Service Learning Oriented Pre Engineering Programs And Their Impact On Non Traditional Engineering Students
Bibliographic record
Abstract
This paper describes and analyzes a new program implemented by Engineers Without Borders-USA (EWB), JETS, Westlake High School (Atlanta, GA), and the Georgia Institute of Technology that introduces pre-college students to the field of engineering through the use of EWB-focused service learning engineering activities.This initiative differs from other high school engineering programs that emphasize competitions in that it highlights important engineering design concepts by rooting the students' motivation in the desire to help those in need.This emphasis on engineering-themed service projects allows for real-world reinforcement of sustainable engineering practices and promotes the education of ethically responsible and internationally aware students.We postulate that this move away from competition-based motivations and towards community service will be particularly appealing to non-traditional engineering students such as minorities and women.This paper will examine the case study of EWB-Westlake High School, the first ever high school EWB chapter, which was chartered in the Fall of 2006, and conducted a work trip to Tanzania in July, 2007.The program assessment surveys address which specific activities were effective and which need future refinement, and explore the impact that an engineering service learning program can have on the future goals of the students involved.In addition, two new initiatives will be highlighted; a new national initiative that promotes engineering-focused service learning in high schools, and a local initiative focused on bringing service learning themes into preengineering curricula throughout the state of Georgia.This paper will include an alignment of program activities with state and national education standards, and should provide other high schools with the tools to initiate their own programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".