Collaborative Group Instruction's Impact on English Learner Success \nin Algebra I at the High School Level
Bibliographic record
Abstract
The purpose of this study was to help determine effective teaching and classroom practices when addressing the mathematics achievement gap between Latino English Learners (ELs) and their White and Asian counterparts. This study sought to discover what effect different teaching strategies had on the growth in proficiency of Latino ELs on Algebra I Benchmark assessments from first quarter to fourth quarter of an academic school year. Particular areas of interest were whether explicitly structured collaborative grouping had any effect on Latino ELs??? growth in Algebra I achievement. Another area of interest was what effect more traditional teacher-directed instruction and lack of structured collaborative grouping had on Latino ELs Algebra I proficiency growth. This study compared the test scores, grades, and survey results of 14 Latino ELs in two Algebra I classes where teachers used different approaches to helping students master content at a Title I school in Orange County, Calif. A mixed-methods approach was used in analyzing both quantitative test scores, grades, and Likert-type surveys as well as qualitative interview answers. Findings suggest that there was little to no difference in test score gains between the teacher who used daily structured collaborative grouping and increased direct instruction (DI) and the teacher who used less frequent group work and half as much DI. The only significant variance found was in final semester grades, with nearly all EL students in the class with more group work and DI earning a passing grade. This study recommends that collaborative group work and DI should be carefully considered, implemented, and adjusted, depending on the needs of the students. Additionally, culturally relevant approaches to teaching should be implemented in every school with high numbers of Latino ELs.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".