5026 research outputs found
Sort by
New Science Teacher Experiences in a Mentor Program with Same Content vs Different Content Mentors: A Qualitative Study
Full text linkThis qualitative case study explores the impact of mentor content knowledge on the experiences of novice secondary science teachers and what qualities novice science teachers find important in a mentor. This study investigates whether having a content-specific mentor affects a new science teacher’s self-efficacy, teaching practices, and career satisfaction compared to those mentored by teachers from different content areas. Using Social Cognitive Theory as a framework, the study examines how the interactions between mentors and mentees shape new teachers’ development and instructional effectiveness. Data was collected through participant images, card sorts, and interviews to uncover themes regarding the value of content-specific mentorship and its influence on new teachers’ confidence and retention. The findings of this study showed that mentor content background does affect the experiences of new science teachers in a mentor program. New teachers with science mentors felt more supported and had reported higher levels of self-efficacy. New teachers also reported valuing qualities associated with professionalism slightly higher than personal qualities. The findings emphasize the importance of tailoring mentorship programs to include content-specific support, especially in science education, where subject matter expertise is critical for teaching effectiveness. This research contributes to the limited literature on mentorship program design and provides actionable insights for improving teacher retention and instructional outcomes in science education. By addressing gaps in mentor mentee alignment, educational leaders can develop strong systems to support novice teachers and foster long-term success in the teaching profession. The findings aim to inform future mentorship program designs, ultimately supporting teacher retention and improving instructional outcomes in science education
Using the College Choice Model to Examine Why HBCU Graduates Choose to Attend PWIs for Advanced Studies
Full text linkThe purpose of this study is to examine why African American students choose to attend Historically Black Colleges and Universities (HBCUs) for undergraduate and Predominately White Institutions (PWI) for graduate studies. Many college students have extenuating circumstances that influence their decision to seek higher education, specifically, HBCU’s. The study will investigate student influences and objectives of the college decision. Also, understanding institutions influences recruitment, retention, and college choice. The study will explore comprehensive and understanding of African American students of various backgrounds, also, understanding the experience for African American students excelling in their academic, social, and personal lives during their undergraduate studies. To examine HBCUs’ unique approach and commitment to providing quality education to all students regardless of social status. Yet, after graduation, many African American students seek advanced certification or degree, however, not at their original institution or another HBCU. The study was conducted with seven HBCU’s in the southeast region of the United States. Participants in the survey included ten HBCU and PWI graduates, male and female. During this qualitative descriptive case, interviews were used to examine why students choose to attend Historically Black Colleges and Universities and Predominately White Institutions. This qualitative research will use the phenomenological model to gather information from the participants. A structured interview is used to collect data, virtual recordings, background survey, standard questions, compare, contrast results, and discussion. This study will investigate the effectiveness of HBCUs and PWI recruitment, retention, practices, and procedures for African American students obtaining a degree(s) and prepare to work as a professional in the workforce and community. This study will also investigate academic options, atmosphere and experience, financial challenges, and opportunities, and socioeconomical background associated with the college decision-making
Muscogiana Vol. 36(1), Spring 2025
No full textMuscogee County, Columbus, Georgia, Genealogyhttps://csuepress.columbusstate.edu/muscogiana/1076/thumbnail.jp
Exploring Brent-Kung Adders in Balanced Ternary CMOS Logic
No full textThe modern computer operates on a 64-bit architecture. These devices can store large numbers and precise decimals, but more advanced devices are needed to support progressing technologies every day. A more efficient system with higher speeds and larger operable numbers would be a key to optimization of computation as we know it. The ternary device, operating in base-3, has the potential to be that optimization. However, binary technology has such precedent and research that it is a difficult gap to span to compare the ternary system to the modern binary system. With a more advanced adder and optimized gates using the CMOS implementation of ternary logic, that gap becomes smaller
Cultivating Mindsets and Transformative Practices of Beginning Alternative Certified Teachers: A Teacher Perception Study
No full textThe shortage of qualified teachers in the United States has been a persistent issue for decades. The number of qualified candidates to fill teaching vacancies has fallen short as the number of teachers needed to educate today continues to increase (Carothers et al., 2019). Traditionally, teacher preparation programs were the primary source of certified teachers. However, these programs have been decimated by declining student enrollment and by criticisms that they do not meet the needs of prospective teaching candidates today (Darling-Hammond, 2020). In recent years, the shortage has become even more acute, with some regions facing severe teacher shortages (Carothers et al., 2019). This shortage of qualified teaching candidates has negatively impacted student achievement by undermining instructional experiences designed to promote it; therefore, the issue of teacher preparation cannot be taken lightly by policymakers (Putman & Walsh, 2021). In response to the overwhelming challenge of teacher shortages, a variety of preparation programs have emerged as an alternative to traditional programs. These programs offer prospective career switchers with field expertise the opportunity to become certified teachers. While alternatively prepared candidates may lack the pedagogy and skills of traditionally trained teachers, they bring valuable expertise and maturity to the classroom (Matsko et al., 2022). Various organizations and institutions, including universities, school districts, and nonprofit organizations, offer alternative preparation programs. These preparation programs offer prospective teaching candidates a viable, quicker option to enter the teaching profession without expectations of traditional route certification (Matsko et al., 2022) Alternative preparation programs have become increasingly popular in recent years, with approximately 1 in 5 new teachers entering the classroom through them. These programs attract a diverse pool of candidates who may not have considered teaching as a career option through
traditional routes. Candidates in these programs bring unique experiences and perspectives to the classroom, which can be valuable for students from diverse backgrounds (Woods, 2016; Darling- Hammond, 2020; Matsko et al., 2022). Therefore, the purpose of this study was to explore the perceptions of initial preparation training, support, and resources through the lived voices of alternatively certified educators. Examined through the lens of Carol Dweck’s (2006) Growth Mindset Theory and Shulman’s (1986) PCK Framework, data were collected from nine elementary alternatively certified educators representing two GaTAPP program providers and six local school districts in the state of Georgia. The study used open-ended questionnaires, semi- structured interviews lasting approximately 45-60 minutes, and artifact collection to gather data on the lived experiences of alternatively certified educators. Using Saldana’s (2019) coding technique, data were systematically coded using open, axial, and selective coding, leading to theme development. The overarching findings presented six themes representing the lived experiences of alternatively certified educators: Career transition, support systems, gaps in preparation, instructional and pedagogical growth, growth mindset, and professional growth and identity. Participants acknowledged that mentorship, collaboration, reflective practices, and consistent feedback helped them build confidence while deepening their pedagogical content knowledge and transforming their professional identity into one that sustained their sense of belonging in the education field. The findings of the study have important implications for local school districts, program providers, and policy makers as they work collaboratively to plan, prepare, and strengthen the teaching pipeline, emphasizing the importance of continued collaboration of all stakeholders to incorporate growth mindset training, create a coaching culture, and sustain continued support beyond initial certification completion
Exploratory Data Analysis (EDA) and Predictive Machine Learning (ML) for Buildings’ Energy Fault Detection
Full text linkBuilding energy load fault detection is a critical challenge in energy usage analysis. It helps uncover energy wastage, machinery/appliance degradation or inefficiency, and failures or faults in buildings’ HVAC (heating, ventilation, and air conditioning) systems. Early identification of machinery failure and energy wastages due to operational maintenance negligence in large sites such as campus buildings is indispensable for achieving energy efficiency. This is crucial for saving patrol and minimizing the response time to restore the building appliances or systems to their optimal state.
Advancements in state-of-the-art AI/ML data-driven algorithms and techniques enabled us to build accurate, efficient and scalable fault detection systems with research-backed results. This study leverages ML techniques to present a framework explicitly designed to operate effectively in unlabeled environments where ground-truth fault data is unavailable, in this domain of anomaly detection in the energy consumption of buildings. Prominent approaches for fault detection include XGBoost (Extreme Gradient Boosting) forecasting for anomaly detection based on forecast error, unsupervised clustering techniques, neural network algorithms for forecasting based (such as long short-term memory) and reconstruction error (using transformers and spectral residual based convolutional neural network), and hybrid composite solutions combining both supervised and unsupervised learning methods.
This research aims to study and compare solutions for fault detection using various statistical and unsupervised solutions that do not require fault labels to train and deploy in a production environment. We examine the performance of regression-integrated fault detection, probabilistic regression and matrix profile threshold approaches to determine a reliable, scalable, accurate, and efficient solution for building energy fault detection systems. We benchmark these algorithms on the publicly available Large-scale Energy Anomaly Detection (LEAD) dataset. The study also emphasizes the importance of thresholding strategies optimizations, such as global versus building-specific approaches.
Our Energy load prediction model experiments proved that a building wise trained XGBoost model with lag features ranked as the best prediction model with R2 . This demonstrates that ensemble machine learning offers the strongest accuracy. However, as the paradigm “no one size fits all” says, one single model cannot generalize effectively on the entire dataset. Buildingwise models\u27 approach consistently outperformed the global model. Our study found that regression integrated fault detection with statistical methods like z-score (building-wise threshold) and IQR (global threshold) provided superior performance with an F1-score of 0.52 and 0.46 respectively. These methods outperformed probabilistic regression and matrix profile threshold methods, which both yielded F1-scores of less than 0.1, on evaluation against the complete LEAD dataset. The matrix profile approach struggled to distinguish precisely anomalies at the hourly level, which resulted in numerous false positives. Similarly, probabilistic regression requires further optimization to reduce false positives
Arden 2025
Full text linkAn amalgamation of creative student works. Items contained include, but are not limited to, art, photography, short stories, prose, poetry, and other avenues of creative composition.https://csuepress.columbusstate.edu/arden/1025/thumbnail.jp
Treatment of Obsessive-Compulsive Disorder Using Inference Based Cognitive Behavioral Therapy
Full text linkTower Day 2025 Graduate Poster 1st place Winner
Traditional cognitive behavioral therapy (CBT) has long been considered an acceptable course of treatment for obsessive-compulsive disorder (OCD). However, CBT has 38 been shown to leave behind residual symptoms after treatment. A more recent method of treatment, inference-based cognitive behavioral therapy (I-CBT), uncovers the doubts behind a client\u27s obsessions. With the growing rate of OCD diagnoses, it is crucial that counselors are able to adequately treat this disorder, teaching their clients how to replace unhealthy narratives with more realistic ones. This presentation will highlight the key aspects of I-CBT that are essential in treating the full scope of symptoms for clients with OCD