East Tennessee Noyce STEM Teacher Preparation Program
Bibliographic record
Abstract
Abstract There is a critical shortage of STEM teachers in high-need fields, especially in Computer Science, Engineering/Engineering Technology, in the southern Appalachian region. This paper describes an NSF funded Noyce Track 1 Teacher Recruiting program at East Tennessee State University (ETSU) awarded in 2019. The program is administered in partnership with local high-need school districts in the First Congressional District of Tennessee as well as four nearby nonprofit educational organizations, namely the Gray Fossil Site/ETSU Natural History Museum, the Hands- On Museum, the Pisgah Astronomical Research Institute, and the Bays Mountain Planetarium, and three summer science camps: the ETSU Governor's School, the ETSU Renaissance Camp, and the ETSU Computing/Technology Camp. The program has three parts: 1) a summer teaching internship program for undergraduate STEM majors designed to recruit students into the teaching profession, 2) scholarships and mentoring for a Masters of Arts in Teaching (MAT) program, and 3) a mentoring program and continuing professional development for newly minted teachers to retain them as teachers. It is focused on Physics, Chemistry, Engineering/Engineering Technology, Computer Science, and Mathematics. The heart of the program is a 4+1 bachelors/post-baccalaureate program in which students obtaining undergraduate degrees in high-need STEM fields are recruited for the MAT program. Thus far two cohorts of 8 students completed the internship programs and all showed interest to become STEM teacher in their respective fields. Of them three students completed the MAT program and currently teaching in high-need schools. We anticipate that ETSU Noyce program will create a group of teachers who will be able to inspire future generations of STEM professionals in Northeast Tennessee region.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".