How teachers attend to and interpret positive relational interactions shapes how they enact instructional practices for equity. We draw on frameworks from equitable mathematics instruction, relational interactions, and teacher noticing to conceptualize mathematics teachers' relational noticing. Using noticing interview and classroom observation data from a research collaborative between secondary mathematics teachers and university-based teacher educators, we document the range and diversity of ten teachers' relational noticing. We use this analysis to examine how teachers' relational noticing supports enacting equitable instructional practices. Our findings indicate five themes of teachers' relational noticing that are informed by their personal histories, understanding of dominant narratives of mathematics education, and their local sociopolitical school context. Additionally, teachers enacted a range of practices for creating positive relational interactions, with attending to student thinking being the most enacted practice. Our findings suggest that mathematics teachers' relational noticing can support the three axes of equitable instruction.

On average, women faculty take on more childcare responsibilities, posing barriers to career success. Work-family policies represent one solution for advancing gender equity in academia as they support parents after childbirth with benefits for children, employees, and organizations. We contribute to understanding how the availability and use of dependent care policies (paid parental leave and childcare benefits) relate to long-term research productivity trends. Based on the work-home resources model, we theorize that policy availability provides contextual resources and policy use provides personal resources, leading to improvements in an individual's research productivity after they have a child. We also examine potential gender differences in the effect of dependent care policies on research productivity trends. We leverage n = 6945 yearly top publications and h-index observations for 386 business professors from 108 universities. Consistent with our hypotheses, the availability and use of paid parental leave and childcare benefits were associated with increased research productivity trends, though the effects depend on birth order, policy, and gender to some extent. Our findings have considerable theoretical and practical implications for organizations and society.

Gender disparities persist in postsecondary computing fields, despite improvements in postsecondary equity overall and STEM fields as an aggregate. The entrenchment of this issue requires a comprehensive, longitudinal lens. Building on expectancy-value theory, the present study examines the relationships among students’ gender-ability stereotypes, attainment values, course-taking, and major choices. Using data from the High School Longitudinal Study of 2009 (HSLS: 2009), we applied weighted t-tests and multiple-group structural equation modeling to investigate how motivational beliefs (i.e., gender-ability stereotypes, attainment values) and course-taking patterns in math and science may predict major choice in computing. Overall, we find gender differences in identity-based mathematics and science motivational beliefs have long-term effects. Gender-ability stereotypes in math and science shape attainment values in each domain, whereby stereotypes suppress girls’ attainment values and enhance boys? attainment values (p < 0.001), in turn shaping course-taking and major decisions. Math- and sciencerelated motivational and curricular factors affect “other” STEM more than computing major outcomes. Specifically, computer science course-taking is completed more by boys (d = 0.21), but girls’ chances of declaring computing majors are especially enhanced by completing these courses in high school. Advanced science course-taking and science attainment value positively predict boys’ but not girls’ likelihood of declaring computing majors. We discuss the implications of these findings for research, policy, and practice. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.

Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the understanding of expected learning goals but also related intermediate understandings that are steppingstones on pathways to learning goals. An accurate and nuanced way of interpreting assessment results will allow subsequent instructional actions to be targeted. An appropriate psychometric model is indispensable in this regard. In this study, we developed a new psychometric model, namely, the diagnostic facet status model (DFSM), which belongs to the general class of cognitive diagnostic models (CDM), but with two notable features: (1) it simultaneously models students’ target understanding (i.e., goal facet) and intermediate understanding (i.e., intermediate facet); and (2) it models every response option, rather than merely right or wrong responses, so that each incorrect response uniquely contributes to discovering students’ facet status. Given that some combination of goal and intermediate facets may be impossible due to facet hierarchical relationships, a regularized expectation–maximization algorithm (REM) was developed for model estimation. A log-penalty was imposed on the mixing proportions to encourage sparsity. As a result, those impermissible latent classes had estimated mixing proportions equal to 0. A heuristic algorithm was proposed to infer a facet map from the estimated permissible classes. A simulation study was conducted to evaluate the performance of REM to recover facet model parameters and to identify permissible latent classes. A real data analysis was provided to show the feasibility of the model. © The Author(s), under exclusive licence to The Psychometric Society 2024. corrected publication 2024.

In this article, the Funding information section was missing and should have read ‘The study is funded by IES R305D200015 and NSF EDU-CORE #2300382’. The original article has been corrected. © The Author(s), under exclusive licence to The Psychometric Society 2024.

Assessment is central to teaching and learning, and recently there has been a substantive shift from paper-and-pencil assessments towards technology delivered assessments such as computer-adaptive tests. Fairness is an important aspect of the assessment process, including design, administration, test-score interpretation, and data utility. The Universal Design for Learning (UDL) guidelines can inform assessment development to promote fairness; however, it is not explicitly clear how UDL and fairness may be linked through students’ conceptualizations of assessment fairness. This phenomenological study explores how middle grades students conceptualize and reason about the fairness of mathematics tests, including paper-and-pencil and technology-delivered assessments. Findings indicate that (a) students conceptualize fairness through unique notions related to educational opportunities and (b) students’ reason about fairness non-linearly. Implications of this study have potential to inform test developers and users about aspects of test fairness, as well as educators data usage from fixed-form, paper-and-pencil tests, and computer-adaptive, technology-delivered tests. © The Author(s) 2024.

Integrating microintervention strategies and the bystander intervention model, we examined social cognitive predictors (i.e., moral disengagement, empathy, and self-efficacy) of the five steps of the bystander intervention model (i.e., Notice, Interpret, Accept, Know, and Act) to address racial microaggressions in a sample of 452 racially diverse college students. Data were collected using an online survey. Path analyses showed that moral disengagement was significantly and negatively related to each step of the model for White students, but for students of color, it was only significantly negatively associated with Act. Empathy was significantly and positively associated with Interpret, Accept, and Act for White students. For student of color, however, there was a significant and positive association solely between Empathy and Act. For both White students and students of color, self-efficacy was positively associated with Notice, Interpret, Accept, Know, and Act. Finally, race did not significantly moderate any relationships. Strengths, limitations, future directions for research, and implications of the study findings are discussed.

Argumentation, a key scientific practice presented in the Framework for K-12 Science Education, requires students to construct and critique arguments, but timely evaluation of arguments in large-scale classrooms is challenging. Recent work has shown the potential of automated scoring systems for open response assessments, leveraging machine learning (ML) and artificial intelligence (AI) to aid the scoring of written arguments in complex assessments. Moreover, research has amplified that the features (i.e., complexity, diversity, and structure) of assessment construct are critical to ML scoring accuracy, yet how the assessment construct may be associated with machine scoring accuracy remains unknown. This study investigated how the features associated with the assessment construct of a scientific argumentation assessment item affected machine scoring performance. Specifically, we conceptualized the construct in three dimensions: complexity, diversity, and structure. We employed human experts to code characteristics of the assessment tasks and score middle school student responses to 17 argumentation tasks aligned to three levels of a validated learning progression of scientific argumentation. We randomly selected 361 responses to use as training sets to build machine-learning scoring models for each item. The scoring models yielded a range of agreements with human consensus scores, measured by Cohen’s kappa (mean = 0.60; range 0.38 − 0.89), indicating good to almost perfect performance. We found that higher levels of Complexity and Diversity of the assessment task were associated with decreased model performance, similarly the relationship between levels of Structure and model performance showed a somewhat negative linear trend. These findings highlight the importance of considering these construct characteristics when developing ML models for scoring assessments, particularly for higher complexity items and multidimensional assessments. © The Author(s) 2023.

With growing expectations to use AI-based educational technology (AI-EdTech) to improve students' learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers' willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect it to benefit them versus how many concerns it raises for them. In this study, we surveyed 508 K-12 teachers across six countries on four continents to understand which teacher characteristics shape teachers' trust in AI-EdTech, and its proposed antecedents, perceived benefits and concerns about AI-EdTech. We examined a comprehensive set of characteristics including demographic and professional characteristics (age, gender, subject, years of experience, etc.), cultural values (Hofstede's cultural dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), and psychological factors (self-efficacy and understanding). Using multiple regression analysis, we found that teachers with higher AI-EdTech self-efficacy and AI understanding perceive more benefits, fewer concerns, and report more trust in AI-EdTech. We also found geographic and cultural differences in teachers' trust in AI-EdTech, but no demographic differences emerged based on their age, gender, or level of education. The findings provide a comprehensive, international account of factors associated with teachers' trust in AI-EdTech. Efforts to raise teachers' understanding of, and trust in AI-EdTech, while considering their cultural values are encouraged to support its adoption in K-12 education.