This paper introduces an approach to measurement that positions context and situativity as an intrinsic part of the measurement model in a manner compatible with the learning sciences' view of learning as a transactive process within a broader sociocultural ecosystem.This approach relies on a mixed methods approach to measurement in which qualitative data are integrated into a moderated nonlinear factor analytic (MNLFA) framework.Using data from a longitudinal student of Ph.D. students in the biological sciences, we demonstrate that sensemaking context elicited through interview data could be incorporated directly into the model to enhance the comparability of scale scores collected across multiple timepoints.On multiple items that demonstrated item parameter drift (IPD) without moderation, the inclusion of a relevant idiographic contextual variable permitted the fitting of a moderated model that eliminated IPD.

Gender parity in academia remains elusive, even in fields, such as biological sciences, that award approximately half of their PhDs to women. Based on a sample of graduate students in biological sciences followed over 5 years during their PhD programs, we find that women publish in journals with lower impact factors and are less likely to publish in top journals. Notably, gender inequality in journal impact factors holds only for total publications but not for first-authored publications, which implies that differential opportunities for collaborations leading to high-impact publications may be one factor hindering gender equity.

Using a national sample of 336 biology Ph.D. students, this study classified students based on their interactions with faculty and peers, and investigated longitudinal changes in their interaction classifications over 3 years. We also examined associations between students' interaction classifications, their demographic backgrounds (e.g., gender, international student status, first-generation status, and underrepresented racial/ethnic minority status), and doctoral outcomes (e.g., sense of belonging, satisfaction with academic development, institutional commitment, and scholarly productivity). The findings revealed that three distinct subgroups existed among the current sample of biology Ph.D. students, with respect to their interactions with their faculty and peers: high interaction with faculty and peers, high interaction with peers only, and low interaction with faculty and peers. However, such patterns of doctoral students' interactions with faculty and peers tended to, in general, be stable over time. In addition, while the differential effects of demographic variables on changes in these interaction patterns were widely founded, such changes were not substantially linked to doctoral student outcomes. Implications for research on doctoral education and socialization theory are discussed.

Compared to continuing-generation bachelor's graduates (CGGs), first-generation bachelor's graduates (FGGs) are underrepresented in U.S. STEM PhD programs. To better understand how students choose to pursue PhDs, we analyzed interview data from 62 FGG and CGG first-year PhD students in bioscience. Across the sample, four themes emerged to illustrate how social, cognitive, and contextual conditions influenced students' decisions to pursue a PhD: research experiences, social support, career autonomy and impact, and program fit and finances. Findings showed how undergraduate experiences critically shape FGGs' career expectations and goals and revealed additional differences in FGGs' and CGGs' career values and decision-making.

Many students in the United States experience educational disruptions when changing schools for reasons other than grade promotion. These nonstructural moves can detrimentally affect students' mathematics learning. While gender significantly shapes individual learning experiences, how student mobility plays out in mathematics learning based on gender has remained largely unexplored. Analysing six California school districts' panel data using school-fixed effect models, the findings in this study showed that the negative association of nonstructural mobility with mathematics performance is more detrimental for males than females and that self-management competency levels fully account for the differential effect. The findings suggest gender should be considered in understanding students' mobility experiences and underscore the need for incorporating self-management into educational interventions to mitigate the negative effects of nonstructural school transitions on mathematics performance.

Clustering methods are often used in physics education research (PER) to identify subgroups of individuals within a population who share similar response patterns or characteristics. Among these, k -means (or k -modes, for categorical data) is one of the most commonly used clustering methods in PER. This algorithm, however, is distance-based rather than model-based: it relies on algorithmic partitioning and assigns each individual to one subgroup through hard assignment. Researchers must also conduct analyses to relate subgroup membership to other variables. Mixture models are a model-based alternative that offers several statistical tools for choosing the optimal number of subgroups, accounts for classification errors by assigning individuals probabilities of belonging to each subgroup rather than hard assignment, and allows researchers to directly integrate subgroup membership into a broader latent variable framework. In this paper, we outline the theoretical similarities and differences between k -modes clustering and latent class analysis (one type of mixture model for categorical data). We also present parallel analyses using each method to address the same research questions in order to demonstrate these similarities and differences. We provide the data and code to replicate the worked example presented in the paper for researchers interested in using mixture models.

One of the most important developmental relationships in the doctoral student experience is that of the faculty advisor, and yet we know little about whether and how advisor relationships vary between first-generation and continuing-generation doctoral students. Drawing on qualitative interviews with 83 late-stage doctoral students in biological sciences, we explore differences in student perceptions of their relationships with advisors. Narratives reveal a continuum of relationship types, including strained, evolving, supportive, and equal. In equal relationships, doctoral students feel more like collegial partners working alongside their advisors. While continuing-generation and first-generation students are similarly represented among strained and evolving relationships, first-generation students rarely attain equal relationship status. The presented findings offer implications for understanding how inequality shapes student–advisor relationships, the role of collegiality in doctoral education’s hidden curriculum, and the supports needed to foster equity for first-generation students in graduate programs. © 2024 by the authors.

Prior literature has documented the importance of faculty advisors in the doctoral student socialization process, with a few studies describing negative advising relationships characterized by disengagement, disinterest, unsupportive behavior, and interpersonal conflict. We extend this research by exploring how negative advising relationships emerge and develop over time. Examining longitudinal interviews over four years with 15 doctoral students in biological sciences in the USA who experienced negative relationships with their advisors, we illuminate how negative advising relationships unfold over the course of graduate studies. We find two primary patterns in challenging relationships: some students show a gradual decline in relationship health over time, while others point to a single event altering their relationship trajectory. We also identify specific factors that shape each of these negative relationship types. By revealing the different social processes that underlie the emergence of negative advising relationships, our findings provide a valuable contribution to understanding the complex social landscape of doctoral education. The findings further the dialogue on how faculty advisors can craft successful pathways through graduate education, thereby supporting the academic and professional success of doctoral students. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

Hesborn Wao, Ph

Three-dimensional (3D) technologies such as 3D digital prototyping have recently been used in apparel engineering to reduce the number of costly physical prototypes. This technology can simulate garment drapes by digitally sewing 2D patterns and placing them on a 3D avatar. Yet, current limitations in simulation accuracy mean physical prototypes are still indispensable. Integrating AI technologies shows promise in accelerating digital transformation in the apparel industry. AI can support decision-making in apparel design, size and fit prediction, and fashion recommendations. It can also assist decision-making in technical design by predicting garment fit before making physical prototypes or offering solutions for misfit areas. Research indicates that models such as Naive Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) offer robust solutions for fit evaluation. However, they require further testing across diverse garment styles and demographics. Developing a nuanced classification system for fit conditions could improve model accuracy, ultimately supporting the industry's shift towards digital and AI-enhanced product development. In this study, we propose a framework to achieve such a system.