Enhancing Pre-service Science Teachers’ Understanding and Practices of SocioScientific Issues (SSIs)-Based Teaching via an Online Mentoring Program
Bibliographic record
Abstract
Science education reformists in Thailand promote the use of socioscientific issues (SSIs)-based teaching to enrich scientific literacy for global citizenship. To achieve this goal, Thai pre-service science teachers (PSTs) must know how to effectively integrate SSIs into their science teaching practices. The purpose of this study was to enhance PSTs’ understanding and practices of SSIs-based teaching via the online mentoring (OM) program. Three PSTs were selected as case studies, and data were collected from online observations, semi-structured interviews, online discussions, and online document reviews. The analytical methods included within-case and cross-case analysis. This study found that the OM program was effective in enhancing PSTs’ understanding and practices of SSIs-based teaching. As a result, their teaching practices evolved from conveying content knowledge to promoting higher-order cognitive practices. In addition, the PSTs demonstrated a deeper appreciation for OM programs as a means to enhance teaching practices. This research demonstrates how the implementation of OM programs has the potential to be powerful tool for professional development of science educators, which is essential for transforming science educational practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".