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While previous research documents that women in STEM doctoral programs tend to fare better when their advisor shares their gender identity, this study provides new insights into the role of student-advisor gender identity congruence, relying on a longitudinal sample of doctoral students in biology and using structural equation and latent growth curve modeling. Findings show that advisor gender played an inconsistent and typically indirect role in predicting student outcomes. Further, all students, regardless of gender, tended to report higher quality advising when their advisor was a woman, pointing to potential gender inequities in advising expectations of faculty. Implications for research, theory, and practice are discussed.

The properties and functions of complex systems apply across a variety of domains of science and are at the heart of the solutions to many global crises facing the world today. As such, understanding of complex systems has been increasingly recognized as a core goal of science education. Learning about complexity-related concepts and phenomena is persistently considered difficult for many students--even at the undergraduate level, but traditional pedagogical approaches have been unsuccessful in teaching complex systems effectively. Evidence indicates that agent-based participatory simulations can be promising for this purpose. Using mixed methods, cross-case analysis, we examined how undergraduates experience changes in their understanding of the watershed as a complex ecosystem with the use of a particular agent-based participatory simulation called the UVA Bay Game. While one of the cases yielded evidence of nonsignificant quantitative change between pre- and post-simulation concept maps, this study observed an overall positive increase of complex system understanding through both concept map analysis and narrative reflections on learning. Our findings extend the current understanding of the role of participatory agent-based simulations in teaching and learning about complex systems in classrooms. Implications and limitations are discussed.

Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered. Recently, threshold concepts have likewise become a research focus at the level of doctoral studies. However, such research faces several limitations. First, the generalizability of findings in past research has been limited due to the relatively small numbers of participants in available studies. Second, it is not clear which specific skills are contingent upon mastery of identified threshold concepts, making it difficult to identify appropriate times for possible intervention. Third, threshold concepts observed across disciplines may or may not mask important nuances that apply within specific disciplinary contexts. The current study therefore employs a novel Bayesian knowledge tracing (BKT) approach to identify possible threshold concepts using a large data set from the biological sciences. Using rubric-scored samples of doctoral students' sole-authored scholarly writing, we apply BKT as a strategy to identify potential threshold concepts by examining the ability of performance scores for specific research skills to predict score gains on other research skills. Findings demonstrate the effectiveness of this strategy, as well as convergence between results of the current study and more conventional, qualitative results identifying threshold concepts at the doctoral level.

Guided by theories of socialization and possible selves, this study examines how STEM doctoral students perceive their academic and professional trajectories. More specifically, we rely on four years of interview data from 66 doctoral students in the biological sciences to explore students' perceived trajectories, focusing on the salient identities and experiences that shape the way students identify and describe their graduate experiences over time. Findings reveal wide variation in terms of how students described their trajectories, with some students describing linear trajectories and/or unchanging career interests, while others described their developmental trajectories as highly turbulent and non-linear. These perceived trajectories were largely shaped by student-advisor interactions, the value students placed on becoming "independent" scientists, and the privilege students brought with them to their graduate programs.

This study seeks to understand the daily violence endured by queer youth. We use Queer Battle Fatigue, Ahmed's cultural politics of emotion, and STEM identity theories to make meaning of youth's experience. We draw from audio recordings and transcriptions of 15 queer youth over the course of a summer and fall LGBTQ + maker camp in a rural town in the Intermountain Western part of the United States. Findings show that the maker camp environment provided queer campers casual conversations about microaggressions and violence endured at school. In this context, STEM served as a cover (concealed goals) in three ways: emotion, validation/advice, and safety. This particular environment provided them recognition and validation of both their STEM and queer identities, allowing the group to be able to casually mention these instances of violence they had endured.

Working memory is an essential mechanism in the cognitive learning process. However, its definitions and mechanisms remain a topic of debate. Miller-Cotto and Byrnes ("Journal of Educational Psychology," "112"(5), 1074-1084, 2020) reported a comparison of three models of working memory to determine which best accounted for data obtained from a national US dataset of young children over time (Early Childhood Longitudinal Study of Kindergarten). In comparing these models, which they derived from competing theories of working memory, they found that: (1) one of the tested models best fit the available data; and (2) the model parameters were similar across content domains, concluding that one specific theory of working memory was best supported. The study reported here is an attempted replication of this work. We implemented a multi-phase effort at replication, first undertaking a review of the literature surrounding competing theories of working memory to aid interpretation of results, then attempting a direct replication, and lastly applying an alternative modeling technique to appropriately differentiate between between- and within-person variance. Neither effort succeeded in fully replicating the original findings. Instead, we found that parsing between- and within-person variance is an essential strategy for appropriately interpreting the relationships between working memory and domain learning. Doing so led to support for different models across domains, and interpretation of aspects of these models through the lens of relevant theory suggested alternative strategies for examining these issues in future research.

Research on cognitive load theory has historically focused on identifying the processes and strategies that enhance learning outcomes. Less attention has been given to the ways in which cognitive load may interact with the motivational and emotional aspects of learning. The aim of the present study is to examine the extent to which experienced cognitive load directly impacts self-efficacy during course-based instruction, after controlling for learning. In Study 1, results showed that within-time measurements of undergraduate students (n = 390) revealed a strong negative association between reported cognitive load and self-efficacy. However, across-time measurements revealed a positive relationship in which higher load predicted increased self-efficacy at later time points. Study 2 (n = 281) showed similar results using a different measure of cognitive load. Findings point to increases in self-efficacy being a consequence of the cognitive load imposed by instruction, rather than a precursor of the decision to invest mental effort. Furthermore, the differential relationships between cognitive load and self-efficacy within and across time highlight different types of motivational costs associated with invested mental effort as perceived by students. As a result, within and across time impacts of cognitive load offer both an enhanced understanding of cognition–motivation interactions and a potentially new approach to empirically differentiating between and quantifying intrinsic and extraneous load types. Educational Impact and Implications Statement: Cognition and motivation have traditionally been examined separately with little attention paid to how the cognitive processing of information itself may influence motivational beliefs. This study uses cognitive load theory to analyze the ways in which mental effort shapes concurrent and subsequent self-efficacy. Findings indicate that effort allocated to content difficulty positively predicts self-efficacy across time, and effort allocated to other factors such as unhelpful features of instruction and instructional materials negatively predicts concurrent self-efficacy. These results suggest that effort allocation due to instructional features has an important role to play on learners' motivation, independent of how much they learn, so instructional design decisions are important to consider even when they do not seem to directly impact learning outcomes. [ABSTRACT FROM AUTHOR]

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.

Structural equation modeling is an ideal data analytical tool for testing complex relationships among many analytical variables. It can simultaneously test multiple mediating and moderating relationships, estimate latent variables on the basis of related measures, and address practical issues such as nonnormality and missing data. To test the extent to which a hypothesized model provides an appropriate characterization of the collective relationships among its variables, researchers must assess the "fit" between the model and the sample's data. However, interpreting estimates of model fit is a problematic process. The traditional inferential test of model fit, the chi-square test, is biased due to sample size. Fit indices provide descriptive (i.e., noninferential) values of model fit (e.g., comparative fit index, root-mean-square error of approximation), but are unable to provide a definitive "acceptable" or "unacceptable" fit determination. Marcoulides and Yuan have introduced an equivalence-testing technique for assessing model fit that combines traditional descriptive fit indices with an inferential testing strategy in the form of confidence intervals to facilitate more definitive fit conclusions. In this paper, we explain this technique and demonstrate its application, highlighting the substantial advantages it offers the life sciences education community for drawing robust conclusions from structural equation models. A structural equation model and data set (N = 1902) drawn from previously published research are used to illustrate how to perform and interpret an equivalence test of model fit using Marcoulides and Yuan's approach.

In the laboratory-based disciplines, selection of a principal investigator (PI) and research laboratory (lab) indelibly shapes doctoral students' experiences and educational outcomes. Framed by the theoretical concept of person-environment fit from within a socialization model, we use an inductive, qualitative approach to explore how a sample of 42 early-stage doctoral students enrolled in biological sciences programs made decisions about fitting with a PI and within a lab. Results illuminated a complex array of factors that students considered in selecting a PI, including PI relationship, mentoring style, and professional stability. Further, with regard to students' lab selection, peers and research projects played an important role. Students actively conceptualized trade-offs among various dimensions of fit. Our findings also revealed cases in which students did not secure a position in their first (or second) choice labs and had to consider their potential fit with suboptimal placements (in terms of their initial assessments). Thus, these students weighted different factors of fit against the reality of needing to secure financial support to continue in their doctoral programs. We conclude by presenting and framing implications for students, PIs, and doctoral programs, and recommend providing transparency and candor around the PI and lab selection processes.

High attrition rates have been a defining characteristic of doctoral education for decades, representing a loss of time, talent, and effort for departing students and their faculty. This qualitative study uses a biomedical science doctoral student sample to collect "real time" data on attrition within the first 2 years of doctoral training. Eighteen students, who represented 16 distinct universities, were interviewed as they engaged in the withdrawal process. Using the conceptual frames of socialization and social cognitive career theory, we explored experiences that preceded these students' doctoral program withdrawals. Furthermore, we examined how expressed roles of students' self-efficacy, outcome expectations, and professional goals contributed to the withdrawal process. Findings indicate that faculty advising (both positive and negative), laboratory rotation experiences, self-efficacy components, and changing professional goals all play a role in the early doctoral program attrition process.

ABSTRACT For some time, there has been a call for cross-disciplinary teaching within the business disciplines. With the rise of data and analytics, there is an opportunity for cross-disciplinary teaching by integrating technology throughout the business curriculum. However, many business professors have little experience in cross-disciplinary teaching. We hope to rectify this by introducing an approach that uses prelecture material to prepare students for learning concepts and terms that are not core to the focal class. In a study that combines Structured Query Language (SQL) coding with an audit principle called Benford's Law, we analyze the impact of adding prelecture material about Benford's Law on student's cognitive load, knowledge gained, and instructional efficiency. Results indicate that students who received a prelecture on Benford's Law outperformed the control group in performance measures related to Benford's Law, and that the instructional efficiency on Benford's Law was also higher for the treatment group. We did not find any significant differences in cognitive load between the two groups, nor did the treatment group perform significantly better when tested on the SQL concepts. We conclude that cross-discipline teaching is a fine balance between each discipline and communicating to students the importance of both disciplines is key.

Faculty and peer interactions play a key role in shaping graduate student socialization. Yet, within the literature on graduate student socialization, researchers have primarily focused on understanding the nature and impact of faculty alone, and much less is known about how peer interactions also contribute to graduate student outcomes. Using a national sample of first-year biology doctoral students, this study reveals distinct categories that classify patterns of faculty and peer interaction. Further, we document inequities such that certain groups (e.g., underrepresented minority students) report constrained types of interactions with faculty and peers. Finally, we connect faculty and peer interaction patterns to student outcomes. Our findings reveal that, while the classification of faculty and peer interactions predicted affective and experiential outcomes (e.g., sense of belonging, satisfaction with academic development), it was not a consistent predictor of more central outcomes of the doctoral socialization process (e.g., research skills, commitment to degree). These and other findings are discussed, focusing on implications for future research, theory, and practice related to graduate training.

Research on cognitive load theory (CLT) has focused primarily on identifying the mechanisms and strategies that enhance cognitive learning outcomes. However, CLT researchers have given less attention to the ways in which cognitive load may interact with the motivational and emotional aspects of learning. Motivational beliefs have typically been assumed to be merely a precursor to the cognitive process. This view provides an incomplete picture of the dynamic relationship between cognitive load and motivational beliefs. In this review, we synthesize previous scholarly efforts concerning the motivational effects of anticipated investment of mental effort, new developments in the expectancy-value theory of motivation, and recent findings implicating cognitive load in the formulation of motivational beliefs. By conceptualizing cognitive load as motivational cost, we argue that motivational beliefs are an important outcome that results from instruction. We examine recent empirical evidence supporting this proposition and consider the implications for the further development of both CLT and motivational theories through their integration.

Within this integrative review, cognitive load theory (CLT) is asserted as a powerful framework for conceptualizing human cognitive processes within learning. The relationship between cognition and emotion is then examined and further integrated within the scope of CLT. Emotion regulation strategies are discussed and adaptive strategies are proposed as being of particular relevance to broadening the theoretical and practical impacts of CLT. Central to the argument of this review is the use of cognitive reappraisal as a potential mitigator of cognitive load. Cognitive reappraisal involves reframing or reassessing understandings or beliefs that underlie an emotional response, which may mitigate cognitive load imposed by maladaptive emotion. It is proposed that effectively integrating adaptive emotion regulation strategies such as cognitive reappraisal in our pursuit of more effective cognitive functioning will aid in the development of a more integrated model of cognition and emotion within CLT. [ABSTRACT FROM AUTHOR]

We explore and critically reflect on the research development of eight science or engineering doctoral students serving as research assistants over the course of an academic year. We use a cognitive apprenticeship framework, assumed to explain doctoral students' skill development, to interpret narratives of skill development for students and their faculty supervisors, and compare these narratives against empirical measurements of student research skill development over the course of an academic year. We found abundant evidence for the "cognitive" component of cognitive apprenticeship, but questionable evidence for the "apprenticeship" component. We suggest that cognitive apprenticeship can be particularly potent in the development of students' research skills through research assistantship, but not without deliberate intention of both faculty supervisor and student.

Cognitive Task Analysis (CTA) interview technique iscommonly used to elicit knowledge of subject-matter expertsand to design instruction better focused on what experts don’tknow they know. However, the knowledge of how to conductan effective interview is, itself, largely implicit. In this studywe performed protocol analysis on a set of interviewtranscripts from an expert CTA practitioner to elicit thecognitive processes of conducting CTA interviews. We alsoconsulted expert CTA practitioners to identify the strategiesthat they used during the interviews. We present keystrategies that were employed by the expert CTA practitionersto ensure comprehensiveness and accuracy in the informationcollected, such as looking for perceptual cues (e.g.considering verbs such as “determine”) to ascertain adequacyof SME’s responses and selection of follow-up questions. Wepresent a production rule model as a detailed description ofthe cognitive processes underlying expert CTA interviewing

Although first-generation students represent a substantial proportion of doctoral students, few studies have examined their experiences and outcomes. We contribute to this nascent area of inquiry by comparing experiences and outcomes of first-generation and continuing-generation students during the first 3 years of doctoral education. Contrary to expectations, the results based on a national sample of PhD students in biology revealed remarkable similarity in experiences and outcomes between first-generation and continuing-generation students. One notable exception to this overall pattern of similarity was research productivity in the second year. By examining the relationships between students' experiences and outcomes over time, the findings illuminate the unique ways in which socialization experiences are related to specific outcomes and the extent to which those relationships change across years. Thus, this study offers initial insights into the nuanced ways in which students' socialization experiences contribute to various outcomes of doctoral education.

This article shares findings from a multilevel analytic induction of administrators, teachers, and gifted students within specialized science, technology, engineering, and mathematics (STEM) schools about their beliefs regarding the role of standards and standardized tests in the education of gifted learners. Sharing results from a cross-case analysis of six schools, we explore the ways in which student experiences of standards-based learning differ from the perceptions of their teachers and school administrators. We found that there is no consensus across administrators, teachers, and students regarding the impact that standards have in the educational process or on outcomes within specialized STEM schools, though all community members value and seek to create deep learning opportunities for students.

Equitable gender representation is an important aspect of scientific workforce development to secure a sufficient number of individuals and a diversity of perspectives. Biology is the most gender equitable of all scientific fields by the marker of degree attainment, with 52.5% of PhDs awarded to women. However, equitable rates of degree completion do not translate into equitable attainment of faculty or postdoctoral positions, suggesting continued existence of gender inequalities. In a national cohort of 336 first-year PhD students in the biological sciences (i.e., microbiology, cellular biology, molecular biology, developmental biology, and genetics) from 53 research institutions, female participants logged significantly more research hours than males and were significantly more likely than males to attribute their work hours to the demands of their assigned projects over the course of the academic year. Despite this, males were 15% more likely to be listed as authors on published journal articles, indicating inequality in the ratio of time to credit. Given the cumulative advantage that accrues for students who publish early in their graduate careers and the central role that scholarly productivity plays in academic hiring decisions, these findings collectively point to a major potential source of persisting underrepresentation of women on university faculties in these fields.

In the biological sciences, very little is known about the mechanisms by which doctoral students acquire the skills they need to become independent scientists. In the postsecondary biology education literature, identification of specific skills and effective methods for helping students to acquire them are limited to undergraduate education. To establish a foundation from which to investigate the developmental trajectory of biologists' research skills, it is necessary to identify those skills which are integral to doctoral study and distinct from skills acquired earlier in students' educational pathways. In this context, the current study engages the framework of threshold concepts to identify candidate skills that are both obstacles and significant opportunities for developing proficiency in conducting research. Such threshold concepts are typically characterised as transformative, integrative, irreversible, and challenging. The results from interviews and focus groups with current and former doctoral students in cellular and molecular biology suggest two such threshold concepts relevant to their subfield: the first is an ability to effectively engage primary research literature from the biological sciences in a way that is critical without dismissing the value of its contributions. The second is the ability to conceptualise appropriate control conditions necessary to design and interpret the results of experiments in an efficient and effective manner for research in the biological sciences as a discipline. Implications for prioritising and sequencing graduate training experiences are discussed on the basis of the identified thresholds.

This meta-analysis assesses the predictive validity of the Graduate Record Examination (GRE) across outcome variables, including grade point average, for graduate students. In addition to aggregate effects, this paper also assessed changes in observed effects over time as related to increasing diversity in the graduate student population and as a function of gender and racial/ethnic composition of study samples. Framed using a lens of critical whiteness, this analysis examined n = 1,659 individual effects across k = 201 studies. Overall, 62.3% of reported effects were nonsignificant (i.e. no predictive value of GRE scores on student outcomes). Further, the magnitude of observed predictive relationships decreased significantly over time. The aggregate mean effect across all studies and outcomes was small, significant, and positive: GRE score predicted 3.24% of variance across measured outcomes, 4% of variance in overall GPA, and 2.56% of variance in first-year graduate GPA. Sample composition effects by race/ethnicity were notable under some conditions, but nonsignificant, with increasing proportions of people of Color within a study sample associated with poorer predictive validity for GPA. Likewise, the magnitude of negative effects where lower GRE scores predicted stronger student outcomes showed increasing trends from 0.16% of variance for all-white samples to 7.3% for samples comprised entirely of people of Color. [ABSTRACT FROM AUTHOR]