Changing students, changing teaching: Understanding the dynamics of adaptation to a changing student population
Bibliographic record
Abstract
As global migrations increase, educators search for effective ways of meeting the learning needs of diverse student populations. I explore this challenge in a study conducted at the high school level where concerns about student diversity and subject matter intersect sharply. Using a case study approach to understand the dynamics of teachers’ adaptations to changing student populations, I document adaptations made by Math and English teachers in a large Canadian city in the areas of curriculum, instruction, and assessment. I examine the goals, conceptions of subject matter, instructional practices, and views about student learning held by Math and English teachers; the teachers contrast as to whether they did or did not reconceptualize and change their practices when faced with new populations of students, specifically African refugee students. I also examine ways in which the teachers’ school contexts, for example, their subject departments, facilitated or inhibited change in their teaching practices. I conclude that different patterns of goals, conceptions of subject matter, and beliefs about students characterize teachers who adapt and those who do not. In the light of these findings I urge teacher education programs to reconsider their exclusive focus on multicultural competence and take these patterns/elements into account in the preparation of teachers for working successfully with changing student populations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".