Promoting Engagement with Peer-Reviewed Journal Articles in Adult ESL Programs
Bibliographic record
Abstract
Engagement with current research is essential for the implementation of evidence-informed instructional practices in adult English as a second language classrooms. We explored Canadian administrators’ and instructors’ engagement with peer-reviewed research articles, perceptions of their impact, and ways in which stakeholders could enhance engagement. Online surveys were conducted with 41 administrators and 268 instructors, and 4 administrators participated in a focus group interview. Results revealed that administrators were not actively fostering instructors’ engagement with peer-reviewed research and that neither administrators nor instructors were engaging extensively with research. Those who were reading research, however, reported a positive impact on their work, and 86% of instructors indicated interest in enhancing their engagement. We provide recommendations for professional organizations, program funders, program administrators, and instructors to promote TESL practitioner engagement with research. La participation à la recherche actuelle est essentielle pour la mise en œuvre, dans les cours d’anglais langue seconde pour adultes, de pratiques pédagogiques éclairées par des données probantes. Nous avons examiné l’implication d’administrateurs et d’enseignements canadiens face aux articles de recherche revus par les pairs, les perceptions de l’impact de ceux-ci et des façons dont les parties prenantes pourraient augmenter l’engagement. Des sondages en ligne ont été complétés par 41 administrateurs et 268 enseignants, et un entretien/groupe de discussion a eu lieu avec 4 administrateurs. Les résultats indiquent que les administrateurs n’encourageaient pas activement la participation des enseignants relative à la recherche examinée par les pairs et que ni les administrateurs ni les enseignants ne prenaient part activement à la recherche. Par contre, ceux qui lisaient la recherche ont noté qu’elle avait un impact positif sur leur travail et 86% des enseignants ont indiqué qu’ils voulaient augmenter leur implication. Nous offrons, aux organisations professionnelles, bailleurs de fonds, administrateurs de programmes et enseignants, des recommandations qui visent la promotion de l’implication des enseignants en ALS dans la recherche.
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 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 | 0.071 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".