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Record W1515649113 · doi:10.20360/g2s88d

“Thanks for the Assignment!”: Digital Stories as a Form of Reflective Practice

2012· article· en· W1515649113 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueLanguage and Literacy · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTransformative learningConstruct (python library)Digital literacyLiteracyPedagogyMathematics educationService (business)SociologyLanguage artsThe artsMultimediaComputer sciencePsychologyVisual artsArt

Abstract

fetched live from OpenAlex

In this article we examine pre-service teachers’ digital literacy stories and post-assignment reflections for evidence of transformative pedagogy. The language arts course design employs both a new literacies approach (Lankshear & Knobel, 2006) and a multiliteracies pedagogical framework (New London Group, 1996). These frameworks are also applied to help us examine the pre-service teachers’ digital stories and reflections. The data consist of approximately 150 digital stories and written student reflections collected over three years. We are encouraged by the finding that the multimedia nature of the assignment appears to help pre-service teachers construct new understandings of literacies, particularly when the digital stories are shared as part of the adult classroom experience. We conclude that digital stories hold potential to encourage pre-service teachers to think critically about how they were taught relative to the teachers they wish to become.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.021
GPT teacher head0.318
Teacher spread0.298 · 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