Towards Transformative Science Education for Responsible Citizenship:Investigating Science Teachers’ Integration of Informed Decision Making
Bibliographic record
Abstract
Learning to make informed decisions on socio-scientific issues (SSI) is considered a crucial step towards taking action in complex real life situations, and is therefore pivotal in modern transformative science education. This paper explores teachers’ integration of informed decision making in science subjects while designing subject-specific citizenship lessons on current SSI. Understanding teachers’ capacities with respect to this integration is important to establish effective teacher education and continuing professional development. The study took place in the context of a series of workshops on the goals and instructional approaches for informed decision making in science-specific citizenship education. Our in-depth multiple-case study involves three teachers in different science subjects. The data was collected through four teacher interviews. A qualitative content analysis was performed in two coding cycles using the framework of pedagogical design capacity. We found distinguishing features and common patterns in the the teachers’ use of personal and external resources for the design of up-to-date integrated citizenship lessons on informed decision making. Our study concludes that science teachers are able to design citizenship lessons when providing them with relevant instructional resources. These include professional development workshops with instructional approaches, example curriculum materials, and other tools.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".