The relationship between non-cognitive skills and the academic achievement of African American males in community colleges
Bibliographic record
Abstract
The purpose of this quantitative correlational study was to examine the relationship between non-cognitive skills and academic achievement in the form of course completion rate and cumulative GPA of African American male community college students.Eight noncognitive variables were measured compared to the course completion rate and cumulative GPA of the study subjects.Study participants were 102 African American males attending Midwest community colleges in urban settings with 10,000 or more student enrollment.Sedlacek's (2004) Non-cognitive Assessment method identified eight non-cognitive variables (NCV) and served as the conceptual framework for the investigation.Participants completed the Noncognitive Questionnaire (NCQ), an instrument created by Sedlacek (2004) to measure the eight non-cognitive variables.Questionnaire data were matched to individual student course completion rates and cumulative GPA records.Pearson product-moment correlational analyses were performed on the data to determine which of the eight non-cognitive variables were related to the participants' course completion rate and cumulative GPA.The results showed that course pass rate was significantly correlated with non-cognitive variable #6 (successful leadership experience, r = .230,p < .05),and with non-cognitive variable #4 (preference for long term goals, r = .203,p < .05).None of the non-cognitive variables contributed to the prediction of cumulative GPA.Additionally, a stepwise linear regression analysis was calculated to determine noncognitive variables most predictive of course completion rate and cumulative GPA.No additional results were found when the non-cognitive variables were entered into a linear, stepwise multiple regression equation.Variable #6 (successful leadership experience) was the only non-cognitive variable contributing to the prediction of Pass Rates.
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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 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".