The purpose of this study was to investigate the math and science beliefs of underrepresented students, with a focus on first-generation college (FGC) students. We do so by estimating a typology of high school students based on their mathematics and science beliefs and examining which group(s) students were more likely to be in. We used latent profile analysis and a nationally representative sample of high school students from the High School Longitudinal Study of 2009. We identified four groups, low-all, medium-low science/medium-high math, medium-high science/medium-low math, and high-all. Among these four groups, FGC students were overrepresented among students in the low-all group, which had negative math and science beliefs and the worst math, science, and STEM (science, technology, engineering, and mathematics) outcomes. Our findings have for research on FGC students as well as for practitioners who work with them.

The purpose of this study is to investigate the extent that there is a typology of high schools based on their orientation toward science, technology, engineering, and mathematics (STEM), as well as the extent to which school-level demographic variables and student high school outcomes are associated with subgroup membership in the typology, by analyzing data from a large nationally representative sample of high schools (n = 940) from the High School

Research Problem. Computer science (CS) education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing. Research Question. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students? Methodology. We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using the CAPE theoretical dissaggregation framework to measure schools’ Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS. Data Collection Procedure. We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators ( n = 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators ( n = 185), analyzed the data, and considered how these findings shape research conducted over the last year. Findings. Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases. Practitioner perspectives also indicated that schools serving students from lower-income families had 1) a greater decrease in the number of students who received information about CS/CTE pathways; 2) a greater decrease in the number of girls enrolled in CS classes; 3) a greater decrease in the number of students receiving college credit for dual-credit CS courses; 4) a greater decrease in student attendance; and 5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses.

Despite the importance of developing elementary science teachers' content knowledge for teaching (CKT), there are limited assessments that have been designed to measure the full breadth of their CKT at scale. Our overall researchassessments, content knowledge for teaching, elementary science, matter and its interactions, pedagogical content knowledge High-quality science instruction requires that science teachers leverage their content knowledge for teaching (CKT) in the work of teaching (National Research Council, 2012; NGSS Lead States, 2013). CKT refers to the professional knowledge that teachers draw upon as they engage in the work of teaching in a specific discipline (Loewenberg Ball et al., 2008). The main focus here is on the application-or use-of their knowledge in the actual tasks of teachingfor example, the knowledge that teachers use when they prepare for, enact, and reflect on their instruction with students. CKT includes both subject matter knowledge, as well as other forms of practice-based knowledge that are directly tied to the work of teaching-what is commonly referred to as teachers' specialized and pedagogical content knowledge. In the area of science this includes knowledge about how students think about, engage in, and learn about specific scientific practices and concepts, as well as knowledge about various instructional strategies and tools science teachers can draw upon to develop students' learning (Carlson et al., 2019;Mikeska et al., 2020;Schneider & Plasman, 2011).In science education, research has indicated that CKT, particularly science teachers' pedagogical content knowledge, can be quite nuanced and variable across subjects, topics, concepts, and even knowledge components (e.g., knowledge of student ideas vs. knowledge of instructional strategies) (

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