Becoming a Teacher of Culturally and Linguistically Diverse Learners: A Future Content-area Teacher’s Professional Identity Construction through Online Coursework
Bibliographic record
Abstract
In this exploratory qualitative study, we applied Clarke’s (2009) framework of ethical self-formation (substance, authority sources, self-practices, telos) to data collected from one pre-service teacher’s completed assignments in an online course at a Hispanic-serving university in the Southwestern U.S. This course was dedicated to helping pre-service secondary content-area teachers develop the knowledge, skills, and experience necessary to provide effective instruction for emergent bilinguals (EBs). Findings revealed examples of how the four axes of the participant’s identity manifested in her coursework and how they dynamically interacted. By engaging in reflective practices, she developed an increased awareness of her own sociocultural-linguistic identity and gained a better understanding of EB education and advocacy. Implications are discussed for how teacher preparation programs can prepare teachers to enter the field with a stronger sense of who they are and who they want to be as future teachers and advocates for more culturally and linguistically responsive educational practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 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".