If You Build it Will They Come? Teachers’ Online Use of Student Performance Data
Open accessAbstract
Testing of students and computer systems to store, manage, analyze, and report the resulting test data have grown hand-in-hand. Extant research on teacher use of electronically stored data are largely qualitative and focused on the conditions necessary (but not sufficient) for effective teacher data use. Absent from the research is objective information on how much and in what ways teachers use computer-based student test data, even when supposed precursors of usage are in place. This paper addresses this knowledge gap by analyzing the online activities of teachers in one mid-size urban district. Utilizing Web logs collected between 2008 and 2010, I find low teacher interaction with Web-based pages that contain student test information that could potentially inform practice. I also find no evidence that teacher usage of Web-based student data are related to student achievement gains, but there is reason to believe these estimates are downwardly biased.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Online web-based data systems are increasingly becoming popular as they allow access to updated data.
“Online web-based data systems are increasingly becoming popular as they allow access to updated data (Tyler, 2013).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler, 2013
The Introduction and Prior Research sections describe a proliferation of Web-based tools for accessing student performance data. Section 3 says the district’s system returns test data to teachers in a timely manner, but the paper does not establish that access to updated data is why these systems are becoming more popular. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTime allocated for teachers to review and discuss data in small groups and adopt procedures for discussing data is essential for data use.
“Time allocated for teachers to review and discuss data in small groups and adopt procedures for discussing data is essential for data use (Tyler, 2013).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler, 2013
The Summary and Conclusions section says time needs to be allocated for teachers to access and analyze student data regularly; Prior Research also identifies allotted time as a factor that promotes data use. Neither passage specifies reviewing and discussing data in small groups or adopting procedures for those discussions. Page numbers are not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAnother significant hindering factor revealed in this study is the perceived reliability and validity of data.
“Another significant hindering factor revealed in this study is the perceived reliability and validity of data (Tyler, 2013).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler, 2013
The Prior Research section says teachers’ belief that test data contain valid information, and their perception of its validity, affect data use. But it summarizes earlier research rather than reporting a barrier revealed by Tyler’s own study, and it does not identify perceived reliability as a separate factor. The Introduction describes Tyler’s study as examining whether and how long teachers access the data, not their perceptions of it; page numbers were not provided in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe availability of training for teachers increases their skills and confidence in using data systems to extract useful insights to inform learning and teaching activities.
“In addition, the availability of training for teachers increases their skills and confidence in using data systems to extract useful insights to inform learning and teaching activities (Tyler, 2013;van der Kleij & Eggen, 2013).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler, 2013
The Summary and Conclusions (page unavailable) says teachers need training and support to use data effectively and translate analysis into instructional practice. But it does not establish that training increases teachers’ skills or confidence. In Results and Discussion, additional training may have increased printing of data, but did not increase interactive use of the Dashboard. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIf teachers' attitude towards data is negative, they will be less inclined to use data to inform practice.
“If teachers' attitude towards data is negative, they will be less inclined to use data to inform practice (Tyler, 2013;van der Kleij & Eggen, 2013).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler, 2013
In Prior Research (page unavailable), Tyler reports that teachers’ belief in the validity of test data affects data use, and describes teachers who were unconvinced that a data-analysis system was useful. This supports a link between particular negative perceptions and data use, but not the broader, categorical claim that any negative attitude toward data means teachers will be less inclined to use it to inform practice. (LLM, conf=0.82)
Experimental — accuracy not yet measured
The probability of teachers' data use proliferating in a school is increased when the 'culture of data use' is developed.
“Furthermore, Tyler (2013) argues that the probability of teachers' data use proliferating in a school is increased when the “culture of data use” is developed.”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler, 2013
The probability of teachers' data use proliferating in a school is increased when the 'culture of data use' is developed.
“Furthermore, Tyler (2013) argues that the probability of teachers' data use proliferating in a school is increased when the “culture of data use” is developed.”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tyler (2013)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Policy Implementation and Cognition: Reframing and Refocusing Implementation Research
Spillane, J. P., Reiser, B. J., & Reimer, T. (2002) · Review of Educational Research · doi:10.3102/00346543072003387 · Cited by 1,923 · see citing works
Learning From Experience: Lessons From Policy Implementation
McLaughlin, M. W. (1987) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737009002171 · Cited by 1,040 · see citing works
A Revolution in One Classroom: The Case of Mrs. Oublier
Cohen, D. K. (1990) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737012003311 · Cited by 660 · see citing works
Accountability Policies and Teacher Decision Making: Barriers to the Use of Data to Improve Practice
Ingram, D., Louis, K. S., & Schroeder, R. G. (2004) · Teachers College Record: The Voice of Scholarship in Education · doi:10.1111/j.1467-9620.2004.00379.x · Cited by 373 · see citing works
Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006) · American Journal of Education · doi:10.1086/505057 · Cited by 341 · see citing works
Teachers’ Use of Data: Loose Coupling, Agenda Setting, and Team Norms
Young, V. M. (2006) · American Journal of Education · doi:10.1086/505058 · Cited by 281 · see citing works
Wayman, J. C. (2005) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1207/s15327671espr1003_5 · Cited by 250 · see citing works
Carlson, D., Borman, G. D., & Robinson, M. (2011) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373711412765 · Cited by 249 · see citing works
Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research
(2006) · RAND Corporation eBooks · doi:10.7249/op170 · Cited by 202 · see citing works
(2009) · US Department of Education · Cited by 192 · see citing works
(2010) · US Department of Education · Cited by 176 · see citing works
(2008) · Cited by 167 · see citing works
Linking Data and Learning: The Grow Network Study
Brunner, C., Fasca, C., Heinze, J., Honey, M., Light, D., Mardinach, E., & Wexler, D. (2005) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1207/s15327671espr1003_2 · Cited by 123 · see citing works
The implementation gap : understanding reform in high schools
Supovitz, J. A., & Weinbaum, E. H. (2008) · Cited by 101 · see citing works
Identifying and Monitoring Students' Learning Needs With Technology
Chen, E., Heritage, M., & Lee, J. (2005) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1207/s15327671espr1003_6 · Cited by 93 · see citing works
SOFTWARE ENABLING SCHOOL IMPROVEMENT THROUGH ANALYSIS OF STUDENT DATA
Wayman, J. C., Stringfield, S., & Yakimowski, M. E. (2004) · Cited by 93 · see citing works
From Testing to Teaching: The Use of Interim Assessments in Classroom Instruction
Goertz, M. E., Oláh, L. N., & Riggan, M. (2009) · doi:10.12698/cpre.2009.rr65 · Cited by 69 · see citing works
Using Data-Based Inquiry and Decision Making To Improve Instruction.
Feldman, J., & Tung, R. (2001) · Cited by 51 · see citing works
Teachers' Use of Student Data Systems to Improve Instruction.
Means, B., Gallagher, L. P., & Padilla, C. (2007) · US Department of Education · Cited by 49 · see citing works
Protheroe, N. (2001) · Cited by 37 · see citing works
Second-Year Results From an Efficacy Study of the Acuity Data System
Wayman, J. C. (2011) · Cited by 28 · see citing works
Goertz, M. E., Oláh, L. N., & Riggan, M. (2009) · Cited by 20 · see citing works
Pardini, P. (2000) · The Journal of staff development · Cited by 10 · see citing works
Technology in America's Schools: An Overview of Status and Issues.
D'Amico, J. J. (2001) · Cited by 6 · see citing works
Wayman, J. C. (2005) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1207/s15327671espr1003_1 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 26 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Payne Charles M 2008 So much reform, so little change Cambridge, MA Harvard Education Press
Cited by
Sorted by citations
Goldring, E. B., Grissom, J. A., Rubin, M., Neumerski, C. M., Cannata, M. A., Drake, T. A., & Schuermann, P. J. (2015) · Educational Researcher · doi:10.3102/0013189x15575031 · Cited by 178 · see citing works
Nudging Technology Use: Descriptive and Experimental Evidence from School Information Systems
Bergman, P. (2019) · Education Finance and Policy · doi:10.1162/edfp_a_00291 · Cited by 44 · see citing works
Longitudinal Effects of Teacher Use of a Computer Data System on Student Achievement
Wayman, J. C., Shaw, S., & Cho, V. (2017) · AERA Open · doi:10.1177/2332858416685534 · Cited by 30 · see citing works
Exploring the factors influencing teachers’ instructional data use with electronic data systems
Luo, J., Wang, M., & Yu, S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104631 · Cited by 32 · see citing works
Does Teacher Data Use Lead to Improved Student Achievement? A Review of the Empirical Evidence
Grabarek, J., & Kallemeyn, L. M. (2020) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/016146812012201201 · Cited by 17 · see citing works
Alonzo, D., Quimno, V., Townend, G., & Oo, C. Z. (2024) · Educational Assessment, Evaluation and Accountability · doi:10.1007/s11092-024-09443-8 · Cited by 16 · see citing works
“Time allocated for teachers to review and discuss data in small groups and adopt procedures for discussing data is essential for data use (Tyler, 2013).”
InfluentialTools of the trade: a look at educators’ use of assessment systems
Farley-Ripple, E. N., Jennings, A., & Jennings, A. B. (2021) · School Effectiveness and School Improvement · doi:10.1080/09243453.2020.1777171 · Cited by 14 · see citing works
Explaining variation in teachers’ use of ICT: a learning analytics approach
Lomos, C., Luyten, J. W., Kesting, F., & da Cunha, F. L. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2170419 · Cited by 9 · see citing works
Technology Adoption in Education: Usage, Spillovers and Student AchievementPreprint
Bergman, P. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2866866 · Cited by 7 · see citing works
Data Leadership for K-12 Schools in a Time of Accountability
Schildkamp, K., Poortman, C. L., Mense, E. G., & Crain-Dorough, M. (2017) · Advances in early childhood and K-12 education · doi:10.4018/978-1-5225-3188-3 · Cited by 8 · see citing works
Leadership to Support and Sustain Data Use in Data Teams
Schildkamp, K., & Poortman, C. L. (2017) · Advances in early childhood and K-12 education · doi:10.4018/978-1-5225-3188-3.ch006 · Cited by 6 · see citing works
Drake, T. A. (2021) · NASSP Bulletin · doi:10.1177/01926365211015311 · Cited by 5 · see citing works
Technology Adoption in Education: Usage, Spillovers and Student Achievement
Bergman, P. (2016) · AEA Randomized Controlled Trials · doi:10.1257/rct.1220-1.0 · Cited by 5 · see citing works
Is This Technology Useless? How Seemingly Irrelevant Factors Affect Adoption and EfficacyPreprint
Bergman, P. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2989042 · Cited by 5 · see citing works
Technology Adoption in Education: Usage, Spillovers and Student Achievement
Bergman, P. (2016) · AEA Randomized Controlled Trials · doi:10.1257/rct.1220 · Cited by 5 · see citing works
Digital learning solutions for improving adults’ basic skills
Murphy, R., Wang, S., Bienkowski, M. A., & Bhanot, R. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2253860 · Cited by 1 · see citing works
Novak, D. A., Hallowell, R., & Elliott, D. (2019) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/978-1-7998-1468-9.ch022
Farley-Ripple, E. N. (2019) · American Educational Research Association Annual Meeting (AERA) · doi:10.3102/1437769
Collaborative data dashboard design for school improvement
Burnham, A., & Bowers, A. J. (2026) · Information and Learning Sciences · doi:10.1108/ils-12-2025-0245
- Authors
- Tyler, J. H.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Tyler, John H.
- ORCID
- Tyler, John H.
- Venue
- Education Finance and Policy
- Publisher
- MIT Press - Journals
- Published
- 2013-04
- Volume
- 8
- Issue
- 2
- Pages
- 168-207
- ISSN
- 1557-3060 (ISSN-L)
- 1557-3079
- PMC
- PMC4292877
- OpenAlex
- W2060914432
- Cited by
- 19
- References
- 26
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 4 · 2017: 4 · 2016: 1 · 2015: 1
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Education Finance and Policy (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24HD041020)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Social Sciences › Education › School Choice and Performance
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- teacher data use, online data access, blogs, academic performance, urban school districts
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