Through building models of student thinking, researchers have identified quantitative reasoning as a foundation for students' mathematical development. This has generated a need to support teachers' capacity to teach for such reasoning. In this paper, we discuss a perspective on supporting prospective and practicing teachers in constructing meanings that foreground quantitative reasoning and research-based models of student thinking. We call this perspective competing meanings and describe it as a three-phase cognitive process. First is a perturbation of an extant meaning resulting from its enactment. Second is the construction of an alternative meaning to reconcile the perturbation. Third are acts of reflection to compare extant and alternative meanings. We introduce the competing meanings perspective and illustrate it with examples. We also discuss theoretical resources that inform this perspective, including Piagetian constructs, quantitative reasoning, and mathematical knowledge for teaching.

Abstract Parents' science, technology, engineering, and mathematics (STEM) support is theorized to influence adolescents' choices to pursue STEM majors and careers (Eccles & Wigfield, 2020). Theory posits that parents' STEM support is situated within the immediate contexts and, thus, is shaped by both parents' beliefs about education and STEM as well as adolescents' achievements and interests (Eccles & Wigfield, 2020), but the current empirical evidence testing these theoretical tenets remains limited. Using data from the nationally representative High School Longitudinal Study (HSLS; n = 12,070; M age = 14.37; 50% female adolescents; 57% White, 16% Latine, 10% Black, 8% Asian, and 10% other ethnic adolescents), this paper examined two parent-level factors (i.e., parents' educational expectations for adolescents and their math/science parenting self-efficacy) and two adolescent-level factors (i.e., adolescents' math/science interests and math achievement) to understand what predicts parents' STEM support in 11th grade, controlling for parents' STEM support in 9th grade. In addition, we examined the extent to which parents' STEM support in 11th grade predicted adolescents' STEM college major, controlling for other major predictors. Parents' math/science parenting self-efficacy beliefs positively predicted the relative changes in parents' STEM support from 9th to 11th grade, which positively predicted adolescents' STEM college major. Additionally, the parents of female and male continuing-generation college students provided more STEM support and had higher educational expectations for their adolescents than parents of first-generation college students. This study provides insights for policymakers and practitioners to focus on the potential precursors of parental socialization of STEM choices during high school and college.

Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a "codeless" and free ML neural network building software - Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to their daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts.