PATHWAYS – A Case of Large-Scale Implementation of Evidence-Based Practice in Scientific Inquiry-Based Science Education
Bibliographic record
Abstract
The fundamental pioneering ideas about student-centered, inquiry-based learning initiatives are differing in Europe and the US. The latter had initiated various top-down schemes that have led to well-defined standards, while in Europe, with its some 50 independent educational systems, a wide variety of approaches has been evolved. In this present paper, we portray a European bottom-up initiative, “PATHWAY to Inquiry Based Science Education”, to define a basis for learning initiatives and to meet current challenges to access learning, to share knowledge and establish competences in learning communities. Our approach was designed to act as bottom-up catalyst by mobilizing teacher communities to (further) foster inquiry. Of a sample of 10.053 science teachers from 15 European countries (incl. Russia), about 5060 provided empirical support for our teachers’ professional development initiative. The response pattern portrayed teachers’ preferences and pointed to potential needs in professional development (PD) efforts. On average, our sample reported an altogether 11 years’ period of teaching practice in general but just 2-3 years of experience in inquiry-teaching. In the view of that, consequences for professional development (PD) initiatives are discussed.
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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 | 0.057 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".