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2017· book-chapter· en· W2568031278 on OpenAlexaff
Monica McGlynn-Stewart, Tiffany MacKay, Gouweleeuw Brooke, Lisa Hobman, Nicola Maguire, Emma Mogyorodi, Vicki Ni

Bibliographic record

VenueAdvances in mobile and distance learning book series · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsLiteracyPedagogyPsychologyMathematics educationMultimediaComputer science

Abstract

fetched live from OpenAlex

This research study examined 27 educators' experiences and comfort levels using open-ended tablet applications to support young children's oral and visual literacy learning. The open-ended applications used positioned students as the producers and creators of the literacy content. While the educators did experience some structural, technological, and pedagogical challenges using the applications, they reported many teaching and learning benefits. They found that using the applications empowered them with additional tools to support children in creating, documenting, and reflecting on their learning. Students were also empowered through being able to represent and archive their experiences in multiple modes and in their own voice. Through viewing the slideshows, parents were able to share in their children's school lives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0440.014

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 head0.016
GPT teacher head0.292
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2017
Admission routes1
Has abstractyes

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