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Record W4211155351 · doi:10.1177/1086296x221076436

Student-Generated Questions in Literacy Education and Assessment

2022· article· en· W4211155351 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Literacy Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReading comprehensionReading (process)PsychologyLiteracyComprehensionMultilevel modelGrade levelMathematics educationQuality (philosophy)PedagogyLinguisticsComputer science

Abstract

fetched live from OpenAlex

This study investigated the extent to which students’ questioning ability is associated with their literacy abilities, attitudes, perceived text understanding, and interest in the text they read. We further examined these relationships by the type of text they read to generate questions. Fifth- and sixth-grade students ( N = 89) were asked to generate three questions after reading two different types of text. The students also completed reading comprehension and writing tests, as well as a questionnaire about their attitude toward literacy, perceived text understanding, and interest in the text. A hierarchical regression analysis showed that the quality of student-generated questions was predicted by reading comprehension ability, a positive attitude toward writing, and perceived level of understanding of the text, with strong effects related to text genre. We explore the implications of these findings on current pedagogy and assessment practices in literacy education and suggest areas for further research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.563
Teacher spread0.469 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it