Three studies examine the relationship between women’s expression of interest in computer science and identity expression threat, the concern about conveying an identity inconsistent with one's gender role. Undergraduates perceive academic majors to signal who they are to peers (Study 1). Women imagining majoring in computer science report greater identity expression threat from their peers outside computer science than from those inside the field (Study 2). Women report greater identity expression threat in computer science (but not biology or English) than do men. Identity expression threat mediates gender differences in reported likelihood of downplaying interest in computer science (Study 3). Women considering computer science perceive they will be doubly isolated, both from those within and outside the field. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Investigating student learning and understanding of conceptual physics is a primary research area within physics education research. Multiple quantitative methods have been employed to analyze commonly used mechanics conceptual inventories: the Force Concept Inventory (FCI) and the Force and Motion Conceptual Evaluation (FMCE). Recently, researchers have applied network analytic techniques to explore the structure of the incorrect responses to the FCI identifying communities of incorrect responses which could be mapped on to common misconceptions. In this study, the method used to analyze the FCI, modified module analysis was applied to a large sample of FMCE pretest and post-test responses (N-pre = 3956, N-post = 3719). The communities of incorrect responses identified were consistent with the item groups described in previous works. As in the work with the FCI, the network was simplified by only retaining nodes selected by a substantial number of students. Retaining as nodes only those incorrect answer choices selected by at least 20% of the students produced communities associated with only four misconceptions. The incorrect response communities identified for men and women were substantially different, as was the change in these communities from pretest to post-test. The 20% threshold was far more restrictive than the 4% threshold applied to the FCI in the prior work that generated similar structures. Retaining nodes selected by 5% or 10% of students generated a large number of complex communities. The communities identified at the 10% threshold were generally associated with common misconceptions producing a far richer set of incorrect communities than the FCI; this may indicate that the FMCE is a superior instrument for characterizing the breadth of student misconceptions about Newtonian mechanics.

Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational interventions and the allocation of educational resources. However, the performance metrics used in that study become unreliable when used to classify whether a student would receive an A, B, or C (the ABC outcome) or if they would receive a D, F or withdraw (W) from the class (the DFW outcome) because the outcome is substantially unbalanced with between 10% to 20% of the students receiving a D, F, or W. This work presents techniques to adjust the prediction models and alternate model performance metrics more appropriate for unbalanced outcome variables. These techniques were applied to three samples drawn from introductory mechanics classes at two institutions (N = 7184, 1683, and 926). Applying the same methods as the earlier study produced a classifier that was very inaccurate, classifying only 16% of the DFW cases correctly; tuning the model increased the DFW classification accuracy to 43%. Using a combination of institutional and in-class data improved DFW accuracy to 53% by the second week of class. As in the prior study, demographic variables such as gender, underrepresented minority status, fast-generation college student status, and low socioeconomic status were not important variables in the final prediction models.

Purpose This study aims to examine how science, technology, engineering, and mathematics doctoral students interact with postdocs within the research laboratory, identifying the nature and potential impacts of student-postdoc mentoring relationships. Design/methodology/approach Using a sample of 53 doctoral students in the biological sciences, this study uses a sequential mixed-methods design. More specifically, a phenomenological approach enabled the authors to identify how doctoral students make meaning of their interactions with postdocs and other research staff. Descriptive statistics are used to examine how emergent themes might differ as a product of gender and race/ethnicity and the extent to which emergent themes may relate to key doctoral student socialization outcomes. Findings This study reveals six emergent themes, which primarily focus on how doctoral students receive instrumental and psychosocial support from postdocs in their labs. The most frequent emergent theme captures the unique ways in which postdocs provide ongoing, hands-on support and troubleshooting at the lab bench. When examining how this theme plays a role in socialization outcomes, the results suggest that doctoral students who described this type of support from postdocs had more positive mental health outcomes than those who did not describe this type of hands-on support. Originality/value Literature on graduate student mentorship has focused primarily on the impact of advisors, despite recent empirical evidence of a cascading mentorship model, in which senior students and staff also play a key mentoring role. This study provides new insights into the unique mentoring role of postdocs, focusing on the nature and potential impacts of student-postdoc interactions.

Machine learning for author name disambiguation is usually conducted on the training and test subsets of labeled data created for a specific task. As a result, disambiguation models learned on heterogeneous labeled data are often inapplicable for other purposes that either do not use the same labeled data or do not make use of any labeled data at all. This article explores the idea of transfer learning in a new context, author name disambiguation. We focus on cases where a disambiguation task lacking labeled training data uses models trained on labeled data generated for other tasks. For this purpose, two labeled source datasets are used for training of disambiguation models to be applied to three test target datasets that are deficient of labeled training data. Our results show that transfer learning can produce disambiguation performances similar to those achievable by traditional machine learning in which training and test datasets come from the same labeled data source. The good performance through transfer learning are possible when training source datasets have similar feature distributions as test target datasets. This study suggests that through transfer learning, rich disambiguation models in previous studies can be retained and reused across ambiguous bibliographic data from different fields and data sources, motivating further research on how to correct feature distribution differences between source and target datasets to expand the application of transfer learning in author name disambiguation beyond the model sharing explored in this research.

A well-developed interview protocol is an essential data collection tool in qualitative research. An established process to refine interview protocols can help build quality and consistency into data collection. However, despite the importance placed on interview protocols by academic texts, there is little guidance regarding how to systematically develop and refine interview protocols, particularly when exploring complex constructs, such as beliefs and identity. In this special session, attendees will learn and practice an approach for refining interview protocols for investigating complex constructs in engineering education. We share this interview refinement approach as it enabled us to determine if our interview questions prompted participants to provide data essential to answering our research questions for a pilot study investigating students' beliefs and identities. This special session will also include conversations around best practices related to data collection to access complex constructs and how these practices can impact and shape future research. We welcome attendees of all experience levels (novice to expert) with regard to designing interview protocols. The session will be facilitated by Dr. Emily Dringenberg, Dr. Rachel Kajfez, and their graduate students. Dr. Dringenberg is a qualitative researcher well versed in beliefs. Dr. Kajfez is a mixed methods researcher well versed in identity. Both have multiple NSF grants exploring these complex constructs.

Increased attention is being placed on the importance of ethnic-racial socialization in children of color's academic outcomes. Synthesizing research on the effects of parental ethnic-racial socialization, this meta-analysis of 37 studies reveals that overall the relation between ethnic-racial socialization and academic outcomes was positive, though the strength varied by the specific academic outcome under consideration, dimension of ethnic-racial socialization utilized, developmental age of the child receiving the socialization, and racial/ethnic group implementing the socialization. Ethnic-racial socialization was positively related to academic performance, motivation, and engagement, with motivation being the strongest outcome. Most dimensions of ethnic-racial socialization were positively related to academic outcomes, except for promotion of mistrust. In addition, the link between ethnic-racial socialization and academic outcomes was strongest for middle school and college students, and when looking across ethnic-racial groups, this link was strongest for African American youth. The results suggest that different dimensions of ethnic-racial socialization have distinct relationships with diverse academic outcomes and that the effects of ethnic-racial socialization vary by both youth developmental levels and racial/ethnic groups. © 2019 Society for Research in Child Development

This study examined how inferences about epistemic competence and generalized labeling errors influence children’s selective word learning. Three‐ to 4‐year‐olds ( N = 128) learned words from informants who asked questions about objects, mentioning either correct or incorrect labels. Such questions do not convey stark differences in informants’ epistemic competence. Inaccurate labels, however, generate error signals that can lead to weaker encoding of novel information. Preschoolers retained novel labels from both informants but were slower to respond in the Inaccurate Labeler condition. When the test procedure was not sensitive to the strength of information encoding, children performed above chance in both conditions and their response times did not differ. These results suggest that epistemic‐level inferences and error generalizations influence preschoolers’ selective word learning concurrently.