Enhancing Pre-service Science Teachers’ Practice according to SocioScientific Issue (SSI)-Based Teaching through Collaborative Action Research
Bibliographic record
Abstract
The objective of this research was to enhance two case studies of pre-service science teachers’ practice according to SSI-based teaching through collaborative action research. The case study participants had taken a field experience course in the universities in Bangkok in the academic year 2014. The researcher gathered data from classroom observation, students’ journal entries, and student artifacts. In addition, they were asked to write journal entries about their practices. Moreover, informal interviews were used for clarification. These collected data were analyzed using within-case and cross-case analyses. The findings showed that both case studies developed grade 10 students’ argumentation skills through SSI-based teaching in natural resource unit with 4 stages of teaching: issue stage; exploration stage; argument stage; and decision making stage for promoting students’ argumentation. Based on the collaborative action research, the participants changed their teaching to engage students with SSI; increasing facilitating of students’ group working in order to get more essential information; using role play to promote the effective students’ argumentation; and providing enough time for reviewing data to better support decision making. Keywords: Pre-service science teachers, Socioscientific issue-based teaching, Collaborative action research
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".