Inquiry, Engagement, and Literacy in Science: A Retrospective, Cross‐National Analysis Using PISA 2006
Bibliographic record
Abstract
ABSTRACT In this study, we examine patterns of students’ literacy and engagement in science associated with different levels of “inquiry‐oriented” learning reported by students in Australia, Canada, and New Zealand. To achieve this, we analyzed data from the Organisation for Economic Co‐operation and Development's 2006 Programme for International Student Assessment, which had science as its focus. Consistently, our findings show that science students who report experiencing low levels of inquiry‐oriented learning activities are found to have above‐average levels of science literacy, but below‐average levels of interest in science, and below‐average levels on six variables that reflect students’ engagement in science. Our findings show that the corollary is also true. Across the three countries, students who report high levels of inquiry‐oriented learning activities in science are observed to have below‐average levels of science literacy, but above‐average levels of interest in learning science, and above‐average engagement in science. These findings appear to run counter to science education orthodoxy that the more students experience inquiry‑oriented teaching and learning, the more likely they are to have stronger science literacy, as well as more positive affect toward science. We discuss the implications of these findings for science educators and researchers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".