International collaborative follow-up investigation of graduating high school students’ understandings of the nature of scientific inquiry: is progress Being made?
Bibliographic record
Abstract
Understandings of the nature of scientific inquiry (NOSI), as opposed to engaging students in inquiry learning experiences, are included in science education reform documents around the world. However, little is known about what students have learned about NOSI during their pre-college school years. The purpose of this large-scale follow-up international project (i.e. 32 countries and regions, spanning six continents and including 3917 students for the high school sample) was to collect data on what exiting high school students have learned about NOSI. Additionally, the study investigated changes in 12th grade students’ NOSI understandings compared to seventh grade (i.e. 20 countries and regions) students’ understandings from a prior investigation [Lederman et al. (2019). An international collaborative investigation of beginning seventh grade students’ understandings of scientific inquiry: Establishing a baseline. Journal of Research in Science Teaching, 56(4), 486–515. https://doi.org/10.1002/tea.21512]. This study documents and discusses graduating high school students’ understandings and compares their understandings to seventh grade students’ understandings of the same aspects of scientific inquiry for each country. It is important to note that collecting data from each of the 130+ countries globally was not feasible. Similarly, it was not possible to collect data from every region of each country. A concerted effort was made, however, to provide a relatively representative picture of each country and the world.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".