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Cited by 112Digital Education and SocietyGlobal Educational Policies and ReformsOnline Learning and Analytics

The structural consequences of big data-driven education

Open access
Zeide, Elana·2017·Big Data
DOI 10.1089/big.2016.0061
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Abstract

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education's crucial impact on individual and collective success, educators and policymakers must consider the implications of data-driven education proactively and explicitly.

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  • The implementation of AI in education raises concerns regarding data privacy and security, as these systems often rely on large volumes of student data for analysis and personalization.

    “The implementation of AI in education raises concerns regarding data privacy and security, as these systems often rely on large volumes of student data for analysis and personalization [25,92].”

    From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [92]

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Authors
Zeide, E.
Affiliations
Princeton University (US)
Corresponding author
Zeide, Elana
ORCID
Zeide, Elana
Venue
Big Data
Publisher
SAGE Publications
Published
2017-06
Volume
5
Issue
2
Pages
164-172
ISSN
  • 2167-6461 (ISSN-L)
  • 2167-647X
DOI
10.1089/big.2016.0061
OpenAlex
W2670772560
Cited by
112
References
28
Citations by year
2026: 24 · 2025: 16 · 2024: 19 · 2023: 13 · 2022: 9 · 2021: 11 · 2020: 5 · 2019: 9 · 2018: 5 · 2017: 1
FWCI
10.18
Type
journal article
Language
en
Open access
Licence
www.liebertpub.com/nv/resources-tools/text-and-data-mining-policy/121/ (tdm)
Locations
  • Big Data (publishedVersion)
  • PubMed (publishedVersion)
  • University of North Carolina School of Law Scholarship Repository (University of North Carolina Hospitals) (submittedVersion)
  • SSRN Electronic Journal (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Digital Education and Society
  • Social Sciences › Political Science and International Relations › Global Educational Policies and Reforms
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
big data in education, data-driven decision making, school structure, pedagogical decision-making, virtual learning environments, algorithmic assessment, student privacy, credentialing, educational technology adoption, parental participation in education
MeSH terms
Data Interpretation, Statistical, Decision Making, Education, Humans, Learning

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