CoChat
Sign Up
Cited by 83Online Learning and AnalyticsBig Data and Business IntelligenceEducational and Technological Research

Data Analytics and Decision- Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions.

Open access
Agasisti, Tommaso, Bowers, Alex J.·2017·Handbook of Contemporary Education Economics
DOI 10.4337/9781785369070.00014
Open this paper in CoChat — free accountOpen PDF

Abstract

In this chapter, we outline the importance of data usage for improving policymaking (at the system level), management of educational institutions and pedagogical approaches in the classroom. We illustrate how traditional data analyses are becoming gradually substituted by more sophisticated forms of analytics, and we provide a classification for these recent movements (in particular learning analytics, academic analytics and educational data mining). After having illustrated some examples of recent applications, we warn against potential risks of inadequate analytics in education, and list a number of barriers that impede the widespread application of better data use. As implications, we call for a development of a more robust professional role of data scientists applied to education, with the aim of sustaining and reinforcing a positive data-driven approach to decision making in the educational field.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.4337/9781785369070.00014

Cites

8 of 8 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Administrators can tailor educational strategies to meet students' needs, promote academic achievement, and utilize interactive tools for enhanced analysis.

    “Research indicates that administrators can tailor educational strategies to meet students' needs, promote academic achievement, and utilize interactive tools for enhanced analysis (Gil et al., 2020;Agasisti et al., 2017).”

    From Data-Driven Decision-Making through Real-time Student Progress Monitoring: Academic Administrators Perspectives (2025) · cites this paper as Agasisti et al., 2017

    Section 3.2 says principals and other decision-makers can use data for evidence-based management oriented toward student success, while Section 3.1 describes data-informed interventions and tools for analysing student progress (page numbers unavailable). The copy does not clearly say that administrators themselves use interactive tools to conduct enhanced analysis; its dashboard example says principals developed a tool used by teachers. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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

Flagged first, then by citations

Research works printed but not linked: 107

  1. Agasisti, T., Bowers, A.J. (2017) Data Analytics and Decision-Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions. In Johnes, G., Johnes, J., Agasisti, T., López-Torres, L. (Eds.) Handbook of Contemporary Education Economics (p.184-210). Cheltenham, UK: Edward Elgar Publishing. ISBN: 978-1-78536-906-3 http://www.e-elgar.com/shop/handbook-of-contemporary- education-economics REFERENCES:
  2. Agasisti, T. (2013). 'The efficiency of Italian secondary schools and the potential role of competition: a data envelopment analysis using OECD-PISA2006 data'. Education Economics, 21(5), 520-544.
  3. Andres, J. M. L., Baker, R. S., Siemens, G., Gašević, D., & Spann, C. A. (in press). Replicating 21 Findings on Student Success in Online Learning. Technology, Instruction, Cognition, and Learning.
  4. Arnold, K. E. (2010). 'Signals: Applying Academic Analytics'. EDUCAUSE Quarterly, 33(1), n1.
  5. Arnold, K. E., & Pistilli, M. D. (2012). 'Course signals at Purdue: using learning analytics to increase student success'. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267- 270). ACM.
  6. Arnold, K. E., Lynch, G., Huston, D., Wong, L., Jorn, L., & Olsen, C. W. (2014). 'Building institutional capacities and competencies for systemic learning analytics initiatives'. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 257-260). ACM.
  7. Baepler, P., & Murdoch, C. J. (2010). 'Academic analytics and data mining in higher education'. International Journal for the Scholarship of Teaching and Learning, 4(2), 17.
  8. Baker, R. S. (2013). 'Learning, Schooling, and Data Analytics'. In M. Murphy, S. Redding & J. Twyman (Eds.), Handbook on innovations in learning Philadelphia, PA: Center on Innovations in Learning, Temple University; Charlotte, NC: Information Age Publishing.
  9. Baker, R. S., & Inventado, P. S. (2014). 'Educational data mining and learning analytics'. In J. A. Larusson & B. White (Eds.), Learning Analytics (pp. 61-75). New York: Springer.
  10. Baker, R. S., & Yacef, K. (2009). 'The State of Educational Data Mining in 2009: A Review and Future Visions'. Journal of Educational Data Mining, 1(1), 3-16.
  11. Behrens, J. T., & DiCerbo, K. E. (2014). Technological Implications for Assessment Ecosystems: Opportunities for Digital Technology to Advance Assessment. Teachers College Record, 116(11), 1-22.
  12. Bernhardt, V. (2013). Data analysis for continuous school improvement (3 ed.). New York: Routledge.
  13. Bernhardt, V. L. (2004). Data Analysis for Continuous School Improvement (2 ed.). Eye On Education.
  14. Bieber, T., & Martens, K. (2011). 'The OECD PISA study as a soft power in education? Lessons from Switzerland and the US'. European Journal of Education, 46(1), 101-116.
  15. Bishop, J. H. (1997). 'The effect of national standards and curriculum-based exams on achievement'. The American Economic Review, 87(2), 260-264.
  16. Borer, V. L., & Lawn, M. (2013). 'Governing Education Systems by Shaping Data: from the past to the present, from national to international perspectives'. European Educational Research Journal, 12(1), 48-52.
  17. Boudett, K. P., City, E. A., & Murnane, R. J. (2013). Data Wise: Revised and Expanded Edition: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning. Revised and Expanded Edition. Cambridge, MA: Harvard Education Press.
  18. Bouwma-Gearhart, J., & Collins, J. (2015, October). 'What We Know About Data-Driven Decision Making In Higher Education: Informing Educational Policy and Practice'. In Proceedings of International Academic Conferences (No. 2805154). International Institute of Social and Economic Sciences.
  19. Bowers, A. J. (2008). 'Promoting Excellence: Good to great, NYC's district 2, and the case of a high performing school district'. Leadership and Policy in Schools, 7(2), 154-177.
  20. Bowers, A. J. (2009). 'Reconsidering grades as data for decision making: More than just academic knowledge'. Journal of Educational Administration, 47(5), 609-629.
  21. Bowers, A. J. (2017). 'Quantitative Research Methods Training in Education Leadership and Administration Preparation Programs as Disciplined Inquiry for Building School Improvement Capacity. Journal of Research on Leadership Education, 12(1), p.72-96. http://doi.org/10.1177/1942775116659462. · DOI
  22. Bowers, A. J., Shoho, A. R., & Barnett, B. G. (2014). 'Considering the Use of Data by School Leaders for Decision Making'. In A. J. Bowers, A. R. Shoho & B. G. Barnett (Eds.), Using Data in Schools to Inform Leadership and Decision Making (pp. 1-16).
  23. Bowers, A. J., Sprott, R., & Taff, S. (2013). 'Do we know who will drop out? A review of the predictors of dropping out of high school: Precision, sensitivity and specificity'. The High School Journal, 96(2), 77-100.
  24. Braun, M. L., & Ong, C. S. (2014). 'Open Science in Machine Learning'. In V. Stodde, F. Leisch & R. D. Peng (Eds.), Implementing Reproducible Research (pp. 343- 365): Chapman and Hall/CRC.
  25. Brunello, G., & Checchi, D. (2007). 'Does school tracking affect equality of opportunity? New international evidence'. Economic Policy, 22(52), 782-861.
  26. Campbell, J. P., & Oblinger, D. G. (2007). 'Academic analytics'. EDUCAUSE Review, 42(4), 40-57.
  27. Cho, V., & Wayman, J. C. (2015). 'Districts' Efforts for Data Use and Computer Data Systems: The Role of Sensemaking in System Use and Implementation'. Teachers College Record, 116(2), 1-45.
  28. Coburn, C. E., & Turner, E. O. (2011). 'Research on Data Use: A Framework and Analysis'. Measurement: Interdisciplinary Research and Perspectives, 9(4), 173- 206.
  29. Cope, B., & Kalantzis, M. (2016). 'Big Data Comes to School'. AERA Open, 2(2), 1-19.
  30. Cosner, S. (2014). 'Strengthening Collaborative Practices in Schools: The Need to Cultivate Development Perspectives and Diagnostic Approaches'. In A. J. Bowers, A. R. Shoho & B. G. Barnett (Eds.), Using Data in Schools to Inform Leadership and Decision Making. Charlotte, NC: Information Age Publishing.
  31. Chung, G. K. W. K. (2014). Toward the Relational Management of Educational Measurement Data. Teachers College Record, 116(11), 1-16.
  32. Daniel, B. (2015). 'Big Data and analytics in higher education: Opportunities and challenges'. British Journal of Educational Technology, 46(5), 904-920.
  33. Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). 'Current state and future trends: a citation network analysis of the learning analytics field'. Paper presented at the Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, Indianapolis, Indiana, USA.
  34. Davies, P. (1999). 'What is evidence-based education?', British Journal of Educational Studies, 47(2), 108-121.
  35. De Witte, K., Cabus, S., Thyssen, G., Groot, W., & van den Brink, H. M. (2013). 'A critical review of the literature on school dropout'. Educational Research Review, 10, 13- 28.
  36. Demski, D., & Racherbäumer, K. (2015). 'Principals' Evidence-Based Practice--Findings from German Schools'. International Journal of Educational Management, 29(6), 735-748.
  37. Denley, T. (2012). 'Austin Peay State University: Degree Compass'. In D.G. Oblinger (ed.), Gamer changers: education and information technologies (pp. 263-267), EDUCAUSE.
  38. Earl, L. M., & Katz, S. (Eds.). (2006). Leading schools in a data-rich world: Harnessing data for school improvement. Corwin Press. European Commission. (2016). Communication from the Commission to the Council and to the EU Parliament, 8/9/2006, on efficiency and equity in European education and training systems, COM(2006)481 final.
  39. Farley-Ripple, E. N., & Buttram, J. L. (2014). 'Schools' Use of Interim Data: Practices in Classrooms, Teams, and Schools'. In A. J. Bowers, A. R. Shoho & B. G. Barnett (Eds.), Using Data in Schools to Inform Leadership and Decision Making (pp. 39-66).
  40. Farley-Ripple, E. N., & Buttram, J. L. (2015). 'The Development of Capacity for Data Use: The Role of Teacher Networks in an Elementary School'. Teachers College Record, 117(4), 1-34.
  41. Fecher, B., & Friesike, S. (2014). 'Open Science: One Term, Five Schools of Thought'. In S. Bartling & S. Friesike (Eds.), Opening Science: The Evolving Guide on How the Internet is Changing Research, Collaboration and Scholarly Publishing (pp. 17-47). Cham: Springer International Publishing.
  42. Feng, M., Krumm, A. E., Bowers, A. J., & Podkul, T. (2016). 'Elaborating data intensive research methods through researcher-practitioner partnerships'. Paper presented at the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, United Kingdom.
  43. Ferguson, R. (2012). 'Learning analytics: drivers, developments and challenges'. International Journal of Technology Enhanced Learning, 4(5-6), 304-317.
  44. Fritz, J. (2010). 'Video Demo of UMBC's" Check My Activity" Tool for Students'. Educause Quarterly, 33(4).
  45. Fritz, J. (2011). 'Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers'. The Internet and Higher Education, 14(2), 89-97.
  46. Gandomi, A., & Haider, M. (2015). 'Beyond the hype: Big data concepts, methods, and analytics'. International Journal of Information Management, 35(2), 137-144.
  47. Gašević, D., Dawson, S., & Siemens, G. (2015). 'Let's not forget: Learning analytics are about learning'. TechTrends, 59(1), 64-71.
  48. Grek, S. (2009). 'Governing by numbers: The PISA 'effect' in Europe'. Journal of Education Policy, 24(1), 23-37.
  49. Grek, S., & Ozga, J. (2009). 'Governing education through data: Scotland, England and the European education policy space'. British Educational Research Journal, 36(6), 937-952.
  50. Halverson, R. (2010). 'School formative feedback systems'. Peabody Journal of Education, 85(2), 130-146.
  51. Ifenthaler, D., & Tracey, M. W. (2016). Exploring the relationship of ethics and privacy in learning analytics and design: implications for the field of educational technology. Educational Technology Research and Development, 64(5), 877-880.
  52. Ingle, W. K., & Cramer, T. (2013). 'Not Just Accountability: A Cost-Effectiveness Analysis of Third- Grade Reading Diagnostic Tools'. In B. G. Barnett, A. R. Shoho & A. J. Bowers (Eds.), School and district leadership in an era of accountability (pp. 143-171).
  53. Jacobs, J., Gregory, A., Hoppey, D., & Yendol- Hoppey, D. (2009). 'Data Literacy: Understanding Teachers' Data Use in a Context of Accountability and Response to Intervention'. Action in Teacher Education, 31(3), 41-55.
  54. Jimerson, J. B., & Childs, J. (in press). Signal and Symbol: How State and Local Policies Address Data- Informed Practice. Educational Policy.
  55. Jimerson, J. B., & Wayman, J. C. (2015). 'Professional Learning for Using Data: Examining Teacher Needs and Supports'. Teachers College Record, 117(4), 1-36.
  56. Jürges, H., Schneider, K., & Büchel, F. (2005). 'The effect of central exit examinations on student achievement: Quasi-experimental evidence from TIMSS Germany'. Journal of the European Economic Association, 3(5), 1134-1155.
  57. Kickmeier-Rust, M. D., & Albert, D. (2013). 'Learning analytics to support the use of virtual worlds in the classroom'. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 358-365). Springer Berlin Heidelberg.
  58. Knowles, J. E. (2015). 'Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin'. Journal of Educational Data Mining, 7(3), 18-67.
  59. In C. Romero, S. Ventura, M. Penchenizkiy & R. S. Baker (Eds.), Handbook of Educational Data Mining (pp. 43-55).
  60. Koedinger, K. R., D'Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). 'Data mining and education'. Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333-353.
  61. Laurillard, D. (2008). 'The teacher as action researcher: Using technology to capture pedagogic form'. Studies in Higher Education, 33(2), 139-154.
  62. Lawson, C., Beer, C., Rossi, D., Moore, T., & Fleming, J. (2016). Identification of 'at risk' students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution. Educational Technology Research and Development, 64(5), 957-968.
  63. Leithwood, K. (2013). 'Concluding synthesis and commentary'. In B. G. Barnett, A. R. Shoho & A. J. Bowers (Eds.), School and district leadership in an era of accountability (pp. 255-269).
  64. Lovenheim, M. F., & Walsh, P. (2014). 'Does Choice Increase Information? Evidence from Online School Search Behavior', paper presented at the 2016 AEFP Conference.
  65. Mac Iver, M. A. (2013). 'Early Warning Indicators of High School Outcomes'. Journal of Education for Students Placed at Risk (JESPAR), 18(1), 1-6.
  66. Macfadyen, L. P., & Dawson, S. (2012). 'Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan'. Educational Technology & Society, 15(3), 149-163.
  67. Mandinach, E. B., & Gummer, E. S. (2013). 'A Systemic View of Implementing Data Literacy in Educator Preparation'. Educational Researcher, 42(1), 30-37.
  68. Mandinach, E. B., Honey, M., & Light, D. (2006). 'A Theoretical Framework for Data-Driven Decision Making', Paper presented at the American Educational Research Association, San Francisco, CA Mandinach, E. B., Honey, M., Light, D., & Brunner, C. (2008). 'A Conceptual Framework for Data-Driven Decision Making'. In E. B. Mandinach & M. Honey (Eds.), Data-Driven School Improvement: Linking Data and Learning (pp. 13-31). New York: Teachers College Press.
  69. Marsh, J. A. (2012). 'Interventions Promoting Educators' Use of Data: Research Insights and Gaps'. Teachers College Record, 114(11), 1-48.
  70. Marsh, J. A., & Farrell, C. C. (2015). How leaders can support teachers with data-driven decision making: A framework for understanding capacity building. Educational Management Administration & Leadership, 43(2), 269-289.
  71. Meyer, H. D., & Benavot, A. (Eds.). (2013). PISA, power, and policy: The emergence of global educational governance. Symposium Books Ltd.
  72. Mockler, N. (2013). 'Reporting the 'education revolution': MySchool. edu. au in the print media'. Discourse: Studies in the Cultural Politics of Education, 34(1), 1-16.
  73. Mosteller, F., & Boruch, R. F. (Eds.). (2002). Evidence matters: Randomized trials in education research.
  74. Murray, J. (2013). 'Critical issues facing school leaders concerning data-informed decision-making'. School Leadership & Management, 33(2), 169-177.
  75. Piety, P. J. (2013). Assessing the educational data movement. New York, NY: Teachers College Press.
  76. Reeves, P. L., & Burt, W. L. (2006). 'Challenges in Data-Based Decision-Making: Voices from Principals'. Educational Horizons, 85(1), 65-71.
  77. Riehl, C. (1999). 'Labeling and letting go: An organizational analysis of how high school students are discharged as dropouts'. In A. M. Pallas (Ed.), Research in sociology of education and socialization (pp. 231-268). New York: JAI Press.
  78. Romero, C., & Ventura, S. (2007). 'Educational data mining: A survey from 1995 to 2005'. Expert systems with applications, 33(1), 135-146.
  79. Rumberger, R. W., & Palardy, G. J. (2005). 'Test scores, dropout rates, and transfer rates as alternative indicators of high school performance'. American Educational Research Journal, 42(1), 3-42.
  80. Rutledge, S., & Gale Neal, B. (2013). '"The numbers speak for themselves": Data use and organization of schooling in two Florida elementary schools' in The infrastructure of accountability: Data use and the transformation of American education (pp. 113-128). Cambridge, Mass: Harvard Education Press.
  81. Scheuer, O., & McLaren, B. M. (2012). 'Educational data mining'. In Encyclopedia of the Sciences of Learning (pp. 1075-1079). Springer US.
  82. Schildkamp, K., Poortman, C., Luyten, H., & Ebbeler, J. (in press). Factors promoting and hindering data-based decision making in schools. School Effectiveness and School Improvement, 1-17.
  83. Schmidt, W. H., McKnight, C. C., Houang, R. T., Wang, H., Wiley, D. E., Cogan, L. S., & Wolfe, R. G. (2001). Why Schools Matter: A Cross-National Comparison of Curriculum and Learning. The Jossey-Bass Education Series. Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741.
  84. Scholes, V. (2016). The ethics of using learning analytics to categorize students on risk. Educational Technology Research and Development, 64(5), 939-955.
  85. Schutt, R., & O'Neil, C. (2013). Doing Data Science: Straight Talk from the Frontline. Cambridge, MA: O'Reilly.
  86. Siemens, G. (2013). 'Learning Analytics: the emergence of a Discipline'. American Behavioral Scientist, 57(10), 1380-1400.
  87. Siemens, G., & Long, P. (2011). 'Penetrating the Fog: Analytics in Learning and Education'. EDUCAUSE review, 46(5), 30.
  88. Slade, S., & Prinsloo, P. (2013). 'Learning analytics ethical issues and dilemmas'. American Behavioral Scientist, 57(10), 1510-1529.
  89. Slavin, R. E. (2002). 'Evidence-based education policies: Transforming educational practice and research'. Educational Researcher, 31(7), 15-21.
  90. Stodden, V., Guo, P., & Ma, Z. (2013). Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals. PloS one, 8(6), e67111.
  91. Spector, J. M. (2016). Ethics in educational technology: towards a framework for ethical decision making in and for the discipline. Educational Technology Research and Development, 64(5), 1003-1011
  92. Strietholt, R., Bos, W., Gustafsson, J. E., & Rosén, M. (Eds.). (2014). Educational Policy Evaluation through International Comparative Assessments. Waxmann Verlag.
  93. Tibshirani, R. (1996). 'Regression shrinkage and selection via the lasso'. Journal of the Royal Statistical Society. Series B (Methodological), 267-288.
  94. Turner, E. O., & Coburn, C. E. (2012). 'Interventions to promote data use: An introduction'. Teachers College Record, 114(11), 1-13.
  95. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). 'Learning analytics dashboard applications'. American Behavioral Scientist, 57(10), 1500-1509.
  96. Wayman, J. C. (2005). 'Involving teachers in data- driven decision making: Using computer data systems to support teacher inquiry and reflection'. Journal of Education for Students Placed at Risk, 10(3), 295-308.
  97. Wayman, J. C., & Stringfield, S. (2006). 'Data use for school improvement: School practices and research perspectives'. American Journal of Education, 112(4), 463-468.
  98. Wayman, J. C., Cho, V., Jimerson, J. B., & Snodgrass Rangel, V. W. (2015). 'A Look into the Workings of Data Use in a Mid-Sized District'. In I. E. Sutherland, K. L. Sanzo & J. P. Scribner (Eds.), Leading Small and Mid- Sized Urban School Districts (Vol. 22, pp. 241-275): Emerald.
  99. Wayman, J. C., Cho, V., Jimerson, J. B., & Spikes, D. D. (2012). 'District-wide effects on data use in the classroom'. Education Policy Analysis Archives, 20(25), 1- 28.
  100. Whitty, G. (2006). 'Education(al) research and education policy making: is conflict inevitable?'. British Educational Research Journal, 32(2), 159-176.
  101. Williamson, B. (2016). 'Digital education governance: data visualization, predictive analytics, and 'real- time'policy instruments'. Journal of Education Policy, 31(2), 123-141.
  102. Willis III, J. E., Campbell, J., & Pistilli, M. (2013). 'Ethics, Big Data, and Analytics: A Model for Application', EDUCAUSE Review Online.
  103. Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64(5), 881-901.
  104. Woessmann, L. (2008). 'Efficiency and equity of European education and training policies'. International Tax and Public Finance, 15(2), 199-230.
  105. Woessmann, L., & West, M. (2006). 'Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS'. European Economic Review, 50(3), 695-736.
  106. Yang, M., Goldstein, H., Rath, T., & Hill, N. (1999). 'The use of assessment data for school improvement purposes'. Oxford Review of Education, 25(4), 469-483.
  107. Yoon, S. Y. (in press). Principals' Data-Driven Practice and Its Influences on Teacher Buy-in and Student Achievement in Comprehensive School Reform Models. Leadership and Policy in Schools, 1-24.

Cited by

Sorted by citations

Authors
Agasisti, T., & Bowers, A. J.
Affiliations
Columbia University (US)
Corresponding author
Agasisti, Tommaso
ORCID
Venue
Handbook of Contemporary Education Economics
Publisher
Edward Elgar Publishing
Published
2017-12-29
Pages
184-210
DOI
10.4337/9781785369070.00014
OpenAlex
W2783293565
Cited by
83
References
8
Citations by year
2026: 2 · 2025: 15 · 2024: 11 · 2023: 11 · 2022: 7 · 2021: 10 · 2020: 13 · 2019: 8 · 2018: 5 · 2017: 1
FWCI
21.98
Type
book chapter
Language
en
Open access
Licence
other-oa
Locations
  • Edward Elgar Publishing eBooks (publishedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Columbia Academic Commons (Columbia University)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Physical Sciences › Computer Science › Information Systems › Educational and Technological Research
Keywords
educational data science, data analytics in education, educational decision-making, learning analytics, academic analytics, educational data mining, education policy-making, school management, classroom pedagogy, barriers to data use

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