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Record W3116978157 · doi:10.21432/cjlt27887

Foreign Language Exposure in Knowledge-Building Forums Using English as a Foreign Language

2020· article· en· W3116978157 on OpenAlexvenueno aff
Marni Manegre

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Foreign languagePsychologyLanguage assessmentTask (project management)English as a foreign languageLanguage proficiencyMathematics educationFirst languageLinguistics

Abstract

fetched live from OpenAlex

This study examines whether the students with higher levels of language and cultural awareness relating to the L2 share this knowledge with their peers in collaborative writing tasks when participating in the Knowledge Building International Project (KBIP). The study was conducted in two Spanish classrooms, where the participants were bilingual in both Catalan and Spanish. A pre-questionnaire was used to determine the level of exposure to English language and English culture and the students were scored on their responses and then divided into three groups: low-, medium-, and high-level exposure to English. A one-way ANOVA was used to determine whether exposure to English language and culture outside of the classroom would influence pre-test scores. There is an interaction effect between language and cultural exposure and the pre-test scores (F = 5.17). Upon the conclusion of the collaborative writing task, a one-way ANOVA was used to determine whether there was an interaction effect between language and cultural exposure and the post-test scores (F = 4.47). The student scores increased at the same rate across the groups. This indicates that the students did not share their knowledge of the English language and culture with their peers in this online writing task.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.230
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.237
Teacher spread0.223 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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