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African American Portraits of Leadership: Tarana Burke
No full textIn its African American Portraits of Leadership series, Columbus State University Libraries celebrated several African American leaders.
This video highlights Tarana Burke. Ms. Burke founded the Me Too Movement.https://csuepress.columbusstate.edu/marketing/1065/thumbnail.jp
“The Spirit Is Willing”: A Study of School Climate, Bullying, Self-Efficacy, and Resilience in High-Ability Low-Income Youth
No full textRelationship Between Grit, Academic Mindset, First-Year GPA, and Perceptions Related to Persistence for Non-Traditional College Students
Get PDFRetention of non-traditional college students has been a significant concern for postsecondary institutions, students, their families, and society. This study sought to explore the relationships between grit, academic mindset, first-year GPA, and the perceptions of students related to persistence. Braxton and associates’ Revised Theory of Student Departure in Commuter College and Universities served as the theoretical framework for this study. This study was exploratory, sequential mixed method design, incorporating survey data from 2015 as well as qualitative interview data from 2020 and 2021. Results indicated a negative, moderate relationship between grit scores and mindset scores, a weak, negative relationship between academic mindset and first-year college GPA, a positive, moderate relationship between grit scores and first-year GPA. In addition, participants perceived that having a productive academic mindset, family support, supportive faculty and staff, flexible course offerings, and affordability could be factors influencing their persistence in postsecondary education settings. Given these findings, institutions should consider developing programming to improve faculty and staff support, becoming more family friendly, utilizing intentional and flexible course scheduling, and review costs of obtaining a postsecondary credential and begin to look for more ways in which college might be more affordable
A Quasi-Experimental Study on Creativity Development
Get PDFWith this quantitative quasi-experimental study, the researcher examined the effect of the implementation of a creativity development intervention on third-, fourth-, and fifth-grade students as measured by the District Screener for Gifted Education in a central Georgia elementary school. Creativity development is of growing concern to U.S. educational and business leaders as jobs require creative ability. Using a two-way repeated-measures analysis of variance, the researcher compared the creativity raw scores from pre- and posttests of all third-, fourth-, and fifth-grade students. The researcher also compared a representative sample of subpopulations using analysis of variance to see if the intervention affected gifted-identified and non-gifted-identified populations in different ways. Results showed significant improvement among all students, and the improvement was sustained over 9 weeks following the intervention. Findings have positive implications for development of creativity in all students. This study will inform educational policy makers of the impact creativity development can have on adolescents
The Marginal Impact of a Publication on Citations, and Its Effect on Academic Pay
No full textThere are good reasons for why academicians should care about citations to scholarly articles. An important one is that members of the academy operate essentially as independent contractors, and in such cases the quality of their work must be evaluated. In the academy, such judgments are rendered about tenure, salary, and hiring for positions above the entry level. Another is that a relatively high rate of citations to a scholar’s work points toward an impressive career in academe, reflecting one’s contributions to society in that part of one’s career spent engaging in research endeavors. As such, it is essential that compensation in academe is geared toward incentivizing the production of impactful research. This study introduces a straightforward approach to calculate the marginal impact of an academic publication on total citations to a scholar’s research portfolio. This variable is then included in an earnings equation, wherein it is expected to be positively related to a scholar’s real academic pay. Using data from three separate state university systems, we find that this variable is indeed positively and significantly related to a scholar’s pay, at least in the case of research-oriented higher education institutions. More specifically, we find that an increase in this variable is associated with a 2.8 to 8.9% boost in the salaries of college and university faculty
Small Group-Delivered Literacy Based Behavioral Interventions for Young Children
No full textMany differences exist between preschool and kindergarten classrooms, including the type and time spent on fine motor activities. Children at risk for developmental delays and learning challenges as they transition to kindergarten often require direct support during preschool to learn new skills needed for kindergarten. This study expanded the storybook literacy based behavioral intervention (LBBI) research by exploring the effect of small group-delivered, electronic LBBIs on preschool students considered at-risk by their teachers. Using a multiple probe design across skills, we delivered LBBIs in a small group to teach common fine motor classroom skills (cutting with scissors, using liquid glue, and matching using one-to-one correspondence). Children acquired and maintained the new skills, then generalized the motor skills to novel materials
RUE: A caching method for identifying and managing hot data by leveraging resource utilization efficiency
No full textIn this study, we propose a caching method called RUE for dynamic large-scale data streams. We define a data model to facilitate hot data identification and management. At the heart of RUE model is hot degree that takes into account two factors data resource utilization efficiency and reuse distance, aiming to quantitatively reflect data popularity in a dynamic data stream. Based on data\u27s hot degree, RUE classifies data into four types, each of which is assigned with an associated cache residence time. Guided by RUE model, we develop HM algorithm to identify and manage hot data in a dynamic data stream. HM algorithm is implemented by four stacks, namely, new stack, short stack, long stack, and temp stack. Moreover, an eviction and a migration algorithms are integrated into HM to facilitate block replacement and migration. To evaluate the performance of HM algorithm, we quantitatively compare the performance of RUE with three state-of-art algorithms, namely, LRU, LIRS, and ARC under various replacement policies, operations, and workloads. Experimental results show that RUE outperforms these three existing algorithms in terms of both read and write hit rates. Furthermore, we show that with the four stacks in place, the computing overhead of HM is negligible
Appreciative inquiry\u27s potential in program evaluation and research
No full textPurpose: The purposes of this paper are to provide a description of AI and to document and compare two applications of AI, one in program evaluation and another in an applied research study. Design/methodology/approach: Focus groups, interviews and observations were used to gather rich qualitative data which was used to detail Appreciative Inquiry\u27s value in evaluation and research. Findings: AI aided the researcher in connecting with the participants and valuing what they shared. In both studies, the use of AI amassed information that answered the research questions, provided a rich description of the context and findings, and led to data saturation. The authors describe and compare experiences with two applications of AI: program evaluation and a research study. This paper contributes further understanding of the use of AI in public education institutions. The researchers also explore the efficacy of using AI in qualitative research and recommend its use for multiple purposes. Research limitations/implications: Limitations occurred in the AI-Design Stage by using a positive viewpoint and because both program and partnership studied were new with limited data to use for designing a better future. So, the authors recommend a revisit of both studies through the same 4D Model. Practical implications: This manuscript shows that AI is useful for evaluation and research. It amplifies the participants\u27 voices through favorite stories and successes. AI has many undiscovered uses. Social implications: Through the use of AI the authors can: improve theoretical perspectives; conduct research that yields more authentic data; enable participants to deeply reflect on their practice and feel empowered; and ultimately impact and improve the world. Originality/value: AI is presented as an evaluation tool for a high-school program and as a research approach identifying strengths and perceptions of an educational partnership. In both studies, AI crumbled the walls that are often erected by interviewees when expecting to justify or defend decisions and actions. This paper contributes further understanding of the use of AI in public education institutions
GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning
No full textGlaucoma is a serious eye disease characterized by dysfunction and loss of retinal ganglion cells (RGCs) which can eventually lead to loss of vision. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods which, unfortunately, are expensive methods and hence, a novel automated glaucoma diagnosis system is needed. This paper proposes a new model for mass screening that aims to decrease the false negative rate (FNR). The model is based on applying nine different machine learning techniques in a majority voting model. The top five techniques that provide the highest accuracy will be used to build a consensus ensemble to make the final decision. The results from applying both models on a dataset with 499 records show a decrease in the accuracy rate from 90% to 83% and a decrease in false negative rate (FNR) from 8% to 0% for majority voting and consensus model, respectively. These results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening