Background: To stem the tide of teacher turnover and prevent shortages, teacher turnover interventions and policies often focus on new and novice teachers because evidence suggests that teacher turnover is particularly high among these teachers. In addition, researchers continue to investigate the root causes of the high teacher turnover observed in many low-income, high-minority schools and whether this is due more to school demographics or poor working conditions. Purpose: This article examines New York City Teaching Fellows (NYCTF) teachers' risk of leaving their first school in their first 9 years. It both describes the patterns in leaving and examines how school demographics and school climate predict these leaving patterns. Participants: The study follows 608 teachers: two cohorts of secondary mathematics NYCTF teachers who entered the classroom in New York City in 2006 or 2007. Research Design: This is a quantitative study of survey and retention data that were collected as part of a longitudinal research project on NYCTF mathematics teachers. Data Analysis: We use an event history analysis (including a life table and hazard function graphs) to describe patterns in teachers' timing of leaving their first school. We also use a discrete time hazard model to estimate the relative relationships between the predictors of interest (school demographics and school climate) and teacher turnover. Results: The findings from this study provide evidence against the general hypothesis in the field that teachers leave their first schools at the highest rate during their first 1 to 3 years. Second, we also found that the turnover of alternatively certified teachers who began in low-income, high-minority urban schools was driven by both student demographics and school climate conditions, including teacher collegiality and student behavior. Third, we found evidence to support our hypothesis that teachers' individual perceptions of their school environment are stronger drivers of their turnover compared with the perceptions of their colleagues. Conclusion: The results from this study add to our understanding about the timing of teacher turnover among secondary mathematics NYCTF teachers, illustrating that teacher turnover may remain higher later in beginning teachers' careers than currently assumed. This suggests that teachers in Years 3 to 5 in their careers may be good targets for supports. Our findings support the theory that improving school climate can help retain teachers but also provide a cautionary tale for a complete focus on school climate; stemming teacher turnover may require addressing larger economic forces (e.g., the global trend toward temporary work) and more insidious social forces, such as structural racism and inequality.

This article investigates the implementation of inquiry-oriented instruction in 20 undergraduate mathematics classrooms. In contrast to conventional wisdom that active learning is good for all students, we found gendered performance differences between women and men in the inquiry classes that were not present in a noninquiry comparison sample. Through a secondary analysis of classroom videos, we linked these performance inequities to differences in women's participation rates across classes. Thus, we provide empirical evidence that simply implementing active learning is insufficient, and that the nature of inquiry-oriented classrooms is highly consequential for improving gender equity in mathematics.

This Research Commentary addresses the need for an instrument abstract???termed an Interpretation and Use Statement (IUS)???to be included when mathematics educators present instruments for use by others in journal articles and other communication venues (e.g., websites and administration manuals). We begin with presenting the need for IUSs, including the importance of a focus on interpretation and use. We then propose a set of elements???identified by a group of mathematics education researchers, instrument developers, and psychometricians???to be included in the IUS. We describe the development process, the recommended elements for inclusion, and two example IUSs. Last, we present why IUSs have the potential to benefit end users and the field of mathematics education.

Several studies have attempted to capture and analyze the intersect of self-regulated learning (SRL) behaviors and agency (i.e., control over one's own actions) during game-based learning. However, limited studies have attempted to theoretically ground or analytically evaluate these constructs in appropriate theoretical assumptions that can discuss and aptly analyze SRL. As such, this paper argues that complex systems theory, which refers to SRL as a system that is self-organizing, interaction dependent, and emergent, should be integrated into theoretical models of SRL and be analyzed using nonlinear dynamical systems theory techniques to fully capture how learners' SRL behaviors can be captured and scaffolded during game-based learning. This paper guides future discussions and empirical research to understand how to better scaffold learners' SRL behaviors using restricted agency during game-based learning by: (1) understanding scaffolding SRL during game-based learning; (2) reviewing studies that review the intersection of SRL, agency, and game-based learning; (3) discussing the limitations within the field; (4) defining and defend SRL according to complex systems theory; and (5) discussing the open challenges in theoretically, methodologically, and analytically applying complex systems theory to SRL.

Studies have proven that providing on-demand assistance, additional instruction on a problem when a student requests it, improves student learning in online learning environments. Additionally, crowdsourced, on-demand assistance generated from educators in the field is also effective. However, when provided on-demand assistance in these studies, students received assistance using problem-based randomization, where each condition represents a different assistance, for every problem encountered. As such, claims about a given educator's effectiveness are provided on a per-assistance basis and not easily generalizable across all students and problems. This work aims to provide stronger claims on which educators are the most effective at generating on-demand assistance. Students will receive on-demand assistance using educator-based randomization, where each condition represents a different educator who has generated a piece of assistance, allowing students to be kept in the same condition over longer periods of time. Furthermore, this work also attempts to find additional benefits to providing students assistance generated by the same educator compared to a random assistance available for the given problem. All data and analysis being conducted can be found on the Open Science Foundation website (https://osf.io/zcbjx/).

The rapid growth and development of NLP techniques have resulted in Computer-Based Learning Platforms (CBLPs) leveraging innovative approaches toward automated grading and feedback generation of open-ended problems. Researchers have explored these techniques in driving a varying range of interventions that range from assessing the quality of the work and recommending changes to the answers that can enhance the quality of the responses for students to automated grading and feedback generation of responses for teachers. A crucial aspect of the automated assessment of student response is identifying and addressing fairness and equity issues in an educational context, as academic performance can impact the types of opportunities available to the students. While prior works have conducted posthoc analysis exploring aspects of algorithmic fairness of various models, the assessment of open-ended answers is often subjective. Teachers leverage contextual knowledge such as the perception of the student effort or students' prior knowledge. While such factors exist, it is not obvious how data from the teacher can introduce biases or introduce measurable risks to the fairness and equity of the NLP models. In this paper, we build on our prior analysis of the grading behavior of teachers on open-ended math problems for middle school students and explore possible next steps we can take to expand on our work. First, we propose a simulation study to explore the various risks associated with Human-AI interaction in the automated grading of open-ended problems. Second, we propose an extensive study expanding on our work to generate grades for open responses when a student is anonymized vs. not anonymized.

Social support has a well-documented impact on adolescent educational success. Nonetheless, there has been less focus on the relationship between social supports and educational attainment for Latinas. Using a sample of 138 Hispanic females (ages 25-31) from an ongoing longitudinal National Science Foundation (NSF)-funded study (2004-present), we identified key sources of social support (family, teachers, and peers) and types of social support (emotional, informational, and instrumental) in the educational pipeline of Hispanic females. We also examined the associations between social supports and their educational attainment. Through descriptive analyses, we found that family was perceived to provide the most support followed by peers and teachers in adolescence. Through regression analyses, we found that family support positively predicted their educational attainment. Our findings highlight the importance of family-rendered support for educational attainment while also expanding our understanding of the social support mechanisms for Hispanic females.

The aim of this study was to use sociocultural perspectives to elaborate on Eccles' parent socialization model and create a culturally grounded, multidimensional model of parent support among Mexican-descent families. Given Latinx underrepresentation in science, technology, engineering, and mathematics careers, we focus on science as an important domain in which to study parent support. Using a qualitative approach, this study examines (a) what forms of parent science support do Mexican-descent parents and adolescents perceive as best practices and (b) what are the social, cultural, and contextual barriers parents face and in what ways do parents continue to support their adolescents in science in spite of those barriers? Seventy-four parent (mean age: 40 years; 23% U.S.-born and 77% Mexico-born) and 73 adolescent (mean age: 15 years; 41% female) nterviews were analyzed using inductive and deductive approaches. Findings suggest that parents use traditional and nontraditional culturally grounded forms of support: involvement at home, providing words of encouragement (e.g.,echale ganas), and leveraging resources (e.g., kin support). Participants felt work-related barriers, linguistic barriers, and limited science knowledge shaped parents' support. Results highlight the unique ways parents support their adolescents' science education as well as the need for educators to consider how parents' sociocultural experiences shape their support.

This study examines the extent to which the New York City Teaching Fellows (NYCTF) has delivered on its promise of improving mathematics teacher diversity, preparedness, effectiveness, and retention in hard-to-staff city schools. As a program theory evaluation study, it articulates the theory of action for selective alternative route programs and uses this to evaluate NYCTF's program for secondary mathematics. The analysis draws on longitudinal data from 620 secondary mathematics teachers who began NYCTF in the prior decade. While the results point to potential improvements, it provides evidence that selective programs like NYCTF serve to maintain important gaps in teacher quality that they were designed to address.

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students’ online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. © 2022 Copyright is held by the author(s).