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Record W3110556947 · doi:10.31468/cjsdwr.827

The Tutor Development Needs of Writing Centre Consultants Working with Undergraduate Students Using English as an Additional Language

2020· article· en· W3110556947 on OpenAlexaffvenueabout
Maya A. Pilin, Michael Landry, Scott Roy Douglas, Amanda Brobbel

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTUTORNeeds analysisPsychologyContext (archaeology)PedagogyMathematics educationPoint (geometry)Medical educationMedicine

Abstract

fetched live from OpenAlex

Growing numbers of international students and newcomers attending post-secondary studies means that there are more students using English as an additional language (EAL) at Canadian universities. Consequently, writing centres have recognized the need for specialized training for their tutors as they support these students. However, it is difficult to find research on tutor perspectives about these training programs in a Canadian context. The current project aimed to gather insight regarding tutors’ perceived knowledge and needs in helping students using EAL with their writing. The findings point to a need for tutor development which specifically contributes to supporting EAL writers in the form of ongoing interactive workshops on language awareness, instructional strategies, and communication skills. Twelve writing tutors completed a questionnaire in which they were asked about their previous EAL experiences, their current understanding of tutoring students using EAL, and their training needs in this area. A qualitative analysis revealed that tutors hoped to develop their ability in explaining grammatical rules, as well as improve their communication skills and developing pedagogical skills. These identified areas of development suggest a need to establish formal training in additional language acquisition theory, language awareness, and intercultural communication strategies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.855

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)0.0000.000

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 head0.118
GPT teacher head0.361
Teacher spread0.243 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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