JURSW Volume 8

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/1747

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Best Practices to Foster Pre-service Teachers’ Science Content Knowledge
(UTSA Office of Undergraduate Research, 2022-12) Salinas, Paulina
Understanding the science instruction approaches to pre-service teacher preparation is important to identify the effective features of these experiences and apply them to the design of new learning experiences. The main idea is that teachers often feel not prepared to teach science, and there are several research reports that teachers need opportunities to continue learning science as they prepare to teach it. Thus, it is important to identify the best practices and science learning experiences that can inform the preparation of teachers. Additionally, it is possible to understand the factors that include usefulness and perceived ease of technology as a special case in teacher preparation. Moreover, the focus of the literature review and revision of research work is to understand the affordances and limitations of different learning environments to support and provide a positive science learning experience to teachers with the intersection of science and technology as a particular case.
Can We Predict Big 5 Personality Traits from Demographic Characteristics?
(UTSA Office of Undergraduate Research, 2022-12) Woods, Ethan; Han, David
Here we aim to predict the Big Five personality traits based on the demographic information using a generalized linear model. Data was obtained from openpsychometrics.org, pre-processed in MS Excel, and imported to R for statistical analysis. First, it was attempted to predict each individual response item using an ordinal regression model. It was however found to be not viable, even after various weightings were applied to the demographic data. The response variables were then aggregated to form five categories, one for each personality trait: conscientiousness, agreeableness, neuroticism, openness to experience, and extraversion. We then applied a dimension reduction technique to the country variable as well as the race variable in order to achieve an adequate model fit. It was determined that although the demographic information could be useful, precise prediction of the Big Five traits require other information that was not captured in the dataset.
Detecting Ventilator Associated Pneumonia Through On-board Endotracheal Tube Diagnostics
(UTSA Office of Undergraduate Research, 2022-12) Johnson, Benjamin; Christenson, Chase; Rahman, Mikayla; Guda, Teja
Ventilator-associated pneumonia (VAP) is a burdensome healthcare-associated infection which puts 54% of intensive care unit (ICU) life support patients across the United States at serious risk. Half of all antibiotics in the ICU are prescribed for VAP, but due to widespread inadequate treatment (up to 30% of cases), mortality rates remain as high as 24-76%. Inadequate treatment stems from lack of diagnostic and monitoring capacity. The current standard of detection is a non-specific complete blood count (CBC) completed every 24 hours. CBCs may take up to an additional 24 hours to process, allowing the infection to grow and become more difficult to treat for a total of 48 hours. To address these issues, this study conceptualizes a passively operated, high-fidelity, and high-frequency bacterial monitoring device to detect the presence and concentrations of bacteria commonly encountered in VAP. Electrochemical Impedance Spectroscopy (EIS) has been utilized extensively in electrochemical industry applications such as acid battery testing and, more recently, as a sensitive method for biofouling quantification. However, EIS has not been implemented clinically. The selected design will use a specialized EIS sensor to analyze mucosal excretions of intubated patients and quantify the bacteria present. This technology can alert physicians of infection 24-48 hours earlier than currently possible, allowing patients to receive treatment faster and thus potentially reducing their length of stay (LOS) in the ICU by ~6 days. Our findings project that this approach would lower each VAP patient’s treatment cost by approximately $24,000 and would save healthcare systems $3,600 per ICU patient (rates and estimates determined in 2019).
The Effect of Early Intervention on the Development of Receptive and Expressive Language Skills on Toddlers with Autism Spectrum Disorder
(UTSA Office of Undergraduate Research, 2022-12) Wenske, Gabrielle T.; Ewoldt, Kathy B.
It is well known that one of the key characteristics in detecting Autism Spectrum Disorder (ASD) is difficulties with communication, along with social and cognitive impairments and repetitive behaviors. Difficulties with communication include deficits in both the understanding of language, known as receptive language, and the use of language, known as expressive language. The acquisition of language skills in toddlers with ASD differs from that of their typically developing peers. While both receptive and expressive language skills tend to be lower in individuals with ASD than neurotypical learners, researchers have found that learners with ASD tend to demonstrate greater impairments in the understanding of language than their use of language. This paper will outline the relationship between the development of expressive and receptive language skills in individuals with ASD in comparison to neurotypical individuals and individuals with developmental delays, as well as explore ways in which teaching these language skills have proven to be effective based on these findings.
An expectation-maximization algorithm for estimating the parameters of the correlated binomial distribution
(UTSA Office of Undergraduate Research, 2022-12) Bennett, Andrea; Wang, Min
The correlated binomial (CB) distribution was proposed by Luceño (Computational Statistics & Data Analysis 20, 1995, 511–520) as an alternative to the binomial distribution for the analysis of the data in the presence of correlations among events. Due to the complexity of the mixture likelihood of the model, it may be impossible to derive analytical expressions of the maximum likelihood estimators (MLEs) of the unknown parameters. To overcome this difficulty, we develop an expectation-maximization algorithm for computing the MLEs of the CB parameters. Numerical results from simulation studies and a real-data application showed that the proposed method is very effective by consistently reaching a global maximum. Finally, our results should be of interest to senior undergraduate or first-year graduate students and their lecturers with an emphasis on the interested applications of the EM algorithm for finding the MLEs of the parameters in discrete mixture models.
Meta-analysis of Odds Ratios from Heterogeneous Clinical Studies
(UTSA Office of Undergraduate Research, 2022-12) Song, Mina; Belle, Macy; Han, David
Many systematic reviews of randomized clinical trials require meta-analyses of odds ratios. A conventional method estimates the overall odds ratios via weighted averages of the logarithm of individual odds ratios. However, this approach has several deficiencies due to the underlying assumptions and approximations. The goal of this study is to understand and quantify the methodological pitfalls in conducting a meta-analysis of odds ratios. The fixed-effect and random-effect models of pooled odds ratios are compared by applying to a meta-analysis of SNP studies. A popular statistical software R is used for the analysis along with SPSS and SAS. It is found that the point estimates and confidence intervals for the overall log odds ratio can differ substantially between the traditional and alternative methods, which would affect the resulting statistical inferences. It is recommended that for producing reliable results, the traditional methods for meta-analysis of odds ratios should be discouraged.
Near-Death Experiences: Life After "Death" and Credibility, a Mixed Method Design
(UTSA Office of Undergraduate Research, 2022-12) Leary, Micah; Hathcote, Andrea
The term near-death experience (NDE) encompasses a broad spectrum of experiences that typically occur when the physical body is under extreme duress or close to death. The goal of this study is to examine narratives of near-death experiences and distinguish their common themes and to examine perceived credibility of self-reported near-death experiences.
Performance of Machine Learning Algorithms for Heart Disease Prediction: Logistic Regressions Regularized by Elastic Net, SVM, Random Forests, and Neural Networks
(UTSA Office of Undergraduate Research, 2022-12) Ikpea, Obehi Winnifred; Han, David
Heart disease, a medical condition caused by plaque buildup in the walls of the arteries, is the leading cause of death in the U.S. and worldwide. About 697,000 people suffer from this condition in the U.S. alone. This research project aims to assess and compare the performance of several classification algorithms for predicting heart disease so that the method can be considered as a clinical indicator of cardiovascular health. These methods include multiple logistic regression regularized with or without elastic nets, support vector machine, random forest, and artificial neural networks. A low prevalence of the disease is reflected in the data imbalance, and an oversampling technique is also suggested to deal with the computational challenges posed by this data imbalance.
Policy-Guided Susceptible-Infected-Recovered Modeling of the COVID-19 Spread in Texas
(UTSA Office of Undergraduate Research, 2022-12) Woods, Ethan; Han, David
The goal of this research was to create an SIR model for the Texas COVID-19 cases based on the state data from March of 2020 through October of 2020, and to investigate the impact of public policies on the transmission of COVID. The data was pre-processed using Excel; some basic time series graphs were produced in Excel as well. All other data analysis, including the production of all graphs relating to the SIR model, was performed in R. Difficulty in estimating the model parameters by the maximum likelihood method was encountered due to the short durations between the implementation dates of various policies designed to curb the spread of COVID-19. Examining the estimate trends of beta, gamma, and R0, a stabilizing pattern for R0 was observed over time, which would require further investigations to understand the epidemiology of COVID-19 in Texas.
A School Connectedness Program for Student Social-emotional Knowledge and Skills
(UTSA Office of Undergraduate Research, 2022-12) Schertzer, Rylee; Penyweit, Kyndall
School connectedness, student perception of how much school staff care about their academic and social success, is a protective factor found to decrease risk for student behavioral problems that disrupt learning. A small group of elementary-aged students in an afterschool program participated in six weekly lessons of a school connectedness curriculum that focused on social-emotional learning knowledge and skills. Before and after the curriculum intervention, student participant social skill knowledge was assessed. In addition, afterschool staff members observed and reported student participant level of positive social behavior exhibited. Student knowledge scores significantly improved, however, observed pro-social behaviors did not significantly differ pre-post program. Many previous school connectedness studies have concentrated on secondary-level students, and more studies in the non-school setting were suggested. The current study contributes to the literature as it examined elementary-level students and occurred in the out-of-school time setting.
Stem Cells used for Tissue Engineering of Articular Cartilage: Literature Review
(UTSA Office of Undergraduate Research, 2022-12) Advano, Dhillon R.; Abu-Lail, Nehal I.
Adult articular cartilage (AC) has a limited self-healing capacity. Cartilage defects lead to osteoarthritis (OA) characterized by severe pain and impaired mobility. Currently, there are no approved treatments for OA that successfully reverse or heal structural defects permanently. Although techniques such as microfracture, arthroplasty and subchondral drilling have been effective at treating small to intermediate sized AC defects over the short term, a long-term solution for OA is still necessary. In recent years, research has focused on tissue engineering of articular cartilage (TEAC) as a potential treatment option for OA. TEAC therapies utilizing chondrocytes such as autologous chondrocyte implantation (ACI) are promising but are limited by their complexity, high cost and inability to promote the formation of healthy hyaline AC. Due to the limitations of ACI, stem cells have been investigated as an alternative cell source for TEAC. To engineer AC, stem cells are allowed to differentiate on/in a scaffold in a bioreactor that controls chemical, physical and biological cues to support the chondrogenic potential of the stem cells. The use of stem cells provides numerous advantages as treatment costs can be lowered, the number of required surgeries can be reduced and high-quality AC can be formed. Mesenchymal stem cells (MSC) in particular are advantageous in that they are easily available and can be extracted from a diverse range of tissues including, bone marrow, adipose, and synovium. Each type of MSC have their own advantages and disadvantages but generally each of them possess high chondrogenic potential and immunosuppressive capacities. Induced pluripotent stem cells (iPSC) have also been recognized as a promising cell type for TEAC due to their unlimited proliferation and self-renewal capacities. Ultimately, each cell source has potential for use in TEAC therapies but further studies comparing cell sources are required before a gold standard can be determined. This review summarizes the pros and cons for potential use of each stem cell source in TEAC. The review is not meant to be comprehensive of the current literature.
Uncommon or Underdiagnosed? The Effectiveness of the Current Diagnostic Criteria for Autism Spectrum Disorder in Women
(UTSA Office of Undergraduate Research, 2022-12) Gonzalez, Alondra; Ewoldt, Kathy B.
Children need to develop in positive learning environments with proper educational and developmental supports to achieve their highest potential and have positive postsecondary outcomes. Since students with disabilities often struggle with accessing curriculum without necessary supports, accuracy in early childhood detection and intervention of developmental delays is very important in the field of Special Education. While the diagnostic criteria of many common developmental delays are becoming more consistent and accurate at identifying children who may have special needs, there are still many factors that educators and diagnosticians are not considering when creating and evaluating said criteria. The idea of camouflaging, or “masking”, emotions or behaviors could be a factor to consider when looking at the disproportionate representation of male to female children receiving special education services in the US. Masking or camouflaging in psychology is a term that means the ability to conceal one’s emotions or reactions in order to achieve a desired outcome. This skill develops quickly in young girls, and can make diagnosing many mental health disorders or general health issues very difficult as key behaviors/symptoms can be suppressed or not severe enough for concern. If a child is masking behaviors associated with a developmental disability, like Autism Spectrum Disorder, they may not receive early childhood interventions until much later in life which may affect their success in the classroom. Knowing that masking occurs in young children, there is a need to evaluate the effectiveness of the current diagnostic criteria of Autism Spectrum Disorder and other developmental disorders to see if girls are under-diagnosed due to alternate manifestations of common signs due to gender and gender norms.
Words as Weapons: A Discourse Analysis on the Weaponization and Mobilization of Language
(UTSA Office of Undergraduate Research, 2022-12) Cavazos, Richard R.; Drinka, Bridget
Within the past hundred years, rhetoric has been often used to push agendas that can become divisive and dangerous. Such was the case with Adolf Hitler in Nazi Germany, Radio Télévision Libre des Mille Collines (RTLM) in the Rwandan genocide, and former U.S. President Donald J. Trump. While all agents utilized numerous rhetorical strategies, a close analysis of speeches, transcripts, and broadcasts reveal language styles and rhetoric had implicit meanings that influenced audiences/supporters and resulted in direct ramifications. Built on a Burkeian framework of rhetoric, this analysis argues that the previously mentioned agents weaponized language as well as mobilized their audiences into action. The analysis focuses on both complex and simple styles of language not focused on in previous literature.

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