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Record W1546971882

iPads and Digital Storytelling: Successes and Challenges With Classroom Implementation

2011· article· en· W1546971882 on OpenAlexaff
Doug Reid, Nathaniel Ostashewski

Bibliographic record

VenueeSpace (Curtin University) · 2011
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsAthabasca University
Fundersnot available
KeywordsComputer scienceMultimediaDigital storytellingStorytellingMathematics educationPedagogyNarrativeSociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

This paper presents successes and challenges of two iPad implementation projects in K-12 schools. The focus of the implementation was on the incorporation of digital storytelling activities supported by iPad technology into classroom practice. Findings of the research provide insights regarding the introduction of new technology into the classroom, engagement of students with mobile connected devices, use of specialty apps, and issues/solutions identified with the management of iPad devices. Numerous implications of this research, including examining the constructionist theories and the learning behind the creation of educational artifacts, apply to the development of 21st Century learner skills. In practice, both management issues regarding implementing new technology as well as the pedagogical implications exist; providing students with technology access that allows them to create digital stories and share them beyond the classroom.

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)0.033
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.100
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0050.005
Scholarly communication0.0120.008
Open science0.0050.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.078
GPT teacher head0.308
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations11
Published2011
Admission routes1
Has abstractyes

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