An Exploratory Study of a Korean EFL Teacher’s Identity Shift during the Pandemic
Bibliographic record
Abstract
Since the outbreak of COVID-19, language teachers have been asked to rapidly react and adapt to constantly changing teaching environments in order to understand their students’ needs in L2 learning and make judgments in conditions of uncertainty. The COVID-19 outbreak has highlighted the need to understand how teachers’ identities evolve during such difficult times and situations. In response to this need, this study reports the findings from my qualitative case study on a Korean English teacher’s identity shift. Drawing upon Foucault’s (1983) notion of ethical self-formation, I examined how the Korean English teacher negotiated and developed her identity to adjust to drastically changing working environments as she weighted the benefits and challenges of online and offline education, particularly for novice Korean EFL learners. Data were collected through various sources from an experienced Korean English teacher, called Anna, at a regional foreign language center in South Korea over the course of two years. Due to the pandemic, she had to make the transition from offline to online teaching. Further, her center closed one year after the outbreak of the pandemic, and she was reassigned as a travelling teacher in a multi-school program for underachieving English students. The findings reveal that Anna became more agentive in searching for and utilizing multiple resources for teaching, showing her reflective and action-oriented practices to involve in ethical, practiced, and productive identity work (Miller, Morgan, & Medina, 2017). The findings contribute to expanding our understanding of the transformative potentials of language teachers’ identity.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".