The rise of Big Data: what does it mean for education, technology, and media research?
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The interest in Big Data is growing exponentially. Research calls, commercial insights, and government initiatives all seem to be focused on exploiting the potential of technology to capture and an...
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All data are always socially constructed: data collection is never a value-free process.
“It is also important to acknowledge here that all data are always socially constructed: data collection is never a value-free process (Eynon 2013).”
From Data-based decision-making for school improvement: Research insights and gaps (2019) · cites this paper as Eynon 2013
In the passage labelled “2013 Taylor & Francis” (page not provided), Eynon says that values are designed into the analysis of data and that whose data are represented can be unequal. This supports the concern that data research is not value-free, but the paper discusses Big Data analysis and representation rather than asserting that all data are socially constructed or that every act of data collection is value-laden. (LLM, conf=0.84)
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“…p. 15) Amidst the current hype around big data (e.g., Boelstorff, 2013) presenting “a paradigm shift in the ways we understand and study our world” (Eynon, 2013, p. 237), there are a number of authors who flag concerns around the “neutrality” of data and algorithms (Boyd & Crawford, 2013;…”
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- Authors
- Eynon, R.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Eynon, Rebecca
- ORCID
- Eynon, Rebecca
- Venue
- Learning, Media and Technology
- Publisher
- Informa UK Limited
- Published
- 2013-02-25
- Volume
- 38
- Issue
- 3
- Pages
- 237-240
- ISSN
- 1743-9884 (ISSN-L)
- 1743-9892
- OpenAlex
- W2010500041
- Cited by
- 154
- References
- 7
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 7 · 2023: 19 · 2022: 8 · 2021: 13 · 2020: 15 · 2019: 8 · 2018: 12 · 2017: 14 · 2016: 29 · 2015: 7 · 2014: 7
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Learning Media and Technology (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Keywords
- big data, educational technology, media studies, data-driven research, research agenda, data capture
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 ›
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