Building Teacher Competency to Work with Diverse Learners in the Context of International Education.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing diversity and complexity in classrooms is happening in schools around the world. The United States (U.S.), Canada, Sweden, Holland, France, and other countries all face the challenge of addressing the needs of a growing diverse student population; in particular, supporting achievement and engagement across language and cultural boundaries, and taking into account different perspectives (Suarez-Orozco, 2005). Teachers need to develop knowledge and skills to succeed in teaching diverse children otherwise they do not ■■■■■■■■■■■■i continue to believe that 'all children can learn' Maria Luiza Dantas is (Banksetal.,2005,p.270). Over the past two decades, an assistant professor in teacher education programs have incorporated the Learning and multicultural education theories to build teacher Teaching Department of education students' (including prospective and in the School of Leadership service teachers) understanding of diversity (Cochran and Education Sciences Smith, 2003; Heath, 1983; Ladson-Billings, 1994; at the University of San Mclntyre, Rosebery & Gonzalez, 2001 ; Moll, 1994). Diego, San Diego, Yet, teacher education and professional develop California. ment programs' ability to foster transformed under
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it