A critical exploration of the evolving identity and online pedagogical realisations of an EAP teacher during the COVID-19 pandemic: \nan autoethnographic study at a Canadian public college
Bibliographic record
Abstract
Fully virtualised English for Academic Purposes (EAP) pre-sessional courses are offered at most public colleges in Ontario. A significant body of research indicates that language teacher identity (LTI) influences teachers’ pedagogical decisions, assessment practices and interaction with the learners, and therefore becomes identity work. However, there is little research which has explored Canadian EAP teachers’ perceptions of their teaching practices, particularly in the context of emergency remote delivery. \n To address this research lacuna, I conducted an autoethnographic study of my virtualised EAP teaching context over two research periods during the COVID-19 pandemic in March 2020 and April 2021. Informed by Vygotsky’s sociocultural theory of human mental processing as theoretical framework, I utilised the concepts of scaffolding, the More Knowledgeable Other (MKO) and Zone of Proximal Development and generated data through reflections in a teacher learning journal and Farrell’s Framework for Reflecting on Practice (2015). \n Utilising the reflexive thematic data analysis method, I identified a conflict between the reflexive, projected and ascribed facets of my LTI, struggling to become and negotiate an imagined identity of an online EAP teacher. The newly empowered position and my developing interactional competence disrupted my established pedagogical approach to create a learner-centred environment and underscored the need to develop EAP specific teaching methodology post-COVID-19. The lack of pre-service and systematic in-service EAP technological and pedagogical training to stimulate teacher development suggests integrating critical reflection on LTI during teacher preparation programmes. \n Becoming critically aware of the effect of teachers’ identities on their methodology through reflective narratives, may have important implications for EAP teachers’ pedagogical realisations and learning experience of EAP students. Therefore, autoethnographic studies of EAP teachers’ views of their online and in-person pedagogy and methodology contribute to a vibrant and promising research strand with important implications for the professional practice of EAP in Canada and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".