Investigating the experiences of in-service English language teachers in the use of language corpora for teaching purposes: An international action research study
Bibliographic record
Abstract
For several decades, applied linguists have highlighted the benefits of using authentic language samples in language learning classrooms in acquiring genuine use of a language. Yet, few EFL teachers use corpora in their classrooms. Could this be because teachers do not know what `a corpus? is? Or could it be a lack of training to use this technological tool to design classroom materials? My research trials a new framework in corpus literacy training with international in-service EFL teachers. By inviting experienced teachers to be co-researchers in my Action Research project, teachers help to shape the training framework for future teachers over a two-year period. They do this by expressing an interest in learning to use corpora in their classroom from an initial questionnaire, recording their experiences of planning and teaching with corpora into reflective journals and completing a post-training survey one year later about their use of corpora in their teaching practice. Quantitative data show that in-service EFL teachers use a wide range of online resources in their classroom and are aware of what corpora are, however, few teachers have been trained to use corpora to design classroom materials. Qualitative data paint the picture of a highly motivated group who want to learn to use new technology in their classroom, that novice users find teaching with corpora to be a brave and exciting new world, and yet, their teaching demands leave little time and energy to invest in professional development. These findings point towards improving teaching conditions for teachers in the private sector. My research concludes that the corpus literacy training framework is effective at teaching in-service EFL teachers to learn to use corpora and design classroom materials with a corpus. It also shows that a majority of teachers continued to teach with corpora a year after the training programme concluded.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".