Increasing Student Activeness Using The Cooperative Model TGT IPAS Class IV SDN Tambak Wedi 508
Bibliographic record
Abstract
This research is motivated by observational data that shows students' learning activity in IPAS material is very low. To respond to that, the researcher conducted a classroom action research aimed at improving students' learning activity. The objectives to be achieved in this research include: (1) To determine the improvement of students' engagement in learning through the use of lecture method, (2) To determine the improvement of students' engagement in the learning process through the use of cooperative learning model, specifically Teams Games Tournament (TGT). This research is a classroom action research. This research was conducted in class IVB with a total of 32 students, consisting of 17 male students and 15 female students. The research was conducted in 2 cycles, and the results showed an improvement in effectiveness in the second cycle. The change occurred because in the second cycle, students were more engaged in working together with their group, so there was less opportunity to discuss with other groups. This research measures several student learning skills, namely: questioning skills, answering skills, discussing skills, and group learning skills. The research findings show improvement in each domain in each cycle.The selection of this learning model is highly suitable and can enhance students' engagement in learning because in this learning model, students are not just passive listeners during the lesson, but they actively participate, think, learn, and compete among groups. This makes students challenged to win the competition and become active in learning.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".