Helping Preservice Content-Area Teachers Relate to English Language Learners: An Investigation of Attitudes and Beliefs
Bibliographic record
Abstract
In the United States and Canada, as in many other countries, it has become common for teachers not specifically trained in English as a second language (ESL) to have immigrant and minority language students in their classrooms. These students, who are generally learning English along with the culture of their new countries, present many challenges for their teachers, who are often not appropriately trained to meet their needs. Often teachers of mathematics, science, and other content-area courses feel less than prepared for these students and lack the skills needed to accommodate instruction to their unique needs. In addition, these same teachers often harbor attitudes and beliefs about immigrant students that are not conducive to the development of a safe learning environment and are difficult to alter. This article describes how a community-based service-learning project (CBSL) was used to begin to investigate the attitudes and beliefs of preservice content-area teachers toward English language learners (ELLs). In this study many participants exhibited some level of change in their attitudes about working with ELLs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".