Mathematics

Background Inclusive STEM (traditionally known to stand for “Science, Technology, Engineering, and Math”) high schools are emerging across the country as a mechanism for improving STEM education and getting more and diverse students into STEM majors and careers. However, there is no consensus on what these schools are or should be, making it difficult to both evaluate their effectiveness and scale successful models. We addressed this problem by working with inclusive STEM high school leaders and stakeholders to articulate and understand their intended school models.

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 language that students use with whole numbers can be insufficient when learning integers. This is often the case when children interpret addition as “getting more” or “going higher.” In this study, we explore whether instruction on mapping directed magnitudes to operations helps 88 second graders and 70 fourth graders solve addition and subtraction problems with negative numbers.

In this study, we explore four, second graders’ performances on integer addition problems before and after analyzing contrasting cases involving integers. The students, as part of a larger study, participated in a pretest, small group sessions, one short whole-class lesson on integer addition, and a posttest. Based on their integer mental models and scores on arithmetic and transfer problems, each student progressed, although in different ways. We use these instances and their interactions in their group sessions to describe their progressions.

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.