MétaCan
Menu
Back to cohort

Arts-Integrated Multiliteracies as Dialogic Catalysts With English Language Learners in a Canadian University

2024· book-chapter· en· W4403484188 on OpenAlexaffabout
Chenkai Chi, Mehdia Hassan, Pauline Sameshima

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2024
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsLakehead UniversityUniversity of TorontoUniversity of Windsor
Fundersnot available
KeywordsDialogicThe artsSociologyLinguisticsEnglish languagePedagogyMultimodalityLanguage artsPsychologyMathematics educationArtVisual artsPhilosophy

Abstract

fetched live from OpenAlex

This study examines the utility of an arts integrated multiliteracies approach called Parallaxic Praxis (PP) in English language learning among English language learners in a Canadian university. It focuses on how this approach influences learning and the insights gained from its application. The research focused on two participants' experiences in the Learning English Through the Arts program. Data was gathered through participant observation and interviews. Findings reveal that the PP model enables social justice by serving both as a research tool that values and encourages the diverse perspectives of participants and researchers, and a pedagogical tool that fosters self-identity formation and knowledge-sharing. This approach shifts the landscape of English language education beyond a deficit thinking model. Furthermore, integrating arts with multiliteracies is highlighted as a vital disposition for addressing the learning needs of English language learners. The study offers productive implications for English language teaching policies and pedagogical strategies for ESL teachers.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0250.012
Scholarly communication0.0060.002
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAdvances in educational technologies and instructional design book seriesSame topicLiteracy, Media, and EducationFrench-language works237,207