The Effect of Cooperative Learning Model of Teams Games Tournament (TGT) and Students’ Motivation toward Physics Learning Outcome
Bibliographic record
Abstract
This research aims at describing the effect of cooperative learning model of Teams Games Tournament (TGT) and motivation toward physics learning outcome. This research was a quasi-experimental research with a factorial design conducted at SMAN 2 Makassar. Independent variables were learning models. They were cooperative learning model of TGT and conventional learning model. Motivation as moderator variable was divided into two factors, namely high and low learning motivation. The dependent variable was physics learning outcome. The instrument was a questionnaire of motivation to learn physics used to measure the motivation of students. Whereas physics achievement test was used to measure the physics learning outcome of the students. There were 36 students of class X in SMAN 2 Makassar taken using random sampling techniques as the research samples. The data were analyzed using two ways of ANOVA. There were some findings of this research. First, the physics learning outcome of the students taught using cooperative learning model TGT was higher than students taught using conventional learning models. Second, the physics learning outcome of students who had high motivation to learn physic and were taught using cooperative learning with TGT was higher than students taught using conventional learning model. Third, the physics learning outcome of students who had low motivation to learn physic and were taught using cooperative learning with TGT was not significantly lower than students taught by conventional learning model. However, the learning outcome between those two learning models were significantly different with α = 0, 05. Fourth, there was a significant interaction effect between learning model and motivation toward physics learning outcome of the students.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".