Alamo Colleges District

This study examines various job demands (emotional labor, secondary trauma exposure, student-related stressors) and job resources (organizational support, self-care) using the Jobs Demands-Resource Theory to analyze professional quality of life (compassion fatigue/satisfaction) for university faculty members. Research questions are related to the predictive relationship among these variables and outcome of quality of life. While studies have examined various aspects of these relationships, the research is scant regarding higher education faculty. Our findings indicated the job demands, in general, did predict higher compassion fatigue and lower compassion satisfaction. One salient result indicated student related stressors mediated the positive relationship between exposure to student trauma and burnout. When stressors were controlled, trauma no longer predicted burnout. Similarly, when stressors were controlled for compassion satisfaction, there was a positive relationship between exposure to student trauma and compassion satisfaction. Discussions of student-centered and trauma-informed practices within the classroom require information for how these concepts may affect faculty.

Los simuladores virtuales (SV) se han convertido en verdaderos escenarios del conocimiento, al ser capaces de trasladar lo abstracto a lo visible y lo inaccesible a lo manipulable. Sin embargo, pocos estudios han explorado, analizado y sistematizado el impacto de los SV en la enseñanza de las ciencias. El método fue una revisión sistemática, aplicando la metodología SALSA (Búsqueda, Evaluación, Síntesis y Análisis), con metaanálisis para definir el efecto de los SV en el aprendizaje de los estudiantes. La muestra fue de 31 estudios extraídos de Scopus, SciELO, PubMed y Latindex 2.0, en los que en primer lugar se analizó el papel educativo de los SV en la última década; de ellos, 16 estudios fueron metaanalizados con un total de 1296 estudiantes. El efecto medio estandarizado de intervenciones con SV fue grande (1.73, IC95% [1.00;2.45]; Z = 4.67, p = .001) frente a los métodos tradicionales utilizados, con una alta heterogeneidad (I2 = 94.8%, p < .01); variables como el nivel educativo, tipo de simulación, enfoque metodológico y duración de programas modularon la efectividad de los SV. Estos hallazgos respaldan que los SV son un recurso eficaz para la enseñanza de las ciencias, siempre y cuando se articulen a procesos híbridos de aprendizaje.

As large language models (LLMs) increasingly permeate diverse industries, it has become crucial to comprehend the mechanisms underlying their moral decision-making processes. Existing studies on the moral decision-making processes of LLMs have been limited in their evaluation of ethical theories. The datasets utilized in these studies relies on human preferences, which introduces a set of inherent flaws. This paper utilizes different prompting styles to assess LLMs on moral ethical questions, addressing challenges associated with human preferences. The contributions include a dataset with human preferences and LLM preference on diverse ethical theories namely commonsense, virtue ethics, justice and deontology. We scrutinize these ethical decision-making on GPT-4 Bard, Mistral, and LLaMA2 to create a broad understanding of ethical reasoning capabilities across different platforms. We used baselines metrics such as accuracy, and F1-score for the evaluation purposes. GPT4 demonstrated a remarkable alignment with human ethical judgments, achieving an average accuracy of 81.5%. By including AI-generated justifications for ethical decisions, our study not only enhances the transparency of AI decision-making but also provides a foundational understanding of how LLMs conceptualize and apply ethical principles.

Large Language Models (LLMs) have demonstrated exceptional capabilities across various tasks, including anomaly detection in time-series data, which is even critical for ensuring the safety, reliability, and cybersecurity of connected vehicles (CVs). CVs rely heavily on sensors to perform essential operations, yet these sensors often lack robust safety mechanisms, leaving them vulnerable to adversarial attacks that can lead to incorrect measurements. This study investigates the potential of LLMs in detecting sensor faults in CVs through fined tuning along with various prompting strategies, including zero-shot and chain-of-thought techniques, and compares their performance against traditional machine learning models. Using the TampaCV BSM dataset, four types of faults—Drift, Stuck-at, Hard-Over, and Trend—are introduced to evaluate the fault-detection capabilities of LLMs. While initial results indicate that LLMs underperform compared to traditional models, significant improvements are observed through fine-tuning with fault-initiated datasets.

This case study analyzes the response to Winter Storm Uri through the lens of adaptive governance, examining how Texas and the City of San Antonio navigated the disaster across mitigation, preparedness, response, and recovery phases. Drawing from document analysis and interviews, the study identifies key challenges in representation, decision-making, public communication, and cross-sector coordination. Despite systemic weaknesses, the crisis prompted innovative responses and institutional learning. The findings underscore the urgent need for integrated, inclusive, and flexible governance structures to prepare for future climate-related disasters. Adaptive governance emerges not as a theory, but as a practical necessity for disaster management.