Synergistic Collaborations among K-12 Technology, STEM Coaches, and Tech-Industry Partners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This project focused on how two technology coaches, a K-12 Technology Coach and a Science Technology Engineering Mathematics (STEM) Coach collaborated with their coach colleagues and tech-industry partners to offer teachers resources and embedded professional learning (PL). As part of a multiple-case study of coaching models of PL, over the course of two academic years, the researchers gathered observational data during classroom coaching sessions, small group professional learning sessions, and professional development workshops hosted by a tech-industry partner. Additionally, the coaches and a subset of middle school teachers participated in one-on-one interviews and the coaches had discussions in a focus group. Data analyses distilled two main themes: (1) coaches appeal to and collaborate with tech-industry partners; and (2) coaches solicit support and collaborate with school district administrators. Conclusions suggest that technology and STEM coaches serve an integral role in the implementation of technology across the district when collaborating with tech-industry partners. Recommendations include the need for technology coaches to be resourceful and initiate and foster tech-industry partnerships as well as dedicate time to collaborate with other coaches to enhance their own professional knowledge and skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it