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Record W4400525591 · doi:10.53967/cje-rce.6275

Digital Literacies Learning Needs in Rural Ontario Elementary Schools: Teacher Insights

2024· article· en· W4400525591 on OpenAlexaffvenueabout
Michelle Schira Hagerman, Sima Neisary

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2024
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMathematics educationPedagogyPsychologySociology

Abstract

fetched live from OpenAlex

Despite known inequalities of digital access in rural Canada, we know little about the foundational digital literacies learning needs of students attending rural elementary schools. This exploratory case study, conducted in Ontario, presents 13 rural-serving Grade 4–6 teachers’ insights on the access needs and digital literacies learning needs of their students. Results point to a set of mixed digital materialities and opportunities across home and school that raise concerns of digital marginalization for children who are least connected. Teachers named 14 unique digital literacies learning needs in relation to online reading, digital writing, and participation. Their insights also reflect an understanding of digital literacies learning as situated in a complex assemblage of structural, social, emotional, cultural, cognitive, developmental, technological, and material considerations. Implications for policy and the design of uniquely rural solutions to digital literacies instruction are discussed.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.005
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2024
Admission routes3
Has abstractyes

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