Technology's Edge: The Educational Benefits of Computer-Aided Instruction
Abstract
We present results from a randomized study of a well-defined use of computers in schools, a popular instructional computer program for pre-algebra and algebra. We primarily assess the program using a test designed to target pre-algebra and algebra skills. Students randomly assigned to computer-aided instruction score significantly higher on a pre-algebra and algebra test than students randomly assigned to traditional instruction. We hypothesize that this effectiveness arises from increased individualized instruction as the effects appear larger for students in larger classes and in classes with high student absentee rates. (JEL H75, I21)
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?
Barrow et al. (2009) find that students randomly assigned to computer-aided instruction using an algebra program largely improve on algebra test scores compared to students receiving traditional instruction.
“For instance, Barrow et al. (2009) fi nd that students randomly assigned to computer-aided instruction using an algebra program largely improve on algebra test scores compared to the students receiving traditional instruction.”
From Banning mobile phones in schools: evidence from regional-level policies in Spain (2022) · cites this paper as Barrow et al. (2009)
Cites
Remedying Education: Evidence from Two Randomized Experiments in India
Banerjee, A. V., Cole, S., Duflo, E., & Linden, L. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.122.3.1235 · Cited by 1,398 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Disrupting education? Experimental evidence on technology-aided instruction in India
Muralidharan, K., Singh, A., & Ganimian, A. J. (2019) · American Economic Review · doi:10.1257/aer.20171112 · Cited by 415 · see citing works
Lavy, V. (2015) · The Economic Journal · doi:10.1111/ecoj.12233 · Cited by 369 · 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
Ill Communication: Technology, distraction & student performance
Beland, L.-P., & Murphy, R. (2016) · Labour Economics · doi:10.1016/j.labeco.2016.04.004 · Cited by 236 · see citing works
Fernández-Gutiérrez, M., Gimenez, G., & Calero, J. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103969 · Cited by 225 · see citing works
Children, wired: for better and for worse
Bavelier, D., Green, C. S., & Dye, M. W. G. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.035 · Cited by 193 · see citing works
Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review
Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M. (2022) · IEEE Access · doi:10.1109/access.2022.3179356 · Cited by 185 · see citing works
What Can Be Done To Improve Struggling High Schools?
Cullen, J. B., Levitt, S. D., Robertson, E., & Sadoff, S. (2013) · Journal of Economic Perspectives · doi:10.1257/jep.27.2.133 · Cited by 61 · see citing works
The impact of banning mobile phones in Swedish secondary schools
Kessel, D., Hardardottir, H. L., & Tyrefors, B. (2020) · Economics of Education Review · doi:10.1016/j.econedurev.2020.102009 · Cited by 60 · see citing works
Banning mobile phones in schools: evidence from regional-level policies in Spain
Beneito, P., & Vicente-Chirivella, Ó. (2022) · Applied Economic Analysis · doi:10.1108/aea-05-2021-0112 · Cited by 49 · see citing works
- Authors
- Barrow, L., Markman, L., & Rouse, C. E.
- Affiliations
- Federal Reserve Bank of Chicago (US)
- Princeton University (US)
- ORCID
- Barrow, Lisa
- Venue
- American Economic Journal: Economic Policy
- Publisher
- American Economic Association
- Published
- 2009-02-01
- Volume
- 1
- Issue
- 1
- Pages
- 52-74
- ISSN
- 1945-7731 (ISSN-L)
- 1945-774X
- OpenAlex
- W3124369876
- Cited by
- 200
- References
- 1
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 10 · 2023: 13 · 2022: 18 · 2021: 10 · 2020: 20 · 2019: 12 · 2018: 15 · 2017: 15 · 2016: 16 · 2015: 9 · 2014: 9 · 2013: 12 · 2012: 16
- FWCI
- 46.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Economic Journal Economic Policy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Education › School Choice and Performance
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Education and Technology Integration
- Keywords
- computer-assisted instruction, educational technology, algebra instruction, pre-algebra, personalized learning, class size, school absenteeism, academic performance, conventional instruction comparison
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