The Creation and Evaluation of an Engineering Student Leadership Academy
Bibliographic record
Abstract
This article describes the creation and evaluation designed to support student success through their inclusion in a variety of professional and technical training programs. The opportunity to develop a leadership academy specifically for engineering students allowed faculty program coordinators to deliver content that was normally not available to students at this Hispanic-Serving Institution (HSI). We implemented and evaluated the leadership academy using a mixed-methods approach involving multimedia interviews and two traditional survey data collection techniques. Traditional online surveys gave quick feedback to coordinators on what the students found valuable, with the biggest change in programming being the spreading out of the Engineering Student Leadership Academy (ESLA) from two days to four half-days over the course of a month. Assessing the change in students’ perception of leadership skills proved harder to measure in the short term. Using videoed interviews shortly after the delivery of the program had additional quality improvements, including the opportunity to showcase Hispanics, particularly Latinas, in leadership roles in the field of engineering. Reflections on improving the assessment and evaluation components are included in the article.
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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 | 0.069 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".