Pengaruh Model Pembelajaran Problem Based Learning terhadap Kemampuan Berpikir Kritis Siswa SMP Negeri Halioan
Bibliographic record
Abstract
Learning is the process of forming behavior or behaviorism and is seen from the cognitive aspect of students, in fact to support the achievement of ideal educational goals with the existing education system in Indonesia. In this research, the author is interested in researching students' cognitive aspects in line with 21st century learning to determine students' critical thinking abilities using the problem based learning model. This type of research is research using a quantitative research approach. The sample in this study was control class VII A students with a total of 13 students and VII B was an experiment with a total of 12 students at Halioam State Middle School, totaling 25 students. Data collection techniques use test questions and documentation, interviews. Data analysis used the independent sample t-test hypothesis test and the N-Gain score test. The results of the research show that the problem based learning model can have an influence on the critical thinking abilities of class VII students at Halioan State Middle School. This is proven by assessing students' critical thinking abilities using a hypothesis test using an independent sample t-test. The result was a 2-tailed sig of 0.000 < 0.05 so that there is an influence of the problem based learning model on Halioan State Middle School students' critical thinking abilitie.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".