On April 15, 2024, Pamela Peters successfully defended her doctoral dissertation, entitled, “Gifted Identification Matrices: An Exploratory Study of the Measurement Considerations”. We are so thrilled to celebrate this outstanding accomplishment by a fellow RMME Community member! Congratulations, Dr. Peters!!!
RMME PhD alumna, Dr. Jessica Flake, presents her “Measurement, Not Schmeasurement” talk at the 2024 annual meeting of the Association for Psychological Science, in San Francisco, California. Join Dr. Flake on Thursday, May 23, at 9am, for this outstanding presentation!
Join RMME Community members for multiple outstanding research presentations at the 2024 Wallace Research Symposium on Talent Development. This year, the Wallace Symposium will be hosted on the University of Connecticut’s main campus, in Storrs, CT (and the home of RMME Programs). So, remember to join us for these excellent presentations with RMME Community members this month!
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RMME is growing! We are currently seeking qualified instructors to teach graduate-level courses in Research Methods, Educational Measurement, Quantitative Methods, and Evaluation. Connecticut residents are especially encouraged to apply! Interested candidates should apply here.
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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Members of the RMME Community will share their work in a variety of different research presentations at the 2024 annual meetings of the American Educational Research Association (AERA) and the National Council on Measurement in Education (NCME). Be sure to check out these awesome RMME Community sessions in Philadelphia, PA this month!
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Current RMME Master’s student (and Giftedness, Creativity, and Talent Development PhD student), Lihong Xie, gave an excellent guest presentation at Harvard University this March. Lihong visited Harvard’s Child/Adolescent Cognitive & Psychological Assessment class to speak and lead an engaging conversation about assessing youth intelligence and creativity.
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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