Investigation of Remedial Education Course Scores as a Predictor of Introduction-Level Course Performances
Bibliographic record
Abstract
This study explores whether performance in remedial English and remedial math is a predictor of success in a college-level introduction English or college-level math class; and whether demographic variables increase the likelihood of remedial English and remedial math as a predictor of success in a college-level introduction English or college-level introduction math course. Participants included two cohorts of students from a degree-granting, for-profit institution taking either face-to-face or online courses. There were 1,091 students for math and 1,297 students for English in the fall quarter of 2007 cohort and 1,098 students for math and 1,372 students for English in the fall quarter 2008 cohort. Analysis showed that remedial class performances for both math and English were weakly associated with college-level class performance. Additionally, results showed that family income and race/ethnicity appear to be significant predictors of performance in their corresponding college-level course.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".