This study focused on answering the research question: What are the independent and combined effects of student characteristics and faculty mentorship on the quality of summer undergraduate research experience (SURE) student poster presentations? The results of this study can improve SURE programs by informing directors on best practices for mentor and mentee matching.

The Measure of Acceptance of the Theory of Evolution (MATE) was constructed to be a single-factor instrument that assesses an individual's overall acceptance of evolutionary theory. The MATE was validated and the scores resulting from the MATE were found to be reliable for the population of inservice high school biology teachers. However, many studies have utilized the MATE for different populations, such as university students enrolled in a biology or genetics course, high school students, and preservice teachers. This is problematic because the dimensionality and reliability of the MATE may not be consistent across populations. It is not uncommon in science education research to find examples where scales are applied to novel populations without proper assessment of the validity and reliability. In order to illustrate this issue, a case study is presented where the dimensionality of the MATE is evaluated for a population of non-science major preservice elementary teachers. With this objective in mind, factor analytic and item response models are fit to the observed data to provide evidence for or against a one-dimensional latent structure and to detect which items do not conform to the theoretical construct for this population. The results of this study call into question any findings and conclusions made using the MATE for a Hispanic population of preservice teachers and point out the error of assuming invariance across substantively different populations.

Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain magnetic resonance imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 43.83%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage.

This paper explores the use of a lesser-known dynamic model for the median, a foundational topic that starts in the middle school curriculum and is associated with student misconceptions and knowledge gaps. This model appears to offer a rich vehicle to explore the median interactively in greater conceptual depth that includes some of its more subtle associated ideas. An exploratory study to assess performance of this model in a class for pre-service middle school teachers yielded evidence that students who completed the dataset sequence associated with the model gained further insight about the median, especially concerning how the mean and median are affected differently by outliers. Analyses of open ended questions as well as empirical results of multiple-choice questions are used to assess the overall learning outcomes gained by students. A one-minute video is offered to illustrate key points of the model.

University students occupy a socially marginal position and therefore are often underserved by academic and service institutions. This article analyzes food and housing security among students at The University of Texas at El Paso, a Hispanic-Serving Institution located in the U.S.-Mexico Border region. Findings of a sample of n = 7,633 university students are presented in the first cross-sectional, two-year food and housing security study on campus administered via platform Campus Labs Baseline. The first sample in 2019 consisted of n = 2,615 students representing 10.4% of student enrollment (25,177 total 2019 enrollment), and the second sample in 2020 was n = 5,018 representing 20.2% of student enrollment (24,879 total 2020 enrollment). To measure food security, the six-item short form of the U.S. Department of Agriculture (USDA) Household Food Security Survey Module was used. To document housing security, we created questions informed by student input. In this study, survey results are reported, and tests are conducted to assess the relationships between various student characteristics and food and housing security. Student characteristics significantly impacting food and housing security are probed further using data visualizations and subpopulation analysis with a focus on analyzing factors impacted by the COVID-19 pandemic. Results indicate that employment status, consistent employment status, hours per week, academic level, number of dependents, and gender are all factors associated with food security during the pandemic but not prior to the pandemic. Other factors, including, college affiliation, ethnicity/race, having any dependents and being head of household, living alone, mode of campus transportation and mode of the transportation, household income, and age, all were associated with food security in both academic years. Using these results, a critical analysis of past interventions addressing food and housing security is presented with a focus on changes made during the pandemic. Recommendations are made for further data-driven interventions and future steps.

Spiders perform many essential ecological services, yet humans often experience negative emotions toward spiders. These emotions can lead to the avoidance of beneficial events. These emotions may affect beliefs about what should or should not be included in a science curriculum. This study investigated how activities with living spiders affected preservice middle school science teachers’ emotions and beliefs. Prior to the activities both groups (i.e., treatment and control) had moderate to extreme fear and disgust toward the spider. The teachers that participated in the spider activities (i.e., treatment group) had much lower levels of fear and disgust after performing the spider activities than the control group that did not participate in the spider activities. The control group continued to have elevated levels of fear and disgust toward the spider throughout the study. Before the spider activities neither group planned to incorporate information about spiders or information about the essential ecological services of spiders into their science classroom. After the treatment group participated in the spider activities, the teachers had definitive plans to teach their students about spiders and the essential ecological services that they provide. The control group remained unchanged and had no plans to teach this information to their students.