The Construction of Teacher Candidates' Imaginaries and Identities in Canada, Colombia, and Chile: An International Comparative Multiple Narrative Case Study
Bibliographic record
Abstract
Preparing future language teachers to address the needs of diverse learners has made it imperative for teacher education programs to embed reflective components in their curricula. As part of these efforts, researchers and teacher educators have become more aware of the need to have future teachers examine their own teaching Selves (Kumaravadivelu, 2012) and their language teacher identities (LTIs) (Barkhuizen, 2016, 2017a; Morgan, 2004; Varghese, Morgan, Johnston Johnson, 2005). One of the notable advances in this burgeoning field is understanding that teachers’ identities can be viewed as the constant intersection between: teachers’ past Selves, their current Selves, and their ideal or imagined Selves (Kubanyiova, 2012). It is precisely within this area of understanding the role of future teachers’ imagination in language teacher education (LTE) and LTIs that this dissertation aims to contribute. The present study provides a detailed account of how future language teachers construct their imaginaries in three five-year teacher preparation programs, one in Ontario, Canada (preparing teachers of French as a second language); one in Chile (preparing teachers of English as a foreign language; and one in Colombia (preparing teachers of both English and French as foreign languages). iii This goal is achieved through an international, comparative, multiple, narrative case study. Data was collected through questionnaires, document collection, familiarization with the contexts, as well as innovative data collection tools like multimodal identity texts (Cummins Early, 2011; Valencia Herath, 2015), and online memes (Shifman, 2014). Findings show the presence of six recurring narratives in the accounts gathered from participants. These include: 1) globalization; 2) neoliberalism; 3) contemporary mediated globalized (mostly Eurocentric) culture; 4) a narrative of interconnectedness in a globalized world; 5) a predominantly Eurocentric understanding of LTE; and 6) images and stories of teachers and learners. This study highlights the influence of these six narratives in the construction of participants’ imaginaries, as well as their agency to transform such narratives to suit their needs and use them to negotiate their own professional identities. Implications for the further exploration of teacher candidates’ imaginaries in LTE programs, as well as future avenues for research are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| 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".