Analysis of a Moodle-based training program about the Pedagogical Content Knowledge of Evolution Theory and Natural Selection
Bibliographic record
Abstract
In this study we aim to find out whether a training program for secondary school science teachers which wasorganized based on the model of Pedagogical Content Knowledge (PCK), could improve their individual PCK for aspecific scientific issue. The Evolution Theory (ET) and the Natural Selection (NS) were chosen as the scientificissues of interest. Both of them are fundamental in biology teaching, especially the ET which can be taught as aunifying theory of biology. The individual PCK of teachers can be improved by strengthening its components:knowledge, pedagogy and managing the context. The principals and content of the seminar were decided based onthe results of another study among Greek teachers for the characteristics of their PCK about ET, NS and Nature OfScience (NoS). The seminar involved 16 secondary school teachers. We found that all trainees improved theirindividual PCK and felt adequate to teach more effectively the ET and the NS to their students. All participantsthrough the activities they performed, moved to a more constructive and learner-centered teaching style compare towhat they used to do before the training program.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".