Implementing Translanguaging in an English as a Second Language Continuing Education Program: A Multiple Case Study on Teachers' Perspectives and Challenges
Bibliographic record
Abstract
English as a Second Language teachers are tasked with creating an environment conducive to academic achievement and English language growth. Over the past decade, language education has transitioned from conventional monolingual approaches that stress the separation of languages to the adoption of translanguaging. Translanguaging promotes the inclusion of students’ languages in the classroom while facilitating language and subject learning. However, during the COVID-19 pandemic, while pedagogy needed to be adapted to online environments, the constraints of online teaching, coupled with limitations on time for preparation, restricted the possibilities for adopting a translanguaging pedagogical approach. Nevertheless, the exceptional circumstances in which my research took place revealed the obstacles and potential to enact an innovative pedagogy in times of crisis. The multiple case study focused on implementing a translanguaging approach within a continuing education program in Ontario, Canada. The objective was to uncover teachers' stances and understanding of translanguaging, demonstrated in their teaching practices, along with students' responses to these practices. Informed by collaborative action research, I worked with each teacher to design a course plan based on a translanguaging approach and I provided individualized support as they implemented strategies and resources for their adult learners. To analyze the findings, I integrated translanguaging theory (García & Li, 2014) and Kumaravadivelu’s (2012) Knowing, Analyzing, Recognizing, Doing, and Seeing (KARDS) model. This modular model served as a lens to analyze participants' experiences with translanguaging and a framework for proposing a translanguaging pedagogy within a Thirdspace context (Soja, 1996). The teachers exhibited an openness to the approach while also highlighting the challenges unique to their teaching context. Their motivation primarily centered on helping students preserve their languages and providing scaffolding to facilitate their learning. However, their nuanced orientations toward translanguaging were distinctly shaped by their experiences in language learning and teacher training. Most importantly, the teacher and researcher established a synergistic collaboration motivated by the transformative nature of the professional development process. In conclusion, the results underscore the significance of tailored professional development to equip teachers with the knowledge and skills required for intentionally including an innovative pedagogical approach, like translanguaging, into adult education settings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".