Published research on pre-college students’ and teachers’ nanoscale science, engineering, and technology learning
Bibliographic record
Abstract
Abstract By the end of the first decade of the 21st century, it was clear that nanotechnology was emerging as one of the most promising and rapidly expanding fields of research and development worldwide. It would not be long before scientists, science educators, engineers, and policy makers began advocating for nanoscience, engineering, and technology (NSET) related concepts to be introduced in K-12 classrooms. Indeed, there has been a surge in the development of pre-college NSET-related education programs over the last decade, as well as millions in funding to support the creation of these programs. In an effort to characterize the state of research to date on pre-college students’ and teachers’ learning of NSET content knowledge and related practices, we have conducted a systematic review of the peer-reviewed, published research studies to answer the following questions: What NSET content knowledge and practices in a pre-college context have been examined in empirical learning studies? What do these studies tell us about the NSET content knowledge and practices that pre-college students and teachers are learning? Implications and recommendations for future research are also discussed.
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.
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 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".