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Record W2106140984 · doi:10.33524/cjar.v16i2.212

STRUGGLING WRITERS’ USE OF IPAD ART AND TEXT APPS FOR STORY WRITING

2015· article· en· W2106140984 on OpenAlexvenueno aff
Michael Dunn

Bibliographic record

VenueThe Canadian Journal of Action Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsMnemonicIntervention (counseling)PsychologyQuality (philosophy)MultimediaJournal writingComputer sciencePedagogyMathematics educationTeaching methodVisual artsArt

Abstract

fetched live from OpenAlex

Many children struggle with writing. Idea generation, planning, organizing ideas, editing, and encoding a final text all have a part in a child’s producing a publishable copy. Part of the challenge for these students is how working memory can be overloaded when initially planning and writing at the same time. To address this issue, the author offered eight struggling writers the opportunity to employ a mnemonic strategy entitled STORY, which included verbalizing story ideas while noting them in an illustration with an iPad art app and then later keyboarding the text. The students received intervention programming over 24 sessions, 45 minutes each. All students demonstrated improvement with story content and quality. Two of the lowest-performing students participated in an additional 24 sessions to promote improvement.

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)0.009
metaresearch head score (Gemma)0.003
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 score0.385
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.511
GPT teacher head0.484
Teacher spread0.027 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2015
Admission routes1
Has abstractyes

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