Penerapan Model Pembelajaran Kooperatif Tipe Think-Pair-Share (TPS) Untuk Meningkatkan Hasil Belajar Matematika Siswa
Bibliographic record
Abstract
This research aims to determine whether the application of the Think-Pair-Share (TPS) cooperative learning model can enhance students' mathematics learning outcomes. The Classroom Action Research (PTK) was conducted in class VII D of SMP Negeri 6 Kintamani. The subjects comprised all students in class VII D of SMP Negeri 6 Kintamani during the first semester of the 2022/2023 academic year, totaling 32 individuals, with 17 males and 15 females. The research focused on students' mathematics learning outcomes. This study on learning enhancement occurred over two cycles: cycle I covered integers, and cycle II involved fractions. Mathematics learning outcomes were assessed based on the scores obtained by students in learning outcome tests. Data collection utilized observation and test methods. Quantitative descriptive analysis and qualitative descriptive analysis were employed for data analysis. Student mathematics learning outcomes data were processed using averages and classical completeness. The success criterion for this classroom action research was defined as average student mathematics learning results falling within the 'good' category (77-86) with a minimum classical completeness of 85%. Analysis of the research revealed an increase in average student mathematics learning outcomes, rising from 71.76 (categorized as fairly good) in cycle I to 80.00 (categorized as good) in cycle II, marking an increase of 8.24. Additionally, there was an increase in learning completeness from 61.76% in cycle I to 88.24% in cycle II, marking a rise of 26.48%. The conclusion drawn from this research is that the application of the Think-Pair-Share (TPS) cooperative learning model positively impacts students' mathematics learning outcomes.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".