Retaining Black Students in Engineering: Do Minority Programs Have a Longitudinal Impact?
Bibliographic record
Abstract
In an effort to assist minority populations who are at risk of attrition in science, mathematics, and engineering programs, university administrators have launched and evaluated minority support programs. One such program implementation and evaluation was completed and reported, which noted trends in academic outcomes of program participants, such as grade point averages and standardized mathematics and science reasoning test scores, with participants' outcomes observably exceeding those of a similar sample of nonprogram participants (Good, Halpin, & Halpin, 1999). As is true with many program evaluations, however, this data only revealed information concerning achievement of the students in the freshman year and did not follow the students' success into subsequent years after program completion. Therefore, the purpose of this study was to examine if an effect on academic achievement occurred throughout the participants' sophomore years of study and if participants in the program were more likely to remain within the College of Engineering as a result of program involvement. The data source for this study was 58 African-American students enrolled in a pre-engineering program at a large land-grant university (34 volunteer program participants and a comparison group of 24). Quarter grade point averages and retention status were collected for both groups throughout their sophomore years. In addition, 12 of these students (six per group) were interviewed concerning their freshman year pre-engineering experiences. Results of this study indicate that, although benefits to academic achievement due to academic support encountered during the freshman year may possibly diminish over time, the effects of engaging in such programs on actual retention remain of significant interest to program administrators and researchers.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".