The Effects of Dual-Credit Enrollment on Underrepresented Students: The Utah Case
Bibliographic record
Abstract
This study considers the effects of Utah’s Dual-Credit Enrollment (DCE) and Early College High School (ECHS) programs on underrepresented students’ performance via an examination of the Utah Data Alliance longitudinal public education dataset. The study assesses standardized testing scores, high school graduation rates, dual course credits earned, higher education enrollment, time-to-completion, and degree attainment outcomes for various minority and low income student groups enrolled in DCE and ECHS programs. To limit the endogeneity and self-selection bias present in non-experimental data, the study employs Propensity Score Matching method (PMS) as a quasi-experimental design methodology. Although PMS offers many advantages, its strength as an estimator is dependent on the existence of complete and quality matching variables. To assure accurate model specifications given the available data, Receiving Operator Characteristic (ROC) Analysis is applied to variations on the PMS models. Estimated outcomes reflect positive effects for each of the examined student populations differentiated by gender, race, income and English Language Learner status. The economic effects of accumulating higher education course credits and decreases in higher education time-to-completion may yield the most interesting outcomes, enjoy the strongest causal claims, and result in measurable household and state level savings. These outcomes may also reveal potential weakness in the structure of higher education course and major programming, and the difficulty presented as high school students make higher education decisions.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".