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Record W7095643906

Teaching Core French Through the Arts: Constructing Communicative Competence

2016· article· en· W7095643906 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmAP French LanguageFocus groupClass (philosophy)Qualitative researchData collectionCore competencyGrounded theoryCompetence (human resources)
DOInot available

Abstract

fetched live from OpenAlex

This study is concerned with Grade nine Applied level students ’ attitudes toward learning French. The following paper provides an in-depth case study of one group of 18 students from a Core French class in a Southwestern Ontario inner city high school. Specifically students ’ attitudes toward learning French through the Arts were examined. Guided by the tenets of constructivism and Arts-based research, with the collaboration of the classroom teacher, French/Arts lesson plans were prepared through which students ’ motivation, attitudes, and enthusiasm to speak French whilst in the process of creating art could be examined. Students ’ comments of learning French through Art were compared with their stories of past experiences within the Core French program. Grounded theory and an emerging theme design using qualitative methods of data collection and analysis were used. Observational data, questionnaires, and focus group interviews were conducted in order to triangulate the data collection for analysis. Findings show that students ’ attitudes toward learning French via Arts-based activities were more positive and their enjoyment, motivation to learn, and spoken French increased. Core French in Canada: Toward an intensive model Students ’ attitudes toward the study of French in Canada are increasingly negative (Kissau, 2005; Netten, Riggs, & Hewlett, 1999). When asked how they and their peers viewed Core French courses in high school, the majority (52%) reported they had not had a good experience

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.995

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.302
Teacher spread0.210 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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