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Identifying Patterns of Special Education Teacher Turnover and Their Relationships With Measures of Principal Experience
No full textBackground: Teacher turnover takes various forms, including changes in role, location, or sector, while some teachers leave the profession altogether. Teacher attrition feeds teacher shortages, which are especially grave in the area of special education. Special education teacher attrition is not only costly for schools due to the expenses of recruitment and training but also has negative consequences for students and their families. The causes of special education teacher turnover are many, but research has identified principals as playing a key role. What remains unknown are the timing and frequency of the various types of turnover that special education teachers exhibit, and whether those patterns of turnover are associated with principal characteristics Purpose: This longitudinal quantitative study seeks to measure special education teacher turnover in West River Public Schools (WRPS, a pseudonym), a large public school district in Texas. By determining whether, when, and to where WRPS special education teachers turn over, this study identifies unique patterns of attrition. Further, it uses inferential analyses to examine the relationship between special education teacher retention and two measures of principal experience. Method: The study centered on 779 academic year-specific observations of all 210 WRPS special education teachers that were new to the profession or the district between the 2012-2013 and 2016-2017 academic years. Through data visualization, (specifically, alluvial diagramming) I demonstrate the proportional flow of teachers into and out of five categories of turnover. The categories include transfers, moves to general education, role changes, and system exits. Additionally, correlational analyses measure the direction and strength of the relationship between the number of years that teachers remain at their original placement and two measures of principal experience. Conclusion: WRSP special education teacher turnover is very high, marked by incredibly short tenures and an overwhelming number of system exits. The low variability in teacher retention may be a causal factor for the lack of any discernible linear relationship with principal experience
Learner Experiences of Using Embedded Self-Regulated Learning Tools in Self-Paced Short Online Professional Development Courses
No full textBackground: The increasing shift toward online professional development (PD) highlights the critical role of self-regulated learning (SRL) for adult learners. SRL involves strategies that empower learners to actively engage in their learning process. As self-paced learning increases, strong SRL skills become essential. Research suggests that embedding targeted tools into the course design can enhance the use of these strategies. However, there is a critical gap in understanding the benefits of SRL tools in short, self-paced online courses. Purpose: This study investigated adult learners’ experiences while engaging with embedded SRL tools. The primary objectives were to explore the benefits and challenges presented by these tools and learners’ activation of SRL strategies when using the tools in self-paced learning environments. Methods: Using a basic qualitative research design within a constructivist inquiry framework, this study analyzed data from ten local government employees enrolled in self-paced online PD courses. Data collection included a pre-interview questionnaire, course evaluations, and online semi-structured interviews. Analysis followed a two-phase approach consisting of an initial analytic induction to address a priori codes followed by constant comparison to ensure consistency and accuracy in applying codes. Thematic analysis was used to discern patterns and build the narrative. Results: Three key findings were identified. First, participants used environmental structuring and attention-focusing strategies during work hours to mitigate workplace distractions; the application of these strategies was facilitated by goal setting, self-motivation, and course design considerations. Second, while 70% found the course navigation intuitive, a subset experienced challenges that impacted their progress. Third, participants demonstrated meaningful engagement with SRL-embedded tools, particularly “Pause and Reflect” activities, to enhance task strategizing, metacognitive monitoring, and self-instruction. Participants also valued the “Expert Insight” videos and “In Real-Life" examples and recognized quizzes as valuable for self-evaluation. Conclusion: This study demonstrates that strategically designed self-paced courses with embedded SRL tools can support learning continuity. The study offers insights into design approaches for integrating SRL tools into PD self-paced short online course design. Future research could examine the long-term impact of these tools on workplace skill application and their effectiveness across different professions
Two Major Problems at Once: The Effects of Tropical Cyclone Disasters on Chronic Absenteeism in Louisiana
No full textBackground: Chronic absenteeism is a complex and escalating issue in the United States, and it has significant adverse outcomes for students. Chronic absenteeism refers to the cumulative missed learning opportunities of students frequently absent from school, regardless of whether the absence is unexcused or excused. Research indicates that chronic absenteeism rates are disproportionately higher among students who are economically disadvantaged (SED) and students with disabilities (SWD) compared to their more affluent and non-disabled peers. As of February 2024, 72.8% of Louisiana's public school students were identified as economically disadvantaged. Simultaneously, Louisiana, a state frequently impacted by tropical cyclones, presents additional unique challenges. Purpose: This study aimed to expand the research on chronic absenteeism by focusing on Louisiana, particularly for SEDs and SWDs, and investigate the potential exacerbation of chronic absenteeism by ongoing tropical cyclone events. Method: This quantitative study used a descriptive causal-comparison design to examine group differences in chronic absenteeism rates across special populations (SEDs and SWDs) in Louisiana public schools during the 2022-2023 school year. The study also analyzed whether a school's location, as it relates to coastal vulnerability, affects chronic absenteeism rates for SEDs and SWDs. The 2010 Science-Based Boundary map, identifying coastal influence by area, was used to categorize schools into four groups, from coast to inland. Results: The study found that irrespective of subgroup, considerably high chronic absenteeism rates were found across Louisiana schools. Consistent with the literature, SEDs exhibited higher chronic absenteeism rates than peers without this designation. No significant differences in chronic absenteeism rates between SWDs and Regular Education students (RESs) were found. The study revealed that SEDs, SWDs, and RESs exhibited high chronic absenteeism rates regardless of a school's geographical location and coastal vulnerability. Conclusion: This study found that socioeconomic status (SES) emerges as a significant risk factor for chronic absenteeism across schools, especially as there is a preponderance of students living in poverty across Louisiana schools. Therefore, educational leaders must understand disaggregated student-level data and engage in adaptive leadership and disaster resilience education to address chronic absenteeism
Tracking the Water pH and Atmospheric Co₂ Evolution During Synthetic Carbonate Precipitation
No full textPaleo-CO2 reconstructions are made possible by the use of well-calibrated proxy data (like stable isotopes) to infer the conditions in which the paleo-archivers formed. Among the largest reservoirs for CO2 on the planet, carbonates are valuable archivers for a multitude of paleoclimate datasets. However, field and archive specific measurements that apply such proxies do not always have equivalent laboratory calibrated results that are temporally monitored to serve as a metric for their reconstructions. In this research, laboratory experiments have been performed, precipitating both biogenic and abiogenic calcium carbonate in a laboratory-constrained environment with known temperature and pressure conditions in an enclosed atmosphere. Changes in the water’s pH were monitored and the evolution of the atmosphere was simultaneously recorded using the Aerodyne Tunable Infrared Laser Direct Absorption Spectroscopy (TILDAS) of the PaeloGeochem research group which monitors changes in CO2 and measures the corresponding triple oxygen isotopic signatures of the synthetic atmosphere during carbonate precipitation. Aliquots were collected from each experimental solution to measure the total dissolved inorganic carbon (DIC) and its isotopic signature (δ13CDIC). Carbonate samples were measured for their δ13C and δ18O and their corresponding mineral fabrics were studied using scanning electron microscopy (SEM) and X-Ray diffraction (XRD). Our data suggests different isotopic signatures, pH evolutions, and CO2 evolutions between biogenically and abiogenically precipitated calcium carbonates and that the biogenic experiment produced the smallest ranges of all these measured variables compared to the abiogenic experiments. Additionally, we found that the crystal structure and phase composition differed between abiogenic and biogenic experiments showing the abiogenic experiments to be 100% calcite, and the biogenic experiment being primarily (>90%) aragonite. Although still preliminary, we anticipate that information from this constrained environment monitoring will serve as a reference for field-collected samples for modern environment calibration and for future research on paleoclimate reconstruction. This research can additionally bring new insights into the tracking of CO2 injected plumes from carbon capture utilization and storage (CCUS) technologies using stable oxygen proxies
Economic News, Social Media Sentiments, and Stock Returns: Which Is a Bigger Driver?
No full textThis study provides empirical evidence on the relative impact of innovations in information content and noise embedded in economic news and social media sentiments on DJIA, S&P 500, NASDAQ, and Russell 2000 index returns. We find that economic news sentiments are relatively more rational and have a greater impact than irrational social media sentiments. There exist significant negative effects of three distinct categories of social media sentiments and a significant positive impact of economic news sentiments on stock returns. The magnitude of the impact of the economic news sentiments is larger. In addition, the economic news sentiments seem to have greater information content and are driven by risk factors to a greater extent than the sentiments of social media, which probably contain more noise. There are significant negative responses of stock returns to irrational components of social media sentiments while significant positive responses to rational components of economic news sentiments. Lastly, the magnitude of the impact of rational economic news sentiments is higher than that of irrational social media sentiments. Our results are consistent with the view that business news is a manifestation of a rational outlook to a larger extent than social media and can drive stock valuations
Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography
No full textBackground: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to analyze the integration of AI into echocardiography. Key AI functionalities, such as image acquisition, standard view classification, cardiac chamber segmentation, structural quantification, and functional assessment, were assessed. Comparisons with traditional imaging modalities like computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) were also explored. Results: AI algorithms demonstrated expert-level accuracy in diagnosing conditions such as cardiomyopathies while reducing operator variability and enhancing diagnostic consistency. The application of ML was particularly effective in automating image analysis and minimizing human error, addressing the limitations of subjective operator expertise. Conclusions: The integration of AI into echocardiography marks a pivotal shift in cardiovascular diagnostics, offering enhanced accuracy, consistency, and reliability. By addressing operator variability and improving diagnostic performance, AI has the potential to elevate patient care and herald a new era in cardiology
Persevering Through Stress: An Examination of Professional Rejection During the Job Search
No full textExperiences of and reactions to work-related rejection (referred to as “professional rejection”) have not been thoroughly explored in the I-O psychology literature, yet the outcomes may have important implications for how individuals progress in their careers. Job seekers are uniquely faced with repeated professional rejections, which may exacerbate the already difficult circumstances of unemployment and lead to increased stress. Professional rejection sensitivity (an individual’s propensity to react negatively to work-related rejection experiences) was expected to influence how job seekers handle rejection by amplifying this stress response. Work centrality was also expected to play a role in whether job seekers choose to persevere or pivot career paths following professional rejection, with the expectation that the importance of one’s work identity to their self-view would make them more likely to persevere. Drawing from affective events theory, the current study surveyed unemployed job seekers at three time points to gain a better understanding of their reactions to professional rejection, dispositional and identity-related traits that may influence these reactions, and how these experiences may impact their career trajectories. While none of the hypotheses were supported, initial results provide insight into how future research may better explore these phenomena and contribute to an emerging stream of research related to professional rejection
Energy Simulation-Based Assessment of Traditional and Modern Wall Materials for Thermal Performance: A Case Study of a Traditional House in Jordan
No full textIn this study, the energy performance of traditional, modern, and insulated wall assemblies in a heritage residential building in Al Salt city, Jordan, is evaluated using the simulation software DesignBuilder version 7.0.2.004. The case study compares the thermal behavior of traditional thick limestone walls, modern reinforced concrete and block-based walls, and contemporary insulated systems under local climatic conditions. The results show that traditional stone walls exhibit limited energy efficiency and require insulation to meet contemporary standards. However, they perform better than modern concrete walls based on their thermal mass. While concrete walls with inadequate insulation exhibit the poorest performance and are associated with significantly higher energy demand and CO<sub>2</sub> emissions, insulated wall systems that combine stone with insulation layers demonstrate the best thermal performance and achieve substantial reductions in energy use and environmental impact. These findings emphasize the feasibility of upgrading heritage buildings through the integration of modern insulated wall assemblies, which can lead to considerable energy savings and a lowered carbon footprint while simultaneously keeping the architectural identity and cultural value
Virtual Reality Centric Stress Detection Using Dynamic Baseline Calibration
No full textWith the increasing adoption of virtual reality (VR) in research and training applications, reliable stress detection in naturalistic settings remains challenging, particularly when hardware complexity must be minimized. This study presents an enhanced framework for real-time stress recognition in VR environments that integrates behavioral interactions with selectively derived physiological signals. Building upon previous architectures, the proposed framework incorporates pre-task baseline measurements to account for subject-specific and session-initial variability. While the comprehensive analysis employs a three-class affective framework, the practical implementation focuses on binary stress detection for real-world VR applications. Stress detection is achieved through VR-based behavioral signals, complemented by minimal input from a Galvanic Skin Response (GSR) sensor. The experimental evaluation demonstrates that baseline calibration improves separation across stress conditions. Quantitatively, the proposed Weighted Baseline Detector (WBD) achieved a classification accuracy of 94.17% and an Area Under the Curve (AUC) of 0.9993, outperforming the fixed global baseline approach (85.0% accuracy, AUC 0.9067), which demonstrates the effectiveness of the proposed calibration method. Rigorous cross-validation confirms that the approach achieves stable performance with statistical significance across stress conditions. These findings highlight the potential of combining behavioral analysis with physiological support to develop practical, low-hardware VR platforms for live stress recognition
Magnetic Iron Oxide Nanoparticles: Advances in Synthesis, Mechanistic Understanding, and Magnetic Property Optimization for Improved Biomedical Performance
No full textMagnetic iron oxide nanoparticles (MIONPs) represent a versatile magnetic nanoparticle (NP) system with considerable, yet underexplored, potential in diverse applications, particularly in emerging biomedical fields such as magnetic resonance imaging, magnetic hyperthermia, targeted drug delivery, and biosensing. The successful translation of MIONPs into these applications requires reproducible synthesis methods and precise control over particle uniformity in terms of size, shape, and composition. However, reproducibility in nanoparticle synthesis remains a persistent challenge, limiting the ability of researchers to replicate results and integrate MIONPs into application-oriented studies. In recent years, substantial efforts have been directed toward elucidating synthesis mechanisms and improving both reproducibility and particle uniformity, enabling notable advances in the biomedical deployment of MIONPs. This review summarizes progress in the synthesis of MIONPs, with emphasis on three widely employed precursors: iron oleate, iron acetylacetonate, and iron pentacarbonyl. The discussion focuses on key findings in NP synthesis, relevant chemical aspects, and the magnetic properties of MIONPs, which are critical for optimizing their functional performance. By consolidating recent advances, this review aims to provide a reliable framework for the preparation of high-quality MIONPs and to support their effective use in specific biomedical applications