MétaCan
Menu
Back to cohort
Record W2512890499 · doi:10.55016/ojs/jet.v37i3.52670

Core French Teachers and Technology: Classroom Application and Belief Systems

2018· article· en· W2512890499 on OpenAlexaboutno aff
Miles Turnbull, Geoff Lawrence

Bibliographic record

VenueJournal of educational thought. · 2018
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsCore (optical fiber)Belief systemPsychologyMathematics educationPedagogyCognitive scienceComputer scienceEpistemologyPhilosophyTelecommunications

Abstract

fetched live from OpenAlex

This research reports results from a survey of274 coreFrench teachers across Canada to define teachers' belief systemstowards computer technology, teachers' experiences withcomputers, and factors contributing to the use of computers in thecore French classroom. Results revealed three principle constructsdefining core French teacher belief systems towards computertechnology, two representing affective perceptions towardscomputers and one representing a more cognitive perception of theeducational utility of computers in core French teaching. A majorityof teachers reported having used computers in their classes and feltcomputers enhanced their students' learning. In spite of this, asizeable number of teachers reported not having used computers intheir teaching, primarily due to lack of access and knowledge abouthow to integrate computers into core French curriculum.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.730

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of educational thought.Same topicOnline Learning and AnalyticsFrench-language works237,207