Gender and participation in mathematics and further mathematics A-levels: a literature review for the Further Mathematics Support Programme

In preparing this report I have considered evidence from over 60 documents that relate to raising girls’ participation in mathematics. These include published research papers and reports compiled by expert bodies that present an evidence base. Although research specifically addressing Further Mathematics A-level is rare, the last ten years have seen considerable efforts to synthesise and update knowledge from different research perspectives about the relationship between gender and participation. For this reason, the review process started with papers from 2008 onwards

How Much Do You Love Grey’s Anatomy?

Does the prominence of medical Television Shows impact people’s decision to pursue a medical career? We are constantly told that the things we see in media and entertainment influences how we think about things, but not much research has been done regarding its ability to impact major life choices such as career choice. This paper investigates this by examining the causal relationship between television and interest in the pursuit of a medical career. An ordinary least squares model, created using time series data over the last 24 years, tested specifically the effect of the number and quality of medical television shows both on the rate of change in medical school applications for men and women and the proportion of female applicants. This study found that while medical tv shows don’t have a statistically significant impact on application rates for men or women, female-driven shows cause a larger proportion of the applicant pool to be female

Predictors of Adolescents’ Interest in Stem Majors and Careers

Advanced Research Winner 2019: The United States currently faces a shortage of qualified workers in fields related to science, technology, engineering, and math (STEM). The first critical step in preventing the labor shortage in STEM is understanding the factors that guide adolescents toward STEM pursuits. Drawing on Eccles’ expectancy-value theory (EVT), the current study aims to identify factors that are relevant to adolescents’ interest in STEM majors and careers. Data were collected from 629 adolescents (Mage = 16.09). Participants attended a high school in northern California and predominantly identified as Asian American (82% of the sample). Preliminary analyses revealed that adolescent boys had higher STEM self-expectancies than did adolescent girls, whereas there was no gender difference in STEM values. Consistent with expectations, multiple regression demonstrated that STEM self-expectancies and values accounted for a significant amount of variance in participants’ interest in STEM majors and careers. STEM value was an especially strong predictor; adolescents tended to be most interested in STEM pursuits when they were also high in STEM value. Moderation analyses showed that the association between STEM value and interest in STEM majors and careers was stronger for girls than for boys. As a whole, this study’s findings suggest that valuing and enjoying STEM pursuits during high school could be an important antecedent of pursuing a STEM major and a STEM career later in life

Female Underrepresentation in STEM Subjects: A Study of Female High School Students in China

In Chinese education, there is a conflict between girls’ socialized gender norms and the values and pedagogies that are incorporated in the science curriculum. These are influenced at both the micro- and macro-level of institutions and often discourage female students from choosing science courses, pushing them into social science instead. In order to ensure that female students have the same opportunities to reach their potential in science areas, it is important to understand the factors that influence their choices. This study investigates the potential reasons why female high school students in China are less likely to choose STEM subjects and direction. By conducting qualitative interviews with six female students, in two Chinese high schools, who have been enrolled in social sciences courses, the study discovers several factors, including the influence of current sciences curriculum, teachers, labor market, parents, and peers, behind their decision-making behaviors. The findings of this study enrich existing research on gender equity in science community by exploring the gender issue in Chinese high school education and provide a direction for future research on this topic while informing policies that can address gender disparities in science programs

The Influence of Selected Personal and Academic Demographic Characteristics on Persistence to Degree Completion Among Traditional-Age Undergraduate Students at a Research University in the Southeastern United States

A college education has never been more important than it is today. With nearly two-thirds of jobs in the future requiring some form of advanced education, and approximately one-third of those requiring a bachelor’s degree, it is imperative that research be undertaken to address the factors that contribute to the success of students in college, more so regarding the persistence and retention of those students. Therefore, the purpose of this study was to determine the influence of selected personal and academic demographic characteristics on the persistence to graduation, specifically, within six years. A causal-comparative research design was used as the format for the study, and discriminant analysis was the technique utilized to analyze the research data. A relatively large extant data set provided by the university was used and all participants were anonymous due to all individual identifiers being removed. Results of the study revealed two substantively significant factors for persistence to graduation within six years of initial enrollment: first semester GPA and credit hours earned by the students in their first semester of enrollment. This study concluded that students who performed better academically and attained higher GPAs in their first semester of college enrollment were more likely to persist to graduation. In addition, it was concluded that students earning more credit hours in that initial semester are more likely to graduate than those students who carry lighter course loads. It is highly recommended by this researcher that there be a monitoring process for students to be assessed multiple times throughout the first semester of college as well as their first full academic year by the university to facilitate persistence to graduation for the student. It is further recommended that the university establish a base of required courses that all incoming students be required to take a minimal number of hours with comparable general education courses in order to monitor their progress. Being proactive during a student’s initial enrollment can provide an avenue for a successful journey to graduation

Using Random Forests to Describe Equity in Higher Education: A Critical Quantitative Analysis of Utah’s Postsecondary Pipelines

The following work examines the Random Forest (RF) algorithm as a tool for predicting student outcomes and interrogating the equity of postsecondary education pipelines. The RF model, created using longitudinal data of 41,303 students from Utah\u27s 2008 high school graduation cohort, is compared to logistic and linear models, which are commonly used to predict college access and success. Substantially, this work finds High School GPA to be the best predictor of postsecondary GPA, whereas commonly used ACT and AP test scores are not nearly as important. Each model identified several demographic disparities in higher education access, most significantly the effects of individual-level economic disadvantage. District- and school-level factors such as the proportion of Low Income students and the proportion of Underrepresented Racial Minority (URM) students were important and negatively associated with postsecondary success. Methodologically, the RF model was able to capture non-linearity in the predictive power of school- and district-level variables, a key finding which was undetectable using linear models. The RF algorithm outperforms logistic models in prediction of student enrollment, performs similarly to linear models in prediction of postsecondary GPA, and excels both models in its descriptions of non-linear variable relationships. RF provides novel interpretations of data, challenges conclusions from linear models, and has enormous potential to further the literature around equity in postsecondary pipelines

Lost female talent: gender differences in college aspirations and expectations in Germany

Our study focuses on the gender gap in college aspirations and enrolment among high school students in Germany. We build on socialisation theory, rational choice theory, and formal restrictions to college access to explain gender differences in idealistic college aspirations, realistic college expectations, and the disparities between the two. Specifically, we examine the prevalence of 'pessimistic' college expectations, where college aspirations are higher than expectations, which we expect to be more likely among young women than young men. By analysing survey data from 1,766 upper secondary students in Germany, we find that women are equally interested in pursuing higher education as their male counterparts. They even express higher aspirations for college enrolment. However, women are more pessimistic than men about realising their aspirations. While factors such as the subjective probability of success and perceived costs impact both genders, young women are also affected by formal restrictions limiting entry to their preferred fields of study

AP STEM Course-taking and College STEM Major Selection: An Examination of the Relationship and How It Differs by Gender and Race/Ethnicity

The United States must expand the STEM pipeline in order to meet the growing demand of the STEM workforce and maintain our nation’s prosperity and competitiveness in the global economy. The urgency of this need has been proclaimed by policymakers, business leaders, politicians, and educators. Despite the growing demand for STEM professionals, women and minorities are an underutilized source of intellectual capital that can and should be tapped into to meet the demand. Doing so creates equity across genders and racial/ethnic groups as well as fosters inclusion of more diverse perspectives to enhance STEM innovations. Efforts to expand the number and diversity of those in STEM fields need to start early on in students’ academic careers. The purpose of this study was to examine the relationship between Advanced Placement (AP) STEM course-taking in high school and selection of college STEM major and to determine whether the relationship differs across racial/ethnic groups and male and female students. This study was designed to help educators and policymakers shape college preparation programs and policies as well as to counsel students during their course selection process in high school. A two-level logistic regression model with fixed effects was utilized to determine the relationship between AP STEM course-taking and STEM major selection, controlling for all relevant student-level and school-level variables. Missing data was accounted for through multiple imputations. Sensitivity testing was also conducted to examine whether exposure to AP STEM courses versus number of AP STEM courses matters in the model explaining STEM major selection. Lastly, the analysis also included a series of interaction effects tests, examining the variation of gender and racial/ethnic differences in STEM major selection as a function of AP STEM course-taking. The sample for this study is taken from the High School Longitudinal Study of 2009 and includes students who were high school freshmen in fall 2009. Data was collected on these students during fall of their freshman year of high school in 2009, during the spring of 11th grade in 2012, and in the spring of 2016, three years after the majority graduated from high school. Findings indicate that gender, STEM course credits, AP STEM course exposure, math self-efficacy, science self-efficacy, aspiring to a graduate degree or higher, and math SAT score are all significant predictors of STEM major selection. Additionally, the results of the interaction effects test using logistic regression show that the relationship between AP STEM course-taking and STEM major selection varies significantly by gender. More specifically, exposure to AP STEM courses increases the odds of female students selecting a STEM major more significantly than for male students

AP STEM Course-taking and College STEM Major Selection: An Examination of the Relationship and How It Differs by Gender and Race/Ethnicity

The United States must expand the STEM pipeline in order to meet the growing demand of the STEM workforce and maintain our nation’s prosperity and competitiveness in the global economy. The urgency of this need has been proclaimed by policymakers, business leaders, politicians, and educators. Despite the growing demand for STEM professionals, women and minorities are an underutilized source of intellectual capital that can and should be tapped into to meet the demand. Doing so creates equity across genders and racial/ethnic groups as well as fosters inclusion of more diverse perspectives to enhance STEM innovations. Efforts to expand the number and diversity of those in STEM fields need to start early on in students’ academic careers. The purpose of this study was to examine the relationship between Advanced Placement (AP) STEM course-taking in high school and selection of college STEM major and to determine whether the relationship differs across racial/ethnic groups and male and female students. This study was designed to help educators and policymakers shape college preparation programs and policies as well as to counsel students during their course selection process in high school. A two-level logistic regression model with fixed effects was utilized to determine the relationship between AP STEM course-taking and STEM major selection, controlling for all relevant student-level and school-level variables. Missing data was accounted for through multiple imputations. Sensitivity testing was also conducted to examine whether exposure to AP STEM courses versus number of AP STEM courses matters in the model explaining STEM major selection. Lastly, the analysis also included a series of interaction effects tests, examining the variation of gender and racial/ethnic differences in STEM major selection as a function of AP STEM course-taking. The sample for this study is taken from the High School Longitudinal Study of 2009 and includes students who were high school freshmen in fall 2009. Data was collected on these students during fall of their freshman year of high school in 2009, during the spring of 11th grade in 2012, and in the spring of 2016, three years after the majority graduated from high school. Findings indicate that gender, STEM course credits, AP STEM course exposure, math self-efficacy, science self-efficacy, aspiring to a graduate degree or higher, and math SAT score are all significant predictors of STEM major selection. Additionally, the results of the interaction effects test using logistic regression show that the relationship between AP STEM course-taking and STEM major selection varies significantly by gender. More specifically, exposure to AP STEM courses increases the odds of female students selecting a STEM major more significantly than for male students

Exploring Community Colleges in the Stem Education Landscape : development of stem college major choice model

Community colleges are becoming increasingly relevant as an entrance point into postsecondary education for potential science, technology, engineering, and mathematics (STEM) majors. These institutions are also an important education pathway for women and racial/ethnic minorities who are currently underrepresented in the STEM workforce. The purpose of this study is to understand how student demographics, high school, and college experiences influence the entrance of community college students to the STEM college majors. Data from the Education Longitudinal Study of 2002 (ELS: 2002) were used for the study. Students included in the study began their postsecondary education at community colleges. Descriptive and binary logistic regression analyses were performed to examine the impact of student demographics, high school, and college experience on the choice of STEM college major. The findings of this study suggest gender, race/ethnicity, academic preparation, and interest in STEM fields upon entering college influence the choice of STEM major by community college students