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Supporting Secondary Students in Building External Models is a collaborative project with Michigan State University and the Concord Consortium, funded by the National Science Foundation (NSF) to examine how to support secondary school students in constructing and revising models to explain scientific phenomena and design solutions to problems. This article describes the project and research plans.

Damelin, D., & Krajcik, J. (2016). Supporting secondary students in building external models to explain phenomena. @Concord, 20(1), 10-11.

This brief article provides an overview of how to use the SageModeler systems modeling tool with an ocean acidification model as an example.

Damelin, D. (2016). Monday's lesson: Students making models. @Concord, 20(2), 7.

This study investigated attributes of 278 instances of student mathematical thinking during whole-class interactions that were identified as having high potential, if made the object of discussion, to foster learners’ understanding of important mathematical ideas. Attributes included the form of the thinking (e.g., question vs. declarative statement), whether the thinking was based on earlier work or generated in the moment, the accuracy of the thinking, and the type of thinking (e.g., sense-making).

This study investigated attributes of 278 instances of student mathematical thinking during whole-class interactions that were identified as having high potential, if made the object of discussion, to foster learners’ understanding of important mathematical ideas. Attributes included the form of the thinking (e.g., question vs. declarative statement), whether the thinking was based on earlier work or generated in the moment, the accuracy of the thinking, and the type of thinking (e.g., sense-making).

This study investigated attributes of 278 instances of student mathematical thinking during whole-class interactions that were identified as having high potential, if made the object of discussion, to foster learners’ understanding of important mathematical ideas. Attributes included the form of the thinking (e.g., question vs. declarative statement), whether the thinking was based on earlier work or generated in the moment, the accuracy of the thinking, and the type of thinking (e.g., sense-making).

Recently, there has been an increase in the number of cluster randomized trials (CRTs) to evaluate the impact of educational programs and interventions. These studies are often powered for the main effect of treatment to address the ‘‘what works’’ question. However, program effects may vary by individual characteristics or by context, making it important to also consider power to detect moderator effects. This article presents a framework for calculating statistical power for moderator effects at all levels for two- and three-level CRTs.

Mathematics professional development is widely offered, typically with the goal of improving teachers’ content knowledge, the quality of teaching, and ultimately students’ achievement. Recently, new assessments focused on mathematical knowledge for teaching (MKT) have been developed to assist in the evaluation and improvement of mathematics professional development. This study presents empirical estimates of average program change in MKT and its variation with the goal of supporting the design of experimental trials that are adequately powered to detect a specified program effect.

We develop a theoretical and empirical basis for the design of teacher professional development studies. We build on previous work by (a) developing estimates of intraclass correlation coefficients for teacher outcomes using two- and three-level data structures, (b) developing estimates of the variance explained by covariates, and (c) modifying the conventional optimal design framework to include differential covariate costs  so as to capture the point at which the cost of collecting a covariate overtakes the reduction in variance it supplies.

The credibility of conclusions made about the effectiveness of educational interventions depends greatly on the quality of the assessments used to measure learning gains. This essay, intended for faculty involved in small-scale projects, courses, or educational research, provides a step-by-step guide to the process of developing, scoring, and validating high-quality content knowledge assessments. We illustrate our discussion with examples from our assessments of high school students’ understanding of concepts in cell biology and epigenetics.

Career aspirations in science, technology, engineering, and mathematics (STEM) are formulated in adolescence, making the high school years a critical time period for identifying the cognitive and motivational factors that increase the likelihood of future STEM employment. While past research has mainly focused on absolute cognitive ability levels in math and verbal domains, the current study tested whether relative cognitive strengths and interests in math, science, and verbal domains in high school were more accurate predictors of STEM career decisions.