AC 2011-592: ENHANCING THE INTEREST, PARTICIPATION, AND RE- TENTION OF UNDERREPRESENTED STUDENTS IN ENGINEERING THROUGH A SUMMER ENGINEERING INSTITUTE
Bibliographic record
Abstract
The summer engineering institute (SEI) in San Francisco State University is a two-week residential engineering program designed to attract, recruit and retain high school seniors and community college students to enter engineering programs. In 2008 Canada College, a Hispanic-Serving community college in Redwood City, collaborated with San Francisco State University, a comprehensive urban university, to design and implement the summer engineering institute which is funded by the US Department of Education’s Minority Science and Engineering Improvement Program (MSEIP) grant to increase the likelihood of success among underrepresented and educationally disadvantaged students interested in pursuing careers in STEM fields. Prior to its partnership with Canada College, SFSU has many years of experience in offering an engineering residential program funded by the California Department of Transportation (Caltrans). With the newly funded grant from the DOE, the Summer Engineering Institute has been designed and taught by SFSU engineering faculty from Civil, Electrical, Mechanical and Computer engineering programs. The redesigned summer program involves projects that were specifically designed to motivate students’ interest in hands-on research. The program also offers students the opportunity to gain insight into various engineering career options, and academic programs through a combination of lectures, field trips, and workshops. Preliminary results indicate SEI participants showed greater understanding of the engineering profession and increased interest in STEM fields. This paper aims to show how a summer engineering program can be designed to enhance interest in engineering among minority students, and how faculty can be actively involved in designing a program that has the potential to strengthen the engineering education pipeline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".