Credit union participation in the consumer lending market continues to grow as an increasing number of consumers and small businesses become members and open accounts. This study investigates the determinants of credit union loan rates during a period of economic expansion in the United States using fourth quarter 2015 data for 5,942 credit unions. Five different interest rate categories are analysed using nine potential loan rate determinants. Results indicate that loan rates tend to be lower as credit union size increases, while high ratios for net charge-offs and operating costs cause interest rates to increase. Opposite to what is expected, loan rates are positively correlated with regional unemployment rates. A possible explanation for this outcome is that weak labour markets are associated with elevated loan delinquency rates and, therefore, greater default risks resulting in higher interest rates.

Mathematics is an important and hotly contested aspect of U.S. postsecondary education. Its importance for academics and careers and the extent and impact of math achievement disparities are all subject of longstanding debate. Yet there is surprisingly little research into how much and what types of mathematics courses are taken by U.S. undergraduates and the extent of math achievement differentials among students. This article advances the understanding of math course taking by developing course-taking metrics for a nationally representative cohort of bachelor’s graduates. Using NCES transcript data to construct consistent measures of mathematics and quantitative course taking, our analysis finds large variability both within and between STEM/non-STEM majors and a large population of non-STEM graduates earning mathematics credits comparable to their peers in STEM fields. Mathematics course taking differs substantially from course taking in other subjects. We also find that often-observed gender differentials are a function of major, not gender, with females in the most mathematics-intensive programs earning as many or more mathematics credits than their male peers.

Events worldwide have heightened concerns that education is failing to prepare students for a “post-truth” world. A core “post-truth” challenge is the prevalence of deep epistemic disagreements: people fundamentally disagree about appropriate ways of knowing. We provide a new analysis of deep epistemic disagreements and propose an educational response based on the Apt-AIR framework of the goals of epistemic education. An apt response to deep epistemic disagreements requires that people develop individual and collective abilities to make epistemic assumptions visible, to justify and negotiate these assumptions, and to develop shared commitments to appropriate standards and processes of reasoning. To develop these meta-epistemic abilities, we propose a cluster of instructional practices and principles called explorations into knowing. We discuss empirical research showing that teachers and students can meaningfully engage in explorations into knowing and productively discuss their deep epistemic disagreements. These proposals lead to new research directions. © 2020 Division 15, American Psychological Association.

High school underrepresented minority students in the US are at an increased risk of dropping out of the STEM pipeline. Based on expectancy-value theory, we examined if Latino students? perception of support from parents, siblings/cousins, teachers, and friends in 10th grade predicted their science ability self-concepts and values, which in turn predicted their classroom engagement. Survey data were collected from 104 Latino high school students and their science teachers. The findings suggest that adolescents? perceptions of overall support and home-based support predicted adolescents? science ability self-concepts at 10th grade while controlling for their 9th grade self-concepts. Although adolescents reported high support from teachers, teacher or school-based support alone was not a strong correlate of their motivational beliefs. Perceived support was indirectly related to classroom engagement through adolescents? ability self-concepts. Feeling supported across home and school may be necessary to sustain adolescents? science motivational beliefs and, in turn, their science classroom engagement.

Public school districts have been operating under a decade’s long press to move beyond functioning as engines of access-oriented mass public schooling to functioning as instructionally focused education systems pursuing educational excellence and equity. This press has researchers developing analytic frameworks useful for examining different ways that districts are responding. Even so, limitations in individual frameworks suggest a need to explore the coordinate use of complementary frameworks to support more comprehensive examinations of districts. This analysis explores the coordinated use of a “coupling framework” and a “systems framework” to analyze efforts in two districts to improve educational quality and to reduce disparities. Findings suggests that the coordinated use of the coupling and systems frameworks supports deeper analyses of instructional organization and management than either framework would on its own, and that further incorporating quality and equity frameworks would support still-deeper analyses. From the perspective of this issue of the Peabody Journal of Education (PJE), the implication is that elaborating new institutional theory to capture micro-level variation in response to macro-level dynamics is but one challenge faced by organizational researchers in education, and that the deeper challenge lies in considering alternative world views—paradigmatic assumptions—underlying the use of singular and complementary analytic frameworks. © 2020 Taylor & Francis Group, LLC.

We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and interactivity, and when researchers aspire to take advantage of big data. Being fully instantiated computer programs, MLCMs can also be used for autonomous assessment and real-time intervention. We illustrate these ideas in the context of an eye movement-based MLCM of textbase comprehension during reading along connected text. Using a dataset where 104 participants read a 6,500-word text, we trained Random Forests models to predict comprehension scores from six eye movement features. The models were highly accurate (area under the receiver operating characteristic curve = .902; r = .661), robust, and generalized across participants, suggesting possible use in future studies. We conclude by arguing for an increased role of MLCMs in the future of discourse research.

Background and Context: Non-traditional training grounds such as coding boot camps that attract a higher proportion of women are important sites for understanding how to broaden participation in computing. Objective: This work aims to help us better understand the women choosing boot camps and their pathways through these camps and into the computing workforce. Method: This paper reports on a longitudinal, qualitative study investigating female boot camp attendees. Findings: Findings show that women attending boot camps are career changers that develop an interest in software development too late to major in CS, discovering a post-college enjoyment of programming undertaken to support work goals at a current job or an aspirational job. Implications: Women at boot camps illustrate a missed opportunity to diversify postsecondary CS classrooms when not recruited early, not given interdisciplinary options, not exposed to enjoyable programming tasks, and not exposed to the array and number of job prospects.

This paper explores the viability of new touchscreen-based haptic/vibrotactile interactions as a primary modality for perceiving visual graphical elements in eyes-free situations. For touchscreen-based haptic information extraction to be both accurate and meaningful, the onscreen graphical elements should be schematized and downsampled to: (1) maximize the perceptual specificity of touch-based sensing and (2) account for the technical characteristics of touchscreen interfaces. To this end, six human behavioral studies were conducted with 64 blind and 105 blindfolded-sighted participants. Experiments 1-3 evaluated three key rendering parameters that are necessary for supporting touchscreen-based vibrotactile perception of graphical information, with results providing empirical guidance on both minimally detectable and functionally discriminable line widths, inter-line spacing, and angular separation that should be maintained. Experiments 4-6 evaluated perceptually-motivated design guidelines governing visual-to-vibrotactile schematization required for tasks involving information extraction, learning, and cognition of multi-line paths (e.g., transit-maps and corridor-intersections), with results providing clear guidance as to the stimulus parameters maximizing accuracy and temporal performance. The six empirically-validated guidelines presented here, based on results from 169 participants, provide designers and content providers with much-needed guidance on effectively incorporating perceptually-salient touchscreen-based haptic feedback as a primary interaction style for interfaces supporting nonvisual and eyes-free information access.

Children's spontaneous focus on numerosity (SFON) is described as an unprompted tendency that is stable across contexts. The attention to number task (AtN), an experimental forced-choice picture-matching task designed to evaluate select aspects of children's focus on numerosity, may reveal whether task materials can implicitly prompt children to focus on numerosity. In two studies, we replicate earlier findings showing an effect of task context on children's performance on the AtN: When asked to identify one or more matches to a target picture from an array of four options, the frequency with which preschoolers and adults identify a numerosity-based match varies as a function of the features on which the remaining match options are based. We addressed a limitation of the original AtN study by including novel combinations of features as additional trials, with which we continued to demonstrate contextual effects. We also showed that adults seemed more susceptible than children to be primed to attend to numerosity on subsequent trials. Children's focus on numerosity under these experimental conditions was remarkably low. We discuss the implications of these findings for better understanding the SFON construct.

In this paper, we focus on the design of assessments of mathematics teachers' knowledge by emphasising the importance of identifying the purpose for the assessment, defining the specific construct to be measured, and considering the affordances of particular psychometric models on the development of assessments as well as how they are able to communicate learning or understanding. We add to the literature by providing illustrations of the interactions among these critical considerations in determining what inferences can be drawn from an assessment. We illustrate how the considerations shape assessments by discussing both existing and ongoing research projects. We feature discussion of two projects on which the authors of this paper are collaborating to demonstrate the affordances of attending to all three considerations in designing assessments of mathematics teachers' knowledge to provide readers with opportunities to see those considerations in use.