Supporting Academic Growth of English Language Learners: Integrating Reading into STEM Curriculum
Bibliographic record
Abstract
English Language Learners (ELLs) in the U.S. have recently received growing attention in educational researchbecause of their struggle in academic performance, especially after the launch of the Common Core State Standards(CCSS) and assessments in 2009. Unfortunately, ELL students are required to take these standardized tests inEnglish language regardless of their proficiency level in reading. Despite increased focus and resources ofimplementing STEM (Science, Technology, Engineering, and Math) curriculum in K-12 education, there is a strongevidence that ELL students do not attain commensurate performance when compared to their nativeEnglish-speaking peers. The integration of Art into STEM disciplines has evolved STEM into STEAM. Lately, therehas been much discussion in the educational field that the acronym STEAM should be further evolved into STREAMby integrating Reading. The purpose of this study is to investigate the efficacy of integrating STEM and Readingcurriculum in K-12 education to reduce the achievement gap between ELL and non-ELL students. Practicalclassroom strategies for classroom teaching and instruction are discussed.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".