Cited by 29Reading and Literacy DevelopmentLinguistics and Education ResearchEFL/ESL Teaching and Learning

Prior knowledge and its activation in elementary classroom discourse

Hattan, Courtney, Alexander, Patricia A.·2020·Reading and Writing

Integrity signals

Registry

Crossref · 10.1007/s11145-020-10022-8

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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

Claims the paper makes

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  • Adequate prior knowledge of the text topic can facilitate basic reading comprehension, counter cohesion gaps, generate successful inferences and construct a situational model of text to support comprehension at higher levels.

    “Adequate prior knowledge of the text topic can not only facilitate basic reading comprehension (Hattan and Alexander 2020) but also counter cohesion gaps in the text, generate successful inferences and construct a situational model of text to support comprehension at higher levels (Li 2017).”

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Authors
Hattan, C., & Alexander, P. A.
Affiliations
  • Illinois State University (US)
  • University of Maryland, College Park (US)
Corresponding author
Hattan, Courtney
Venue
Reading and Writing
Published
2020-02-10
Volume
33
Issue
6
Pages
1617-1647
ISSN
  • 0922-4777 (ISSN-L)
  • 1573-0905
OpenAlex
W3006108455
Cited by
29
References
2
Citations by year
2026: 6 · 2025: 5 · 2024: 7 · 2023: 6 · 2022: 3 · 2020: 2
FWCI
4.15
Type
journal article
Language
en
Open access
closed
Locations
Reading and Writing (publishedVersion)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
  • Social Sciences › Arts and Humanities › Literature and Literary Theory › Linguistics and Education Research
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
Keywords
prior knowledge activation, classroom discourse, early reading instruction, teacher prompts, teacher-student relationship, student response

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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.

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