Disasters may have significant and lasting impacts on educational programs and academic achievement, yet the examination of differing patterns of school recovery after disasters is understudied. This paper focused on two aims: (i) identification of school academic recovery trajectories; and (ii) examination of potential risk factors associated with these trajectories. We used latent class growth analysis to identify school academic recovery trajectories for a cohort of 462 Texas public schools that were in the path of Hurricane Ike in 2008. Using Texas Assessment of Knowledge and Skills (TAKS) data from 2005 to 2011, we found that attendance and percent of economically disadvantaged youth emerged as significant risk factors for two identified academic recovery trajectories (High-Stable and Low-Interrupted). Higher levels of economically disadvantaged youth were associated with lower likelihood of falling in the High-Stable trajectory, relative to the Low-Interrupted trajectory. Higher levels of attendance were associated with higher likelihood of membership in the High-Stable trajectory, relative to the Low-Interrupted trajectory. These findings are consistent with the notion that disasters do not
affect all people or communities equally. Findings highlight the need for policy initiatives that focus on low performing schools, as these schools are at highest risk for adverse outcomes post-disaster.
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Support Recovery for Sparse Recovery and Non-stationary Blind Demodulation
- Award ID(s):
- 1704204
- NSF-PAR ID:
- 10174996
- Date Published:
- Journal Name:
- 2019 53rd IEEE Asilomar Conference on Signals, Systems, and Computers
- Page Range / eLocation ID:
- 235 to 239
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEEECONF44664.2019.9048791
