Bibliographic record
Abstract
The purpose of this study is to examine graduation and dropout rates for Hispanic or Latino K–12 students enrolled in fully online and blended public school settings in Arizona. The independent variables of school type (charter vs. non-charter) and delivery method (fully online vs. blended) were examined using multivariate and univariate methods on the dependent variable’s graduation and dropout rates for Hispanic or Latino students. The results of this research study found a statistically significant difference when using multivariate analysis to examine school type (charter vs. non-charter) and delivery method (fully online vs. blended) on graduation and dropout rates. This finding warranted further univariate examination which found a statistically significant difference when examining delivery method on dropout rates. A comparison of mean dropout rates shows that Hispanic or Latino students involved in K–12 online learning in Arizona are less likely to drop out of school if they are in a fully online learning environment versus a blended learning environment. Students, parents, teachers, administrators, instructional designers, and policy makers can all use this and related research to form a basis upon which sound decisions can be grounded. The end result will be increased success for Hispanic or Latino online K–12 students not only in Arizona schools, but in many other important areas of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".