Teacher perceptions of English learners and the instructional strategies they choose to support academic achievement
Bibliographic record
Abstract
The new Common Core State Standards (CCSS) are designed to be robust and relevant to the real world by reflecting the knowledge and skills that students should possess in order to be college and career ready (www.corestandards.org). The content, performance, and language demands of the new standards are more difficult than those of the previous standards. This change will have an impact on all students, but especially on English learners. Students are required to engage with complex texts across all disciplines. This study was conducted to analyze two components of quality instruction that contribute to the academic success of English learners in light of the expectations the new standards have on student performance: (a) to determine teacher perceptions of English learners in a sociopolitical context, and (b) to determine the instructional strategies teachers use to support the challenging expectations of student learning. This study focused on elementary teachers in five public school districts across Southern California: Yucaipa-Calimesa Joint Unified School District, Fontana Unified School District, Ontario-Montclair School District, Riverside Unified School District, and Banning Unified School District. A quantitative method was used to collect survey data from 442 participants in order to analyze the perceptions, knowledge, and skills teachers have that may contribute to, or deter from, the academic achievement of English learners. Study findings revealed the challenges in using a quantitative method as the sole form of research when attempting to determine the knowledge and skills of teachers that may be preventing them from performing their jobs well and improving the academic achievement of English learners. Based on the findings, research-based solutions are presented to address the challenges.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".