The Tutor Development Needs of Writing Centre Consultants Working with Undergraduate Students Using English as an Additional Language
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".