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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.

In this article, we review knowledge about student engagement and look ahead to the future of study in this area. We begin by describing how researchers in the field define and study student engagement. In particular, we describe the levels, contexts, and dimensions that constitute the measurement of engagement, summarize the contexts that shape engagement and the outcomes that result from it, and articulate person-centered approaches for analyzing engagement. We conclude by addressing limitations to the research and providing recommendations for study.

The United States has made a significant effort and investment in STEM education, yet the size and the composition of the STEM workforce continues to fail to meet demand. It is thus important to understand the barriers and factors that influence individual educational and career choices. In this article, we conduct a literature review of the current knowledge surrounding individual and gender differences in STEM educational and career choices, using expectancy–value theory as a guiding framework.

Although the gender gap in math course-taking and performance has narrowed in recent decades, females continue to be underrepresented in math-intensive fields of Science, Technology, Engineering, and Mathematics (STEM). Career pathways encompass the ability to pursue a career as well as the motivation to employ that ability. Individual differences in cognitive capacity and motivation are also influenced by broader sociocultural factors.

Student engagement in math and science is vital to students' academic achievement and long-term participation in science, technology, engineering, and mathematic (STEM) courses and careers. In this study, we conducted in-depth interviews with 106 students from sixth to twelfth grade and 34 middle and high school teachers about how they conceptualized math and science engagement and disengagement. Our qualitative analysis of student and teacher interviews supported the multidimensional construct of engagement outlined in the academic literature.

There is an urgent need to develop appropriate instruments to measure student engagement in math and science for the fields of research and practice. The present study developed and validated student- and teacher-report survey measures of student engagement in math and science. The measures are built around a multidimensional perspective of engagement by using a bifactor modeling approach. The sample was recruited from an ethnically and socioeconomically diverse middle and high school student population in the United States.

This paper describes a model for differentiating professional development to address teachers’ varied knowledge, experiences, and interests.

Brodesky, A., Fagan, E., Tobey, C., & Hirsch, L. (2016). Moving Beyond One-Size-All PD: A Model for Differentiating Professional Learning for Teachers. NCSM Journal of Mathematics Education Leadership, 17(1), 20-37.