Preparing diverse teachers for diverse students: perceptions of linguistic identity, experiences and teaching responsibilities in a Canadian teacher education program
Bibliographic record
Abstract
As the population in Canada is growing increasingly diverse, it has become critical to understand how to respond to such demographic change in the public school system and in teacher preparation programs. This thesis responds to this call by exploring perceptions of linguistic identity, experiences (participation, choices and challenges), teaching responsibilities and reported changes of future secondary teachers from diverse backgrounds in a Canadian teacher preparation program. The participants in this study include 25 linguistically diverse teacher candidates (TCs) enrolled in a 9-month intensive Bachelor of Education program and four teacher educators (TEs). Data were collected over the period of six months through interviews, questionnaires, observations and additional documents. This study draws on various sociocultural perspectives and more specifically the notion of "community of practice" (COP) (Lave Wenger, 1991). The examination of the participation, choices and challenges of TCs demonstrated the multiple, interrelated and complex issues underlying their experiences. These factors include specific COPs, instructors, peers, support in the program, age, culture, linguistic identity, personality, attitude, length of residence in Canada, curriculum and instructional practices. Findings also suggest that personal and professional experiences with ESL and diversity-related issues contribute to a higher level of empathy with English Language Learners (ELLs). However, empathy does not ensure that TCs are more likely to employ ESL-inclusive pedagogy. Explicit instruction on language education and diversity-related issues is required to help TCs develop such skills. The exploration of how TCs reported having changed as a result of the program revealed that there is evidence of both stability and change in their beliefs. The exploration of the respondents' native and nonnative self-ascription and self-assessed English proficiency, juxtaposed with the judgments of three TEs revealed the idiosyncratic and complex interplay between individuals and the social contexts in which they negotiate their linguistic identities. A six-category typology which better depicts the linguistic identities of the TCs is proposed. This inquiry reconceptualizes the native/nonnative dichotomy suggesting that linguistic identities should be viewed as dynamic, dialogic, relational, situated and multiple and thus explored in the process of their development in specific social contexts rather than as a unitary and fixed phenomenon in isolation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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, 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".