Digital divide scholars suggest that the speed and scope of the digital precipitates unique catalysts of societal inequity, which public schools have long sought to mitigate by democratizing access to education. This study investigates a one-to-one digital device program in one of the largest public school districts in the United States, and its impact on literacy achievement in varying socioeconomic climates and the attitudes and beliefs of marginalized parent populations. Previous studies on one-to-one programs are largely qualitative, and existing quantitative studies suffer too many variables for reliable conclusions. Through a mixed methods design, this study centers on a highly-standardized implementation across 200,000 students, controlling for variables plaguing existing work, and offering a breadth of comparable data previously unavailable. The quantitative phase analyzed standardized test scores over seven years surrounding the implementation, and the qualitative phase analyzed survey data gathered from parents in varying socioeconomic climates. These analyses found no statistically significant change in the literacy achievement gap between low and high-income communities, and no concerns unique to any particular parent demographic, negating concerns of some scholars that one-to-one programs might exacerbate the digital divide. This study also found that parents—regardless of language, income, or educational background—generally believe this program eased the transition to remote learning when schools closed due to Covid-19 in 2020, and will better prepare students for a digitized workplace. Recommendations are made for existing and future digital learning and one-to-one laptop programs, and suggestions are offered for future research in or tangential to the fields of digital learning and digital inequity.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2009 |
| Date | 01 January 2022 |
| Creators | Gindlesperger, Theresa |
| 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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