Social Media in English Learning and Teaching: A Duoethnography
Bibliographic record
Abstract
This article examines how social media affects English learning and teaching from the perspectives of two English teachers. By employing duoethnography, we engaged in collaborative conversations exploring each other’s biographies and life experiences in order to shed light on broader cultural, social, and educational issues (Sawyer & Norris, 2013). Through dialogues, we shared our personal stories on the impact of social media on learning and teaching English in two different first-language (L1) settings to enhance our understanding of the topic and connect our experiences to research. Based on these insights, we also proposed practical implications for second-language (L2) teaching. Data were collected from the recordings of face-to-face discussions, digital reflection notes, and recollections shared by the two authors. Three main themes emerged: (1) hesitancy on using social media, (2) Facebook as a prominent social media platform, and (3) learners’ attitudes and perceptions toward using social media in class. Based on these findings, practical implications for L2 teaching are discussed and we hope this duoethnography resonates with readers and generate further discussion for practitioners and researchers alike.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".