Towards Local Community Involvement in Students’ Science Learning: Perspectives of Students and Teachers
Bibliographic record
Abstract
The European Commission calls for schools to move towards becoming open to their communities, integrating external social, civil, and expert stakeholders into authentic learning experiences’ development alongside teachers and students, particularly in terms of science education. However, little research or practical implementation has been reported on how community actors could participate in the development of such curricular learning activities. In this study, we present an implementation of the open science schooling (OSS) approach to science learning, where community involvement in the development of science missions takes a vital role. During the study, students developed science missions related to local societal issues that interested them in collaboration with their teachers and community experts, with frequent hands-on investigations outside their classrooms or laboratories, in five European countries and Israel. Questionnaires with quantitative and qualitative questions concerning students’ and teachers’ views and perspectives about implementing science education using OSS were administered after the participants finished their science missions. The results indicate the effectiveness of the OSS approach to science learning involving the community from both students’ and teachers’ perspectives. This study is a step towards supporting schools in becoming active agents of change through the implementation of contextualized learning experiences alongside external stakeholders.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".