Application of Jigsaw Type Cooperative Learning Model to Improve Conceptual Knowledge and Problem Solving Ability of Senior High School Students
Bibliographic record
Abstract
This study proves that the Jigsaw type cooperative learning model can improve the conceptual knowledge of students on thermochemistry material, which is a learning model that has an influence on improving students' conceptual knowledge of thermochemistry. It aims to improve students' conceptual knowledge and problem solving ability through the application of the Jigsaw type cooperative learning model, taking into account the students' basic mathematics ability level which is classified into three categories: low, medium, and high. This research used quasi-experiment method with pretest-posttest control group factorial design. The subjects of the study were students of class XI IPA at SMA Negeri 06 Bombana, Bombana Region. Data were obtained through conceptual knowledge test, problem solving ability test, and student response questionnaire. The results showed that the conceptual knowledge of students who followed the Jigsaw type cooperative learning model was quantitatively higher than that of students who followed the conventional learning model. The results showed that the conceptual knowledge of students who followed Jigsaw cooperative learning was quantitatively higher than that of students who followed conventional learning. The same applies to problem solving ability, where students with the Jigsaw model showed quantitatively higher results compared to conventional learning students. the increase in conceptual knowledge was greater in the Jigsaw group compared to conventional learning. The level of students' basic mathematics ability did not affect the increase in conceptual knowledge, but did affect the increase in problem solving ability. Students gave positive responses to the Jigsaw type cooperative learning model, stating that this model was fun, easy to follow, and increased learning motivation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".