Evidence-based teaching is widely recognized as critical for student learning in science, technology, engineering, mathematics, and medicine (STEMM), yet whether and how faculty develop these instructional practices through their social ties remains less well understood. To illuminate these dynamics, this study applies a social capital lens to investigate the role of faculty teaching-focused social networks. Using mixed-methods social network analysis of survey data from 868 faculty and 83 in-depth interviews, we examine the connection between instructors' personal networks and their use of evidence-based teaching approaches for fostering communication, teamwork, and self-directed learning skills. Quantitative findings suggest that the size, range, and strength of faculty teaching-focused personal networks correlate with their use of evidence-based instructional practices. Complementing these associations, our qualitative analysis details the specific relational mechanisms, objects, and instrumental and expressive returns that faculty perceive as influential. We conclude by discussing implications for institutional policies and faculty development approaches, with findings suggesting that cross-sector professional connections and strategic network cultivation are critical for advancing evidence-based teaching practices in STEMM fields.

Algorithmic predictions are inherently uncertain: even models with similar aggregate accuracy can produce different predictions for the same individual, raising concerns that high-stakes decisions may become sensitive to arbitrary modeling choices. In this paper, we define algorithmic sensitivity as the extent to which arbitrary modeling choices propagate into human decisions: how much a decision outcome shifts when a more favorable versus less favorable algorithmic prediction is presented to the decision-maker for the same individual. We estimate this in a randomized field experiment (n = 19,545) embedded in a selective U.S. college admissions cycle, in which admissions officers reviewed each application alongside an algorithmic score while we randomly varied whether the score came from one of two similarly accurate prediction models. Although the two models performed similarly in aggregate, they frequently assigned different scores to the same applicant, creating exogenous variation in the score shown. Surprisingly, we find little evidence of algorithmic sensitivity: presenting a more favorable score does not meaningfully increase an applicant's probability of admission on average, even when the models disagree substantially. These findings suggest that, in this expert, high-stakes setting, human decision-making is largely invariant to arbitrary variation in algorithmic predictions, underscoring the role of professional discretion and institutional context in mediating the downstream effects of algorithmic uncertainty.

ABSTRACT Background Educational technologies typically provide teachers with analytics regarding student proficiency, but few digital tools provide teachers with process‐based information about students' variable problem‐solving strategies as they solve problems. Utilising design thinking and co‐designing with teachers can provide insight to researchers about what educators need to make instructional decisions based on student problem‐solving data. Objectives This case study presents a collaboration where researchers and teachers co‐designed MathFlowLens, a teacher‐facing dashboard that provides analytics and visualisations about students' diverse problem‐solving strategies and behaviours used when solving online math problems in the classroom. Methods Over several sessions, teachers discussed, mocked up, and were provided with behavioural data and strategy visualisations from students' math problem‐solving that demonstrated the variability of strategic approaches. Throughout this process, the team documented, transcribed, and used these conversations and artefacts to inform the design and development of the teacher tool. Results and Conclusions Teachers discussed and designed prototypes of data dashboards and provided the research team with ongoing feedback to inform the iteration of the tool development.

The developmental processes underlying in-group-favoring math stereotypes (i.e., beliefs that favor one's own gender over another gender in math ability) and their associated motivational benefits in adolescence remain understudied. Guided by developmental intergroup theory and situated expectancy-value theory, the current study examined (a) the reciprocal relations between in-group–favoring math stereotypes and perceived math teacher support; (b) their associations with math motivational outcomes (i.e., math expectancies, math identity, and math GPA); and (c) gender differences in these associations among adolescents within each of four largest racial/ethnic groups in the United States (i.e., Asian, Black, Latine, and White). Using the High School Longitudinal Study of 2009 (N=18,419; Mage=14.53, SD=0.63 in 9th grade), we found that Asian and White girls' female-favoring math stereotypes and their perceived math teacher support reinforced one another from early to late adolescence, but not among other groups. In-group-favoring math stereotypes positively predicted math motivational beliefs among boys across all racial/ethnic groups, but only among Black and White girls. These stereotypes indirectly predicted math GPA via motivational beliefs only for White adolescents. Findings suggest that gender differences in the development of in-group-favoring math stereotypes and the benefits of stereotype lift are not the same across race/ethnicity. Rather, gender norms that vary across different racial/ethnic groups may shape who develops and benefits from in-group-favoring math stereotypes, underscoring the need to consider intersectionality to better capture motivational processes in real-world contexts.

This study examines whether virtual internships serve as a viable alternative to in-person work-based learning opportunities, and, if so, for whom and in which specific dimensions. Drawing on a large-scale survey ( N =11,009) and administrative data collected from 17 U.S. postsecondary institutions during the 2020-2021 academic year, the research compares the experiences of historically marginalized students in virtual and in-person internships to those of their non-marginalized counterparts across access, program quality, and outcomes. Findings from a Latent Profile Analysis indicate that while most virtual interns (86%) participate in programs of comparable quality to in-person formats, this parity breaks down at the extremes, with virtual interns overrepresented in under-resourced experiences and underrepresented in high-quality, long-term placements. Regression analyses further reveal that racial minorities and students with moderate work commitments experience smaller gaps in either participation, learning quality, or outcomes in virtual internships. However, rural students, other racial minority groups, women, and those with extensive work commitments face equivalent or amplified barriers in either access to or quality of virtual learning experiences. These asymmetrical distribution patterns highlight important nuances concerning technology-fueled internships as universal equity solutions, offering implications for stakeholders designing inclusive work-based learning ecosystems. • This study examines virtual internships as alternatives to in-person opportunities across access, quality, and outcomes. • 86 % of virtual interns participate in educationally structured programs at rates comparable to in-person counterparts. • However, 14 % are overrepresented in lower-quality roles or underrepresented in long-term, high-quality programs. • Racial minorities and moderately working students experience smaller gaps in virtual internships. • Rural, women, and extensively working students face sustained or amplified barriers, requiring targeted equity efforts.

This study explored 7th-grade students' help-seeking behaviors in a digital mathematics learning game.In particular, we applied response time decomposition to examine the relationships among the amount of time a student spent reading a hint (i.e., hint reading time), hint type, and in-game performance.The results showed significant differences in hint reading time by the following action and hint type.Overall, hint reading time was positively related to problem solving efficiency scores.However, this association varied by hint type.The use of an indirect hint was negatively correlated with efficiency on the problem in which the hint was accessed but positively correlated with efficiency on the next problem.These findings suggest that prompting students to pause and think about the hint presented may support efficient problem solving.Although indirect hints may cause students to struggle through the problem, they show the benefits of that struggle in subsequent performance.