Reconsidering poetry’s difficulty and value in English as a second language and English as a foreign languageeducation
Bibliographic record
Abstract
Abstract There is a longstanding stigma attached to the use of poetry in English Language Teaching (ELT). Poetry is considered difficult and peripheral to ELT curricula and L2 teacher education. Until recently, there was little scholarly interest in studying the effectiveness of poetry in second language teaching. This article aims to advance the research on the use of poetry in ELT. Using a systematic literature review approach covering the years 2008–2020, the present study investigates whether poetry remains underused and undervalued. It is structured around the questions: Is poetry a difficult subject to teach to ESL/EFL learners and learn by L2 students? What value is there to teaching poetry in ELT? Of the 199 peer reviewed journal articles retrieved, 43 met the inclusion criteria for this study and were examined in-depth. Two themes emerged from the analysis: the perception of poetry and using poetry as a research tool in ELT. The majority of evidence was qualitative research, which included 19 case studies; whereas, the most used quantitative research designs were Random Control Trials. We attest to the need for more rigorous scholarship on the part of researchers. Results show that poetry is a valuable tool for ESL/EFL students and teachers, and not difficult for L2 learners. This study concludes with a call for including poetry in teaching practices.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".