Graduate

Institutions in the United States have been producing PhDs in mathematics education for more than a century. Teachers College at Columbia University and the University of Chicago produced the first graduates in mathematics education in 1906 and 1912, respectively. In those institutions, doctoral students in mathematics education typically took courses along with doctoral students in mathematics. However, the dissertation research took a different direction as doctorates in mathematics education focused on issues related to mathematics learning, teaching, or curriculum.

Research has documented that doctoral preparation in mathematics education varies greatly across institutions of higher education (McIntosh and Crosswhite [13], Reys and Kilpatrick [18], Reys and Dossey [15]). This study reports data gathered from over 500 doctoral graduates in mathematics education from 23 institutions in the USA from 1997 to 2014 as they self-assessed their research preparation during their doctoral program. At least 80% of the doctoral graduates rated their preparation as adequate or very well addressed for qualitative methods and quantitative methods.

There are many paths to becoming an active researcher in mathematics education. This article describes one ideal path. It begins with identifying a useful educational background prior to entering a doctoral program. Then it offers some suggestions for selecting an institution to pursue a doctoral program. The article outlines some academic knowledge and intern experiences that will help prepare the next generation of researchers in mathematics education once a doctoral program has been entered.

Zambak, V. S., & Magiera, M. T. (2015). An Exploratory Analysis of Pre-service Middle School Teachers’ Mathematical Arguments. In T. Bartell, K., Bieda, R. Putnam, K. Bradfield, & H. Dominguez (Eds.). Proceedings of the 37th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. 428, East Lansing, MI: Michigan State University.

We analyzed 37 PSTs’ written solutions to four figural pattern generalization tasks, video recordings of class discussions, and audio-recordings of problem-based interviews during which the PSTs were asked to solve one pattern generalization task, to answer the following research questions: (1) What relationships and structural aspects of a figural pattern do PSTs build upon to formulate pattern generalization? (2) How do they utilize uncovered relationships and structural aspects of a figural pattern to justify their general rules? 

This book focuses on the preparation of secondary science teachers to teach science to English Learners (EL) and is based on the SSTELLA (Secondary Science Education with English Language and Literacy Acquisition) project that has been implemented in four pre-service teacher education programs in Arizona, California and Texas, all states with large populations of EL.

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.

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.