One-to-One Technology and Student Outcomes: Evidence From Mooresville’s Digital Conversion Initiative
Abstract
This article uses a difference-in-differences strategy to evaluate the impact of a one-to-one laptop program. Teaching practices changed with the introduction of the program, and the district worked to make wireless Internet more accessible in the community. We find that while short-term impacts of the program were statistically insignificant, math scores improved by 0.13 standard deviations in the medium term. Time spent on homework stayed constant, but students spent more of their homework time using a computer. We also investigate the impact of the program on other measures of student behavior as well as heterogeneity in impacts. A limitation of this study is that we cannot distinguish which aspects of the program were most important in improving student outcomes.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the same study of 1-to-1 laptops, the program was associated with improved math scores by 0.13 standard deviations in the medium term.
“However, the program was associated with improved math scores by 0.13 standard deviations in the medium term.97”
From Digital Ecosystems, Children, and Adolescents: Technical Report (2026) · cites this paper as [97]
In a study of 1-to-1 laptops for fourth through eighth graders, short-term impacts on learning were statistically insignificant.
“One study implementing 1-to-1 laptops for fourth through eighth graders found that short-term impacts of the program were statistically insignificant in promoting learning.97”
From Digital Ecosystems, Children, and Adolescents: Technical Report (2026) · cites this paper as [97]
Cites
Handbook of The Economics of Education
(2011) · Handbook of the economics of education · doi:10.1016/c2009-0-19772-6 · Cited by 2,590 · see citing works
American Economic Journal: Applied Economics
(2017) · American Economic Journal Applied Economics · doi:10.1257/app · Cited by 1,653 · see citing works
Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates
Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014) · American Economic Review · doi:10.1257/aer.104.9.2593 · Cited by 1,241 · see citing works
Implementation and Effects Of One-to-One Computing Initiatives
Penuel, W. R. (2006) · Journal of Research on Technology in Education · doi:10.1080/15391523.2006.10782463 · Cited by 458 · see citing works
Learning in One-to-One Laptop Environments: A Meta-Analysis and Research Synthesis
Zheng, B., Warschauer, M., Lin, C.-H., & Chang, C. (2016) · Review of Educational Research · doi:10.3102/0034654316628645 · Cited by 312 · see citing works
Home Computer Use and the Development of Human Capital *
Malamud, O., & Pop-Eleches, C. (2011) · The Quarterly Journal of Economics · doi:10.1093/qje/qjr008 · Cited by 284 · see citing works
Bulman, G., & Fairlie, R. W. (2016) · Handbook of the Economics of Education · doi:10.1016/b978-0-444-63459-7.00005-1 · Cited by 284 · see citing works
Assessing the effect of school days and absences on test score performance
Aucejo, E. M., & Romano, T. F. (2016) · Economics of Education Review · doi:10.1016/j.econedurev.2016.08.007 · Cited by 222 · see citing works
HOME COMPUTERS AND EDUCATIONAL OUTCOMES: EVIDENCE FROM THE NLSY97 and CPS*
Fairlie, R. W., Beltran, D. O., & DAS, K. K. (2010) · Economic Inquiry · doi:10.1111/j.1465-7295.2009.00218.x · Cited by 74 · see citing works
Fairlie, R. W. (2015) · The B E Journal of Economic Analysis & Policy · doi:10.1515/bejeap-2015-0094 · Cited by 25 · see citing works
Innovation and American K–12 Education
Chatterji, A. (2017) · Innovation Policy and the Economy · doi:10.1086/694406 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Edwards M., 2012, Learning & Leading With Technology, 40, 12
- Edwards M. A., 2013, Every child, every day: A digital conversion model for student achievement
Cited by
Sorted by citations
Ross, S. M. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09756-7 · Cited by 51 · see citing works
The times they are a-changin’:Teaching and learning beyond COVID-19
Marshall, D. T., Pressley, T., & Love, S. M. (2022) · Journal of Educational Change · doi:10.1007/s10833-022-09469-z · Cited by 28 · see citing works
Isolating the “Tech” from EdTech: Experimental Evidence on Computer Assisted Learning in China
Ma, Y., Fairlie, R. W., Loyalka, P. K., & Rozelle, S. (2020) · National Bureau of Economic Research · doi:10.3386/w26953 · Cited by 29 · see citing works
Diminishing Marginal Returns to Computer‐Assisted Learning
Bettinger, E., Fairlie, R. W., Kapuza, A., Kardanova, E. Y., Loyalka, P. K., & Zakharov, A. (2022) · Journal of Policy Analysis and Management · doi:10.1002/pam.22442 · Cited by 21 · see citing works
Diminishing Marginal Returns to Computer-Assisted Learning
Bettinger, E., Fairlie, R. W., Kapuza, A., Kardanova, E. Y., Loyalka, P. K., & Zakharov, A. (2020) · National Bureau of Economic Research · doi:10.3386/w26967 · Cited by 19 · see citing works
Technology in the classroom: Personal computers and learning outcomes in primary school
Häll, C., & Lundin, M. (2024) · Economics of Education Review · doi:10.1016/j.econedurev.2024.102536 · Cited by 24 · see citing works
Ross, S. M., & Morrison, J. (2020) · ECNU Review of Education · doi:10.1177/2096531120916742 · Cited by 22 · see citing works
Potter, A., & Wilson, J. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-021-10004-9 · Cited by 15 · see citing works
Computers in education: the association between computer use and test scores in primary school
Karlsson, L. (2020) · Education Inquiry · doi:10.1080/20004508.2020.1831288 · Cited by 16 · see citing works
A laptop for every child? The impact of technology on human capital formation
Hall, C., Lundin, M., & Sibbmark, K. (2021) · Labour Economics · doi:10.1016/j.labeco.2020.101957 · Cited by 21 · see citing works
Digital Ecosystems, Children, and Adolescents: Technical Report
Munzer, T., Milkovich, L. M., Madigan, S., Tomopoulos, S., Parga-Belinkie, J., Ajumobi, T., Cross, C., Gerwin, R., Council on Communications and Media, Radesky, J., et al. (2026) · Pediatrics · doi:10.1542/peds.2025-075321 · Cited by 14 · see citing works
South Africa’s (Unequal) Digital Learning Journey: A Critical Review
Isaacs, S. (2020) · Lecture notes in educational technology · doi:10.1007/978-981-15-6157-3_11 · Cited by 12 · see citing works
Lakdawala, L. K., Nakasone, E., & Kho, K. (2023) · American Economic Journal Economic Policy · doi:10.1257/pol.20200719 · Cited by 8 · see citing works
Weinhandl, R., Helm, C., Anđić, B., & Große, C. S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13740-0 · Cited by 11 · see citing works
Isolating the “Tech” from EdTech: Experimental Evidence on Computer-Assisted Learning in China
Ma, Y., Fairlie, R. W., Loyalka, P. K., & Rozelle, S. (2023) · Economic Development and Cultural Change · doi:10.1086/726064 · Cited by 6 · see citing works
Irwin, D. A., Arslan‐Ari, I., & Morris, W. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11450-5 · Cited by 8 · see citing works
Weinhandl, R., Helm, C., Anđić, B., & Große, C. S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35381 · Cited by 5 · see citing works
Examining State and District Policy and Resource Allocation to Support Digital Learning
Bowden, A. B., Danks, A., Rodriguez, V., Yang, H., & Davis, R. A. (2023) · Peabody Journal of Education · doi:10.1080/0161956x.2023.2160105 · Cited by 1 · see citing works
Zahedi, S., Kimmons, R. M., & Venkat, P. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13372-4 · Cited by 1 · see citing works
Weinhandl, R., Anđić, B., Baldinger, S., Mayrhofer, J., & Helm, C. (2025) · Computers in the Schools · doi:10.1080/07380569.2025.2544051 · Cited by 1 · see citing works
Let's Tweet Again? Social Networks and Literature Achievement in High School Students
Barbetta, G., Canino, P., & Cima, S. (2021) · Education Finance and Policy · doi:10.1162/edfp_a_00363 · Cited by 4 · see citing works
The Effects of 1:1 Technology on African American Students' Achievement in Algebra and English
Wolf, C. B. (2020) · Digital Commons - ACU (Abilene Christian University)
How assessing district response to COVID-19 can lead to positive change
Levenbrook, H. (2022) · doi:10.17760/d20455969
Achievement Differences Between GendersPreprint
Thomas, F. (2023) · doi:10.31124/advance.22811588.v1
Achievement Differences Between Genders.docxPreprint
Thomas, F. (2023) · doi:10.31124/advance.22811588
Zhang, C., & Shi, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9748439/v1
Lamb, A. J. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/979-8-88730-873-920251005
- Authors
- Hull, M., & Duch, K.
- Affiliations
- University of North Carolina at Greensboro (US)
- One Minus Beta Analytics
- ORCID
- Hull, Marie
- Venue
- Educational Evaluation and Policy Analysis
- Published
- 2018-09-21
- Volume
- 41
- Issue
- 1
- Pages
- 79-97
- ISSN
- 0162-3737 (ISSN-L)
- 1935-1062
- OpenAlex
- W2891662579
- Semantic Scholar
- 31f498a2d49d4ac6809d797c2f92d95283f6031d
- Cited by
- 28
- References
- 10
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 6 · 2022: 4 · 2021: 3 · 2020: 8
- FWCI
- 8.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Educational Evaluation and Policy Analysis (publishedVersion)
- Funders
- Sanford School of Public Policy, Duke University
- Topics
- Social Sciences › Education › Education and Technology Integration
- Social Sciences › Education › School Choice and Performance
- Social Sciences › Education › Educational Environments and Student Outcomes
- Keywords
- one-to-one laptop programs, academic performance, mathematics scores, homework time, student behavior, educational technology, wireless internet, treatment effect heterogeneity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review