The study explores predictive associations between student perceptions of a sense of belonging, teacher perceptions of teaching efficacy, family perceptions of school fit, and academic growth within a post-COVID-19 shutdown landscape. Learning loss due to the COVID-19 pandemic instructional disruption can have long-lasting impacts, necessitating instructional interventions that aim to close learning gaps and increase learning gains (UNESCO, 2020). Drawing from 5 global datasets, the World Bank simulation conducted in 2021 projected a COVID-19 school closure learning loss range of .3 to 1.1 years, the range attributed to 4 possible scenarios with converse intervals of closure and effectiveness of mitigation measures (Azevedo, 2021), marking a minimum 20% increase in average learning loss of 1 to 4 months attributed to breaks in instruction, such as summer break (Kuhfeld, 2019). The study assesses statistical predictive associations through ordinal logistic regression utilizing climate survey data and dichotomously coded learning gains. Such predictive associations will empower school leaders with an expanded arsenal of creative solutions to novel challenges presented by both the COVID-19 pandemic and the looming funding cliff created by pandemic relief efforts. Stratified random sampling was employed to select participants from 138 schools within Orange County Public Schools (OCPS) grades 3-5 that met the eligibility for learning gains defined by Florida Department of Education (FDOE) School Grade accountability criteria and are enrolled in schools meeting the Annual Stakeholder participation thresholds. Findings indicate a statistically significant relationship between student math learning gains and student sense of belonging ratings, providing administrators with a method of monitoring student performance trajectories beyond traditional assessment-based monitoring systems.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2813 |
| Date | 15 August 2023 |
| Creators | Izzo, Deborah |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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