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Record W4286520725 · doi:10.36315/2022v1end112

BRIDGING LANGUAGE GAPS OF L2 (SECOND LANGUAGE) TEACHERS BY OPTIMIZING THEIR SELF-AWARENESS

2022· article· en· W4286520725 on OpenAlexaffabout
Marie J. Myers

Bibliographic record

VenueEducation and New Developments 2022 – Volume I · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsQueen's University
Fundersnot available
KeywordsInterlanguageComputer scienceSecond-language acquisitionSession (web analytics)Mathematics educationSecond languageLanguage acquisitionPsychologyLinguisticsPedagogyWorld Wide Web

Abstract

fetched live from OpenAlex

"During a Canada-wide consultation session of teacher trainers for future teachers of French, Canada’s official second language (L2), given the problematic situation of unprepared candidates with questionable mastery of the language, some instructors even retreated to a position stating that these students need to be encouraged although they are struggling with French. What this implies is placing role models in classes with inaccurate French, repeating the same situation if not making it even worse as indeed early French immersion is still the chosen protocol by Canadian non-French speaking parents. Young children absorb language like sponges repeating their teacher and if their French is inaccurate, learning the mistakes. What is however of more crucial importance is not to replicate language programs delivery from which learners emerge without sufficient mastery to make themselves understood because of inaccurately learnt language forms. Therefore, we have to uncover remedies to properly guide all learners, through strategies and techniques for their individual management of the language they are trying to acquire-learn. We want to ensure an economy of time in teaching programs with efficient contact times. Revisiting language programme approaches to uncover what was advocated for error correction, we looked at actional attention (Ellis, 1992), work on noticing (Fotos, 1993), markedness (Larsen-Freeman, 2018), interference (Abdullah & Jackson, 1998) interlanguage theory (Selinker, 1972), the monitor model (Krashen, 1982) and recent types of approaches, namely notional functional, communicative, and action-oriented. As well, we gleaned insights from a review of the literature on strategies and techniques including Raab, (1982) on spectator hypothesis with feedback to the whole class; through peer correction by Cheveneth, Chun and Luppesku (1983); with other innovative techniques suggested by Edge (1983); techniques advocated by Vigil and Oller (1976) for oral correction; and correction across modalities (Rixon, 1993). We will report on a qualitative study (Creswell & Poth, 2018) based on an analysis of instructor’s notes regarding the observed effect on some of the strategies that were tried and across different student groups. In this study, notes on how the instructor devised ways of drawing attention and using metacognition to obtain the best results are examined. In addition, ways involving the affective domain, through emotions and also using innovative ways through disruptions etc. were tried to see if they provided a further impact. Students reported that they appreciated the corrective feedback the way it was dispensed. However results show a variety of concerns, namely the problem with deeply fossilized errors, some students’ being over confident about their language ability, and either a deep concern for making errors that is paralyzing or a belief that over time correction will take place in interlanguage development without making any effort. Due to page limitations, in this paper we will essentially present overarching aspects."

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.992

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.014
GPT teacher head0.251
Teacher spread0.236 · 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 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
Published2022
Admission routes2
Has abstractyes

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