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 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 | 0.001 | 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) | 0.000 | 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".