The Effects of Single-sex Mathematics Classrooms on African-American Males in the Ninth Grade
Bibliographic record
Abstract
High dropout rates and poor academic performance are frequent characteristics of the educational experience for African-American male youths (Bailey & Paisley, 2004). With increased accountability standards to raise the academic achievement levels, single-sex educational environments have taken a central role in school reform agendas and initiatives in many school districts (Herr & Arms, 2004). The incorporation of a single-sex grouping served as a conduit for teachers to deliver instruction based on the varied learning styles of the students.This study was designed to determine the effect single-sex classroom instruction has on African American males’ achievement in ninth-grade mathematics compared to that of the other student groups. The researcher investigated a public school in a North Texas school district that offered alternative educational options for the students. Single-sex classrooms were created in mathematics. Teachers taught single-sex classes of students from varied ethnic backgrounds during at least one double-blocked period each day. Ex post facto data were analyzed. An independent samples t-test and a two-way factorial Analysis of Variance (ANOVA) were utilized to test each hypothesis to determine whether significant differences existed in the scale scores of comparison groups on state-mandated assessments over a period of 3 years. The instrument used to measure academic achievement is the Texas Assessment of Knowledge and Skills.This study found that the mathematics scores of African American males in single-sex classes did not significantly differ from the scores of African American males in coeducational classes. The scores for the males in single-sex classes were slightly higher than that of males in coeducational classes at each grade level throughout the 3-year period. The mathematics scores of African American females in single-sex learning environments were not significantly different than those of African American males in single-sex classes. The scores for the female students were consistently higher throughout the 3-year period. This study identified the need for single-sex instructional opportunities for students as a means of eliminating the achievement gap.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".