A Longitudinal Study of the Effectiveness of a K-12 Engagement Program on Graduate Student Learning Outcomes
Bibliographic record
Abstract
Programs that connect higher and K-12 education provide benefits to K-12 students, teachers, and higher education. The National Science Foundation (NSF) invested in programs connecting domestic STEM graduate students with K-12 education for over a decade (GK-12), intending that such engagement would help achieve graduate student learning outcomes and would be sustained after NSF funding. By comparing two cohorts of graduate student participants in a sustained GK-12 we have begun longitudinal assessment of a program as it matures and diversifies by integrating non-STEM and international students. Qualitative analysis of participant journals shows that the sustained GK-12 has continuing impacts on graduate students’ teaching, teamwork, and communication skills, and aids in shaping their future career plans. A new theme in Cohort 2 related to changing perspectives on pedagogy and teaching in faculty responsibilities. We encourage universities seeking to meet expanded graduate student learning outcomes to consider adopting/adapting the GK-12 model.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".