The Power of Story in the ESL Classroom
Bibliographic record
Abstract
Although considerable research has examined the use of literature in the second language (L2) classroom, there has been less investigation into the integration of learners' personal stories in the English as a second language (ESL) classroom. Following Wajnryb's (2003) categorizations of story as language learning, genre, and the creation of what she termed a ‘storied classroom,’ this study explores the ways in which learners' stories are used in the ESL classroom. Five ESL instructors and nine adult ESL learners enrolled in ESL classes at a settlement agency in Edmonton were interviewed about the practice, benefits, and challenges of incorporating personal stories into the L2 classroom. Participants perceived that story promoted language learning, an understanding of genre, and community building, while also enhancing authenticity, affect, and motivation. This article provides guidelines and recommends resources for using personal story in the adult ESL classroom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".