Cited by 122Literacy, Media, and EducationEducational Strategies and EpistemologiesLibrary Science and Information Literacy

Just google it! Digital literacy and the epistemology of ignorance

Open access
Bhatt, Ibrar, MacKenzie, Alison·2019·Teaching in Higher Education

Abstract

In this paper we examine digital literacy and explicate how it relates to the philosophical study of ignorance. Using data from a study which explores the knowledge producing work of undergraduate students as they wrote course assignments, we argue that a social practice approach to digital literacy can help explain how epistemologies of ignorance may be sustained. If students are restricted in what they can know because they are unaware of exogenous actors (e.g. algorithms), and how they guide choices and shape experiences online, then a key issue with which theorists of digital literacy should contend is how to educate students to be critically aware of how power operates in online spaces. The challenge for Higher Education is twofold: to understand how particular digital literacy practices pave the way for the construction of ignorance, and to develop approaches to counter it.

Integrity signals

Registry

Crossref · 10.1080/13562517.2018.1547276

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • When students looked for outside sources, they favored the top search results from search engines and clicked on higher-ranked results, even if those results were less credible or relevant.

    “For instance, when students looked for outside sources, they favored the top search results from search engines and clicked on higher-ranked results, even if those results were less credible or relevant (Bhatt & Mackenzie, 2019;Wineburg & McGrew, 2017).”

    From Expanding on the Frames: Making a Case for Algorithmic Literacy (2023) · cites this paper as Bhatt & Mackenzie, 2019

  • Students' research habits made them vulnerable to algorithmic ranking and filtering.

    “Other studies have suggested that students' research habits made them vulnerable to algorithmic ranking and filtering.”

    From Expanding on the Frames: Making a Case for Algorithmic Literacy (2023) · cites this paper as Bhatt & Mackenzie, 2019

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Bhatt, I., & MacKenzie, A.
Affiliations
Queen's University Belfast (GB)
Corresponding author
Bhatt, Ibrar
Venue
Teaching in Higher Education
Published
2019-02-20
Volume
24
Issue
3
Pages
302-317
ISSN
  • 1356-2517 (ISSN-L)
  • 1470-1294
OpenAlex
W2898534435
Cited by
122
References
1
Citations by year
2026: 10 · 2025: 14 · 2024: 24 · 2023: 20 · 2022: 18 · 2021: 15 · 2020: 11 · 2019: 9 · 2018: 1
FWCI
148.57
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Teaching in Higher Education (publishedVersion)
  • Research Portal (Queen's University Belfast) (submittedVersion, other-oa)
Funders
The Society for Research into Higher Education
Topics
  • Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
  • Social Sciences › Library and Information Sciences › Library Science and Information Literacy
Keywords
digital literacy, epistemology of ignorance, ignorance, social practice approach, algorithms, undergraduate students, course assignments, critical awareness, higher education, knowledge production

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