Fostering the Use of Talking Stick Learning Model on the Critical Thinking Ability in Science Learning
Bibliographic record
Abstract
This study aimed to investigate the influence of the Talking Stick learning model on the critical thinking skills of fourth-grade students in science (IPA) subjects. Conducted as a quantitative research using a pre-experimental One-Group Pretest-Posttest Design, the study involved 30 students from class IV at UPTD SD Negeri 122345 Pematangsiantar. Data were collected through critical thinking tests administered before and after the implementation of the Talking Stick model. The test instrument consisted of eight essay questions validated through Aiken’s V formula, showing a high level of content, construct, and language validity. The results showed a significant increase in students’ critical thinking skills after the intervention. The average pretest score was 44.86, whereas the posttest average rose to 88.83. The normalized gain (N-Gain) score reached 0.79, falling into the high category, indicating effective improvement in the learning process. The Talking Stick model, by encouraging turn-taking and structured student interaction, effectively enhances analytical thinking, reasoning, and active participation in science learning. These findings support the integration of active learning models into elementary education to develop critical thinking skills. The model's simplicity and effectiveness suggest that it is particularly suitable for primary-level classrooms. Therefore, educators and curriculum developers should adopt Talking Stick and similar strategies to promote 21st-century competencies. Further research is recommended to explore its application in diverse learning contexts and subject areas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".