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Record W4387316877 · doi:10.18806/tesl.v39.i2/1375

The use of digital tools in French as a Second Language teacher education in Ontario

2023· article· en· W4387316877 on OpenAlexafffundvenueabout
Taylor Boreland, Heather Lotherington, Brittany Tomin

Bibliographic record

VenueTESL Canada Journal · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of ReginaYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPedagogyTeacher educationMathematics educationLanguage educationSociologyLanguage acquisitionPsychology

Abstract

fetched live from OpenAlex

This article discusses a 2021 survey of French as a second language (FSL) teacher candidates (TCs) in Faculties of Education in Ontario whose practice teaching experiences were affected by the COVID-19 pandemic, pivoting them into remote FSL teaching and learning. The survey, which formed a component of a larger mixed method SSHRC-funded research project1, was designed to capture the varied practice teaching experiences of FSL teacher candidates in order to ascertain symmetries and asymmetries in their preferred digital practices, devices and tools for social communication, and for French language teaching and learning. Survey respondents (N=17) from different teacher education programs in universities across Ontario provided a picture of scattered and fragmented approaches to FSL digital pedagogies and hinted at a persistent reliance on traditional FSL pedagogies in the classroom. Digital preferences for teaching and learning were, interestingly, not parallel, and were anchored in common educational tools and platforms that reaffirmed teacher-centred approaches to FSL rather than more innovative, learner-centred, and agentive language teaching and learning. The survey results raise an important question: Has FSL teacher education adequately moved with the communicative changes wrought by socio-technical change and related pedagogical innovations?

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.979

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.000

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 head0.040
GPT teacher head0.240
Teacher spread0.200 · 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 designNot applicable
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

Citations4
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueTESL Canada JournalSame topicSecond Language Learning and TeachingFrench-language works237,207