“Why am I here?”: A phenomenological exploration of first-generation college student experiences in STEM majors within a predominantly white institution

First-generation college students (FGCSs) are a growing population in undergraduate education. Research on FGCS primarily focuses on the challenges and barriers they encounter in college. While important, this literature offers a limited view of FGCS as learners. Moreover, minimal literature has examined these students' lived experiences within sciences, technology, engineering, and mathematics (STEM). This study explores the learning experiences of FGCS (N = 17) in undergraduate STEM programs at a mid-sized, private, predominantly white institution. Guided by the frameworks of intersectionality and social cognitive career theory, the researchers utilized a qualitative, phenomenological research methodology to hear the perspectives of FGCS to and to identify problematic higher educational structures in STEM. Findings point to an elitist stance underlying STEM programs based in general disciplinary norms that restricted FGCS access to STEM majors and careers across social, academic, and professional elements of the academic experience. FGCS who were multiply minoritized based upon race, gender, and social class experienced the compounding of marginalization, and academic success and persistence came at a personal cost. Students also reported relying predominantly on personal strengths and motivations to overcome elitism in STEM rather than on institutional supports. Findings also highlight the variation in the first-generation experience and identify the unique barriers FGCS encounter within STEM fields. Implications for dismantling inequitable structures for higher education in STEM attending to the social, academic, and professional inclusion of FGCS.     

Predicting shifts in generalization gradients with perceptrons

Perceptron models have been used extensively to model perceptual learning and the effects of discrimination training on generalization, as well as to explore natural classification mechanisms. Here, we assess the ability of existing models to account for the time course of generalization shifts that occur when individuals learn to distinguish sounds. A set of simulations demonstrates that commonly used single-layer and multilayer perceptron networks do not predict transitory shifts in generalization over the course of training but that such dynamics can be accounted for when the output functions of these networks are modified to mimic the properties of cortical tuning curves. The simulations further suggest that prudent selection of stimuli and training criteria can allow for more precise predictions of learning-related shifts in generalization gradients in behavioral experiments. In particular, the simulations predict that individuals will show maximal peak shift after different numbers of trials, that easier contrasts will lead to slower development of shifted peaks, and that whether generalization shifts persist or dissipate will depend on which stimulus dimensions individuals use to distinguish stimuli and how those dimensions are neurally encoded.     

Inferences About Variance Components and Reliability-Generalizability Coefficients in the Absence of Random Sampling

Generalizability theory (G theory) employs random-effects ANOVA to estimate the variance components included in generalizability coefficients, standard errors, and other indices of precision. The ANOVA models depend on random sampling assumptions, and the variance-component estimates are likely to be sensitive to violations of these assumptions. Yet, generalizability studies do not typically sample randomly. This kind of inconsistency between assumptions in statistical models and actual data collection procedures is not uncommon in science, but it does raise fundamental questions about the substantive inferences based on the statistical analyses. This article reviews criticisms of sampling assumptions in G theory (and in reliability theory) and examines the feasibility of using representative sampling, stratification, homogeneity assumptions, and replications to address these criticisms.     

Changing Teacher–Child Dyadic Interactions to Improve Preschool Children's Externalizing Behaviors

A randomized controlled trial was used to examine the impact of an attachment‐based, teacher–child, dyadic intervention (Banking Time) to improve children's externalizing behavior. Participants included 183 teachers and 470 preschool children (3–4 years of age). Classrooms were randomly assigned to Banking Time, child time, or business as usual (BAU). Sparse evidence was found for main effects on child behavior. Teachers in Banking Time demonstrated lower negativity and fewer positive interactions with children compared to BAU teachers at post assessment. The impacts of Banking Time and child time on reductions of parent‐ and teacher‐reported externalizing behavior were greater when teachers evidenced higher‐quality, classroom‐level, teacher–child interactions at baseline. An opposite moderating effect was found for children's positive engagement with teachers.     

BioOne and SPIE: A reflection on the first 3 years of the nonprofit platform partnership

• There is great potential for organizations to form collaborative partnerships, particularly in the nonprofit environment, which can reduce costs and aid in strategic development.
• Regular and transparent communication with internal and external stakeholders is critical to the health of any partnership, especially when doing something new or novel.
• Reciprocal service-level agreements with clear expectations and ongoing evaluation ensure the collaboration keeps pace with commercial alternatives.
• The SPIE-BioOne partnership was built to scale and incorporate future collaborators to further reduce costs and bring new ideas to the user group.