Supporting STEM Education: Needs Assessment of Southeastern Rural Teacher Librarians
Bibliographic record
Abstract
A current focus in schools in the United States is STEM education, which prepares students for successful employment and post-secondary studies that require unique and more-technically advanced skills through teaching and learning in the areas of science, technology, engineering, and mathematics (STEM). This approach is grounded in problem solving, discovery, and exploratory learning, which requires students to actively engage in a situation in order to find its solution. Students engage in STEM learning in many different ways, with technology and digital resources playing an important role. The prominence of technology in STEM education provides leadership opportunities for teacher librarians. Yet, teacher librarians must be prepared to lead in the integration of technology to support STEM education. This report presents identified needs of teacher librarians in regards to supporting STEM education and discusses implications for better preparing pre-service teacher librarians to lead in order to address the needs of a new generation of learners.
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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 | 0.001 |
| 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) | 0.005 | 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".