to remotely attend this talk*

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RMME/STAT Joint Colloquium

In Defense of Unrestricted Spatial Regression

Dr. Dale Zimmerman

University of Iowa

Friday, April 12, at 11AM ET

AUST 202

http://tinyurl.com/rmme-Zimmerman

Spatial regression is commonly used in the environmental, social, and other sciences to study relationships between spatially referenced data and other variables, and to predict variables at locations where they are not observed. Spatial confounding, i.e., collinearity between fixed effects and random effects in a spatial regression model, can adversely affect estimates of the fixed effects, and it has been argued that something ought to be done to "fix" it. Restricted spatial regression methods have been proposed as a remedy for spatial confounding. Such methods replace inference for the fixed effects of the original spatial regression model with inference for those effects under a model in which the random effects are restricted to a subspace orthogonal to the column space of the fixed effects model matrix; thus, they “deconfound” the two types of effects. We prove, however, using classical linear model theory, that frequentist inference for the fixed effects of a deconfounded linear model is generally inferior to that for the fixed effects of the original spatial linear model; in fact, it is even inferior to inference for the corresponding nonspatial model (i.e., inference based on ordinary least squares). We show further that deconfounding also leads to inferior predictive inferences. Based on these results, we argue against the use of restricted spatial regression, in favor of plain old (unrestricted) spatial regression. This is joint work with Jay Ver Hoef of NOAA National Marine Mammal Laboratory and was published in 2022 in The American Statistician.

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RMME/STAT Joint Colloquium

Some Recent Developments in Educational and Psychological Measurement

Dr. Zhiliang Ying
Columbia University

Friday, March 29, at 11AM ET

In Person: AUST 202

Virtual: https://tinyurl.com/rmme-Ying

Measurement theory plays a foundational role in educational and psychological assessment. Classical item response theory (IRT) models are widely used in the design and analysis of educational tests and psychological surveys that involve multiple choice questions. In this talk, we will first discuss some recent progress related to variations and extensions of the classical IRT model-based methods. We will then turn to the modeling and analysis of process data arising from complex problem-solving items, which are increasingly being adopted in large scale educational assessment. New developments, including statistical models and machine learning algorithms, will be presented. Examples from educational testing and psychological assessment will be used for illustration.

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