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Record W3709510 · doi:10.26522/tl.v5i1.299

Differentiating Instruction with Digital Storytelling While Making Connections to Critical Literacy

2009· article· en· W3709510 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

VenueTeaching and Learning · 2009
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsBrock University
Fundersnot available
KeywordsDigital storytellingStorytellingDigital literacyCritical thinkingLiteracyInformation literacyMathematics educationPedagogyCritical literacyProcess (computing)PsychologyComputer scienceNarrative

Abstract

fetched live from OpenAlex

Incorporating digital storytelling activities into learning experiences for students not only engages students in acquisition of 21st century skills, but also provides teachers with opportunities to differentiate instruction. This paper describes a Digital Storytelling Workshop that matched diverse student learners with teacher candidates in creating digital stories, and the resulting investigation of how participation in the project impacted the ability of student learners to demonstrate critical literacy. Data sources included exit surveys, student interviews, researcher observational field notes, and student products from the workshop. Findings indicated that the digital story-making process engaged students in all levels of higher order thinking skills (Anderson & Krathwohl, 2001) and at least one component of critical literacy, identified by Wolk (2003), as advocacy, evaluating or solving real-world problems, or making reflective connections between classroom content and culture and/or society.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.034
GPT teacher head0.376
Teacher spread0.342 · 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