The Effect of Biotechology Module with Problem Based Learning in the Socioscientific Context to Enhance Students’ Socioscientific Decision Making Skills
Bibliographic record
Abstract
This aim of the research was to train socioscientific decision making skills for the science students. Students are involved to solve the socioscientific problems by making informative and systematic decisions. The development of socioscientific decision making skills was done by applying biotechnological module based on Problem-based Learning with socioscientific cases. Quasi-experimental design was used in this research. Two science classes were employed in this research: first class for experimental class which had the treatment by applying biotechnology module based on problem-based learning and another class for control class using biology books from school. Those two classes were given the same questions in pre-test and post-test to measure socioscientific decision making skills. The research results showed that the average post-test score of socioscientific decision making skills in experimental class was 82.80 which is higher than the control class (62.32); moreover, normalized gain score in experimental class obtaining 0.745 and this is also higher than that in control class (0.434). The results of ANCOVA analysis show, that there was significant differences in the score of socioscientific decision making skills between experimental class and the control one by the value of F count (25.54), this value was higher than F table (4.075). In addition, the score of partial eta squared was 0,384, which means that the application of PBL-based module with socioscientific cases have the high level of effectiveness to improve socioscientific decision making skills. The result of assessment transcript in the decision making quality showed that experimental class has the decision supported by the justified arguments which is contains 2-4 socioscientific aspects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".