Supporting Socially Responsible Science Education through a three visions of scientific literacy framed learning study
Bibliographic record
Abstract
Purpose This study explores how secondary science teachers in British Columbia engaged with the Three Visions of Scientific Literacy Heuristic (3-VSL) within a Learning Study (LearnS) to support students’ responsible engagement with controversial societal issues related to science, termed Socially Responsible Science Education (SRSE). The study examined how LearnS promoted teachers’ integration of 3-VSL, addressed SRSE implementation challenges, and developed theoretical fluency in their practice. Design/methodology/approach A case study approach involved three science teachers and a school administrator in a 30-week LearnS. A novel concurrent-and-successive (C&S) LearnS model allowed iterative cycles of lesson planning, enactment, and reflection. Data included semi-structured interviews, classroom observations, artifacts, and meeting recordings. Thematic analysis identified key themes in teachers’ experiences employing 3-VSL. Findings The teachers initially resisted critical orientations of 3-VSL but, through reflection and research lessons, enriched their understandings and enactments of SRSE. They generated personal interpretations of 3-VSL to guide SRSE practices. The 3-VSL framework helped refine literacy-focused objects of learning and guide pedagogy. Practical implications Findings highlight LearnS as an effective professional development model for promoting SRSE. The study demonstrates how 3-VSL scaffolds teacher learning, supports curricular enactment, and addresses context-specific challenges in implementing critically oriented science teaching. Originality/value This study contributes a novel LearnS case from Canada. It expands LearnS beyond variation theory, engages with a complex teaching challenge (SRSE), and introduces a novel LearnS model (C&S). Findings suggest that varying critical components of LearnS—such as objects of learning, theories, models, and contexts—can strengthen its capacity to support diverse teacher aims across educational milieus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".